Trustpilot Stock

Good morning, everybody, and thank you for joining us today for Trustpilot's Half Year 2026 Results Call. I would now like to hand the call over to Adrian Blair, Chief Executive Officer, to open the presentation. Please go ahead.

Good morning, everyone. Thank you for joining us on the webcast today for Trustpilot's first half 2026 results. Hanno and I look forward to walking you through what was an excellent first half with strong growth and strategic progress. Before we get into the numbers, I want to put on record my sincere thanks to our outgoing CFO, Hanno Damm, for his extraordinary contribution to Trustpilot over the last 11 years. Hanno has been instrumental in taking a startup into the era of profitable growth we're now in. I'm sure I speak for everyone on this call and wishing Hanno well for the future.

Following on from that, I'm delighted to introduce our new CFO, Marcus Roy, who joined us yesterday following an extremely successful 5 years as CFO of The Economist Group. Welcome, Marcus.

Thanks, Adrian, and good morning, everyone. I'm delighted to be joining Trustpilot as CFO and look forward to meeting many of you in the coming days. I'm honored to play a part in the business' future success. I will now pass back to Adrian and Hanno to take you through today's results.

Thanks, Marcus, and welcome on board. Given Marcus's short tenure, Hanno, of course, will be supporting me on today's call. As usual, I'll run through operational highlights before handing over to Hanno for the financials. I'll then finish up with a deeper look at strategic progress. Three headlines for today's results. First, the business is delivering financially. We grew bookings 18% in constant currency, with particularly outstanding performance in North America, up 27%. This drove adjusted EBITDA to increase by 46% to $26.3 million and a margin of 17.4%.

Second, we're winning in enterprise. Annual recurring revenue from customers paying over $20,000 a year grew 35% with a host of new product features, increasing our appeal to large businesses across sectors. Third, AI is deepening our influence. Trustpilot is the #1 cited review platform globally according to research by Seer Interactive. We saw a more than 400% year-on-year increase in ChatGPT citations. Let's take a deeper look now at operational performance. As you know, a strong growth flywheel underpins our business.

In essence, Trustpilot products collect customer feedback and turn it into impact on the actions of people and businesses. Because the feedback has impact, people are motivated to write it and businesses want to engage with Trustpilot. Increasingly, that impact extends to AI as well as people. In H1, our users submitted 20% more reviews year-on-year, and our data set expanded to 394 million active reviews. Just after the period closed in July, we celebrated crossing the major milestone of 400 million reviews on the platform.

TrustBox impressions of our brands on other websites grew 15% year-on-year, and Trustpilot reviews received 29 billion Google impressions in the first half. This flywheel of more consumers writing and reading reviews, more businesses deeply engaging with the platform continues to reinforce itself around the world and underpins all the growth you'll hear about today. So bookings grew 18% on a constant currency basis with healthy growth across focus markets, including an outstanding performance in North America. Key verticals driving growth in the period included lending, insurance, health and well-being, and utilities.

Gross retention remained at the record 87% level we delivered in 2025. On the new sales side, enterprise momentum is excellent. We're increasingly seeing large multinational brands prioritize AI search optimization. We recently won Expedia as a customer, and spending time with businesses like that has made clear to me that they understand LLMs need relevant, authoritative, and recent review data to recommend their offerings. Customer logos paying more than $20,000 per year grew 29% year-on-year, and we also saw significant growth in customers signing contracts over $100,000 in the half.

As this enterprise base expands, it becomes much more relevant to view our growth through a global lens. With large multinational customers, we're seeing a natural shift in how some of these accounts manage their billing relationships, a dynamic I'll come back to on the next slide. Because of the inherent operating leverage in our model, this top-line growth translated into a 46% increase in adjusted EBITDA to $26.3 million, delivering a margin of 17.4%, up 2.8 percentage points year-on-year. Later, Hanno will take you through where we're seeing the operating leverage come through in more detail.

But let me now touch on each region individually. Starting with the U.K., where we delivered bookings of $64 million, up 11% on a constant currency basis. The net retention rate here remains above the group average, and with a 60% contribution margin, the U.K. continues to demonstrate the highly cash-generative profile the wider group can grow to at scale. Sales growth was particularly strong among customers paying more than $20,000 per year, up 27% year-on-year. Notable customer wins included Halfords, Space NK, and EE. Growth was particularly strong in the telecoms, automotive, and electronics verticals, which all grew well in excess of 20%.

Our business in the U.K. is incredibly diversified across the economy with revenue above $1 million in 41 separate industry verticals and the top 10 accounting for just 39% of total ARR. With overall market penetration still low at around 5%, the runway for growth remains significant in both the number of customers and the revenue per customer. A growing number of our largest enterprise customers are moving their billing relationships overseas as they expand internationally. Expedia is a good example, a global deal won by our U.K. team that billed out of the U.S. and therefore, captured in our North America bookings figure.

Overall, this had a 1 to 2 percentage point impact on reported U.K. growth. It's a reminder that particularly on the enterprise side, customer billing location is not a perfect proxy for underlying market penetration given how these multinational accounts actually operate. Turning now to Europe and the rest of the world. We delivered strong bookings growth of 19% at constant currency to $68 million with growth across the region in both number of customers and average contract value. DACH, that is Germany, Austria, and Switzerland, delivered a particularly strong performance, up 30% in constant currency year-on-year.

As a result of this sustained momentum, our DACH ARR has now surpassed $20 million, and Germany is our third biggest country after the U.K. and the U.S. We recorded our best ever H1 sales from customers paying more than $20,000 per year, and the share of bookings from that cohort in DACH was up 5 percentage points to 59%. We continue to win enterprise accounts across the region, including Bending Spoons and isybank in Italy. Moving to North America, which comprises the U.S. and Canada. We delivered outstanding bookings growth of 27% on a constant currency basis.

As I touched on earlier, this top-line figure is also supported by the expansion of our multinational base with billing relationships in North America. We saw excellent new business momentum and new business sales up 76% year-on-year. Both sides of the flywheel strengthened in the half with organic reviews up 50% year-on-year and the number of paying customers 11% higher year-on-year. We welcome notable enterprise clients in the half, including Bed Bath & Beyond, Intact Insurance, and Sunrun. I'll now hand you over to Hanno to take you through the financials in more detail.

Thank you, Adrian, and good morning, everyone. We delivered another strong half for bookings, EBITDA growth, margin, and continued cash generation. Let's go through the numbers in more detail. Bookings grew 18% in constant currency to $171 million in H1, continuing a clear and consistent track record of top-line growth. Our LTM net dollar retention rate was 101%. As you can see on the right, we continue to scale profitably. Adjusted EBITDA grew 46% to a record $26.3 million, a record margin of 17.4%, which is up 2.8 percentage points year-on-year at the top end of our guidance, and we delivered $16 million of adjusted free cash flow in the period.

Our LTM gross dollar retention rate remained strong at 87% through H1. Since 2023, we have placed significant focus on improving gross retention and delivered a 3 percentage point improvement over the past 3 years. Within that, it's worth highlighting the U.K.'s particularly strong gross retention rate of 88%. Net expansion of 14% brought net dollar retention to 101% from 103% in the LTM ending H1 '25 as we completed the annualization of previously flagged package migrations. Retention rates of 100% plus reflect the strength of our growth flywheel, product innovation, and our enterprise strategy.

This chart shows the compounding power of our subscription model with each new cohort of bookings stacking on top of a highly retentive expanding existing base. The mix shift towards our highest margin enterprise segment continues to accelerate. Customers paying us over $20,000 a year now represent 47% of total bookings, up from 51% back in H1 2023. The underlying unit economics of these accounts also remain excellent with gross dollar and net dollar retention for this segment well ahead of the group. Momentum in the U.S. has been particularly notable here.

The proportion of ARR coming from customers paying over $20,000 a year in the U.S. is now approaching similar levels to the U.K., which stands at 49% of ARR, albeit the U.S. is coming from a much lower base. That's a real marker of how quickly North America is scaling and how our value proposition there is strengthening. Let's look at the income statement from a management view down to adjusted EBITDA, excluding stock-based compensation, D&A, and nonrecurring items. As always, an IFRS statement and reconciliations are in the appendix.

We delivered revenue of $151.4 million and a gross margin of 83%, up 0.8 percentage points year-on-year, reflecting infrastructure and hosting optimizations as volume scales, partially offset by continued investment in our customer success teams following the sales reorganization. Sales and marketing was $41 million or 27% of revenue, in line with H1 '25. We continue to make investments into new customer acquisition and are improving efficiency every year. Technology and content improved to 23% of revenue, down 0.5 percentage point as revenue growth outpaced our continued investment in technology capabilities, including our nearshore engineering partnership and new AI adoption capabilities.

We rolled out Claude across our tech product and finance team towards the end of the period, and we see this as a foundational investment with potential to expand to additional teams in H2, which we expect to build efficiency gains over time. G&A was 14% of revenue, down 1.2 percentage points, reflecting good underlying cost discipline. Whilst this slide presents the income statement as management views it, it's worth spending a few minutes explaining the nonrecurring items on the face of the income statement.

We excluded the previously announced Italian regulatory fine, which we are robustly appealing. We also excluded a provision relating to a historical U.S. sales tax exposure. We identified this following revised external advice on state indirect tax nexus rules where sales tax hadn't been applied to certain customer invoices. This has resulted in a $2.6 million restatement of prior period and $0.8 million in the current period. The adjustment has no impact on current period trading performance, adjusted EBITDA, or our FY '26 guidance.

We delivered record adjusted EBITDA of $26.3 million, up 46% and a margin of 17.4%, up 2.8 percentage points year-on-year, which keeps us firmly on the path to our 25% adjusted EBITDA margin target in 2028 and 30% in 2030. The clearest measure of underlying performance is cash generation. We delivered $16 million of adjusted free cash flow, up 5% year-on-year. Our cash flow is very seasonal with H1 burdened by the company bonus payment and H2 typically much stronger, so it makes sense to look at it for the entire fiscal year.

Last year, we also benefited from a rent-free period in the London office and higher interest income on our cash balances, while this year, we paid more cash taxes as the business becomes more profitable. Adjusted diluted free cash flow per share was up 13% to 3.9 cents. As we said before, for the full year, we expect our free cash flow margin to be slightly below our adjusted EBITDA margin. We closed the period with $21.9 million in cash after returning $25.8 million to shareholders through buybacks in H1, plus a further $16.5 million purchased by our Employee Benefit Trust.

Moving down the P&L. The noncash IFRS stock-based compensation charge was $9.8 million (sic) [ $9.7 million ], up from $6.3 million a year ago. This consists of an $8.2 million noncash charge, reflecting the annualization of high-performance RSUs, our new share save scheme, new senior leadership LTIP awards, and accelerated executive severance agreements. Additionally, the charge includes an associated social security charge of $1.5 million. Despite this higher charge, total diluted share count continued to fall, down 5% year-on-year to 424 million as grants were more than offset by forfeitures, employee benefit trust utilization, and our ongoing buyback program.

We anticipate a similar SBC charge in H2 given new execs joining the team. This brings me to the capital allocation framework, which remains consistent and unchanged. First, we invest in organic growth, $13 million were invested in the business in H1, deliberately deploying capital into go-to-market, product innovation, and trust and content integrity. Second, we retain flexibility for strategic M&A that can accelerate our road map. Finally, we return excess capital to shareholders, having executed $25.8 million of buybacks during the first half.

We're halfway through executing the GBP 30 million program we announced in March, which consists of GBP 7.5 million EBT purchases and GBP 22.5 million of outright buybacks. As of September 11, we've completed GBP 15 million. We will complete this program prior to announcing another share buyback. As noted in the statement, we also recently identified a technical point relating to demonstrated distributable reserves impacting certain share purchases under prior buyback programs, which we will remediate in Q4. More details can be found in the statement in Note 10.

Since we started our buyback program in 2024, we have returned $141 million to shareholders and repurchased 11% of the shares outstanding at the program's start. Turning to the outlook. Following a strong first half, we maintain our guidance for high-teens constant currency revenue growth for the full year, alongside a 2 to 3 percentage point increase in adjusted EBITDA margin. We remain confident in delivering sustainable growth and operating leverage over the long term given the significant market opportunity ahead of us.

Finally, as this is my last results presentation, I want to take this opportunity to reflect on my tenure at Trustpilot. It's been about 11 years since Peter, the founder, and I met for the first time in New York to talk about Trustpilot and its massive potential. During this time, Trustpilot has grown 11-fold from $30 million ARR at the end of 2015 to circa $330 million today, and we have taken it from loss-making to approaching 20% EBITDA margin and created an amazing business. The opportunity ahead remains vast, and with the AI tailwind, we're just getting started. I feel privileged to have helped lead such a fantastic organization and remain incredibly bullish on Trustpilot's future. And with that, I'll hand back to Adrian.

Thanks. Thanks, Hanno. So in 2026, as we said in March, we have 3 clear priorities: scaling trust, accelerating with AI, and growing enterprise, all in service of one goal to build trust in the age of AI. Let me take each of these in turn, starting with trust. We think about this in 3 parts. First, review integrity. Our focus remains on an AI-led moderation approach, complemented by human expertise. We're continuously strengthening top-of-funnel detection to ensure the content on Trustpilot remains authentic and reliable. We removed 3.9 million fake reviews in the first half, a 28% increase year-on-year with the vast majority of those removed entirely automatically.

Second, business misuse. We're continuing to take steps to remove ineligible businesses and warn consumers about bad actors. By bringing together fraud, technology, and engineering teams to build a dedicated rules engine spam domain model, we're seeing exceptional results. Our automated systems remove domains and detect policy breaches at scale, and we've reduced threat response times down to a matter of hours. As a result, we've seen an 83% year-on-year increase in business profile warnings, reflecting the growing strength of our detection and enforcement approach. These warnings do more than command consumer attention as they actively signal to large language models that a brand may be untrustworthy.

The third strand of our approach to trust is regulatory and policy engagement. We continue to see the regulatory landscape around reviews evolving as bodies like the CMA and FTC move into enforcement of rules they introduced in recent years. We welcome this and are well equipped to navigate this changing environment. For a deeper dive into the entire trust and safeguarding ecosystem, I encourage you to take a look back at the replay of the May 2026 Trust Capital Markets event that's available now on our investor site.

Moving to the second priority of accelerating with AI. Large language models prioritize the relevance, ranking, and recency of reviews. A recent research study from Seer Interactive showed that Trustpilot is the #1 cited review platform globally. Research from Forrester in July highlighted that Trustpilot often outranks owned and paid media, heavily influencing large language models' opinions of brands and how they show up in results. As you can see on the slide, we recently reached a Semrush domain authority score of 100, the maximum possible, up from 95 last year and a score that belongs exclusively to the world's most authoritative sites.

This is calculated on a logarithmic scale, making a 5-point jump at the top end extremely substantial. Where a business has no Trustpilot profile at all, AI is increasingly highlighting the absence of independent validation as a real trust gap. By contrast, having an active profile drives up to 75x greater citation visibility than having none at all, making Trustpilot essential for any business looking to enhance their AI visibility. Turning to our third priority of accelerating with enterprise. We're driving an unprecedented level of product innovation velocity with 2 major B2B release windows planned for 2026 alongside continuous feature improvements in between.

In April, we released AI search analytics, an in-app review collector, invitation optimizer, and custom dashboards. We also launched a new lower-cost digital-only starter pricing plan. While still early, this is already showing good momentum. We have a second major release window this month focused on helping our customers get AI recommended. I want to highlight 2 specific updates here that are particularly important for enterprise customers. First, multi-domain user management. Previously, user management was focused on individual domains, but to help large enterprises operating across large domain portfolios, we're now launching a centralized space to operationalize feedback. This allows tracking of sentiment and performance side by side across every brand in one view.

Second, review collection by QR codes. This allows businesses to capture feedback in the moment, creating a faster feedback loop and building review volume. By tagging these codes with specific location IDs, customers will be able to track performance across physical touchpoints. And in between, we're continuing to ship updates, including our refreshed product review pages, which I'll cover next, and our expanded Shopify partnership. Back in June, we announced Trustpilot is now a Shopify key product partner. And through our upgraded Shopify 2.0 app, merchants can seamlessly embed Trustpilot directly within their native ecosystem.

We've had a basic product review offering for a long time and have just released a couple of major upgrades. First, a universal product catalog pulls all product variants such as size or color into a single aggregated score, and product review pages give each product its own Trustpilot profile page. These product review pages are currently available across all English-speaking markets, and we're rolling them out in all other languages from the 1st of October. It's important to note that product reviews are not just for retail. Most businesses divide what they do up into different offerings, for example, a bank with different accounts or TUI who are using product reviews for specific holiday offerings.

We tested these across a wider cohort of over 400 businesses during the beta trial and saw an over 15x average increase in AI citation growth, generating 2.4 million ChatGPT citation requests. Product reviews are an opportunity for many different customer types to become more visible in AI search, particularly in comparison inquiries. They're available as an add-on module for those already on paid plans. Our focus on large enterprise customers continues to deliver financially. Customers paying over $20,000 a year have grown at a compound 34% annual rate since H1 2023, and ARR from this segment is up 174% over the same period.

Ultimately, the tangible financial impact of the product innovation we've just been discussing is showing up directly in these numbers, given the immense value customers get from the product. We're also seeing strong regional outperformance, particularly in the U.S., where $20,000-plus customer growth is up 43% year-on-year. As product value and AI visibility grow, our ability to attract and expand these large high-value enterprise accounts accelerate alongside it. To ensure we continue accelerating this enterprise momentum and scale efficiently, we recently reorganized our sales operations.

On the customer acquisition side, we introduced a new outsourced business development model. By shifting top-of-funnel lead generation to external specialists, internal sales teams can now focus on converting qualified pipeline and closing new relationships. On the customer value side, we've split our existing roles to better serve the growing customer base. We introduced a dedicated customer success role focused entirely on onboarding and product adoption. Alongside them, we now have dedicated account managers with a deep commercial focus on cross-selling, upgrading, and driving net revenue retention growth.

It's early days and changes of this sort always take time to bed in, but we believe this model sets us up best to scale into the opportunity ahead. Now to bring all this to life, the enterprise momentum, the product value, and the importance of independent trust signals, I want to highlight a fantastic recent example from Navy Federal Credit Union near Washington, D.C. What stands out most to me is how the Chief Marketing Officer, Pam Piligian, describes our partnership. She notes that when it comes to brand metrics, trust is at the absolute top of the pyramid, but a brand can no longer just tell people trust me, they have to provide independent proof. That's exactly what Trustpilot delivers. We have a short video now to play here.

[Presentation]

We are a credit union we're 92 years young, founded to serve active duty veterans and their families. And we offer a full suite of retail banking products to talk about brand metrics. Trust is at the top of the pyramid. Trust is the metric that matters the most. And we talk a lot about it has to be earned, not given. I can't say trust me, we have to earn it. So putting our own data on the website that says we say 95% of our members are happy, we felt like we didn't have a lot of credibility. So we wanted to make sure that we went to a third party outside, which is how we got to Trustpilot to say help us [ group ] we want the data that we get from our members to be visible to people. And without those metrics that really showed we had done what we promised to from a third-party, Trustpilot, it was hard for somebody to believe.

Large language models are becoming a really important way that people decide who they can trust. How have you seen the feedback that you're getting on Trustpilot play into those large language models and help you there?

Well, it shows up because it's a third-party endorsement. It's not just us saying what the service was like, but it is exactly our members saying what it was like. So it will show up in those different models and what we have found is a lot of our content still matters, third-party content matters, influencers matter as well as these third-party endorsements, which for us, the Trustpilot is a very high source of credibility.

If you had to summarize the impact of Trustpilot on Navy Federal in just one sentence, what would it be?

It would be earned trust. It's proof that we did what we said we would.

So to wrap up before we open the line for your questions. Financially, we achieved 18% constant currency bookings growth while increasing adjusted EBITDA by 46% and margin by 2.8 percentage points. Strategically, we firmly established Trustpilot as the leading AI trust signal with the #1 cited review platform globally backed by a perfect domain authority score of 100. We're moving faster than ever to capitalize on this accelerating product innovation. The enterprise focus is proving highly effective with ARR from customers paying more than $20,000, up 35% year-on-year.

We're excited about the trajectory we're on, the value we're delivering to businesses and consumers, and the huge market opportunity ahead as AI continues to grow. We remain well positioned for the second half of the year. And with that, thank you all. We're now ready to take your questions.

[Operator Instructions] Our first question comes from Olivia Venancio at Barclays. Can you walk us through why no upgrade in the full year guidance and how we should think about shape in H2? Can you walk us through dynamics region in EBITDA margin? I think U.K. is accretive, but is U.S. still? When should this change?

Well, I think we've been given pretty clear guidance of 2 to 3 percentage point margin improvement for the full year, and we're well on track on this. In the half year, we had 2.8 percentage point improvement on the margin, and we're well on plan to deliver this as well as reaffirming the guidance for the full year and the longer-term outlook, of course, of 25% EBITDA margin in 2028 and 30% in 2030. And as you think about seasonality between H1 and H2, obviously, we talked about the sales reorganization happening in H1.

The H1 margin tends to be lower than the H2. We typically have sort of a steady margin progression in the business. If you followed it for a while, you'll sort of see that trend basically repeating every year. And so I don't think the progression should be a surprise to anyone. And I mean we talked about regional profitability. I think if you -- and contribution margin, highlighting the strength in the U.K. in particular, but we also talked about the U.S. being very profitable on a contribution margin basis. And if you reallocate your product and tech and G&A expenses, I think you'll also find that the implied EBITDA margin for each of these regions is breakeven or positive.

Our next question comes from Hai Huynh. There's a couple of questions here. So number 1, adjusted free cash flow fell from 85% to 61%. What level do you regard as normalized once billing benefits, exceptional bonus, U.K. tax credits, and lease payments settle?

Yes. Like I said in the prepared remarks, I think it makes sense to look at cash flow on a trailing 12 months or a 12-month basis really or rolling 12-month basis. And it's always around 1 point lower than the adjusted EBITDA margin. Last year, obviously, we had some H1 benefits with the free rent. The interest income as we've utilized balance sheet cash to buy back shares and brought it down to more appropriate levels for the scale of the business has come down, and that has impacted the free cash flow.

As the business continues to grow, that will be a less and less relevant factor in overall cash generation, of course. And the working capital benefits of the prepayments are sort of now at a normalized level, which will sort of offset CapEx. And so if for your modeling, I think using adjusted EBITDA margin and then taking a point off of that as a proxy for free cash flow for the full year is a good starting point and then taking into account the seasonality. So obviously, the implied H2 margin will be materially higher than the H1 margin given those seasonalities.

And the second question, what was the parent company's distributable reserve balance in the newly filed interim accounts? And how much headroom will remain after completing the current GBP 22.5 million buyback?

So I don't have the headroom off the top of my head. I think we're in the process of doing another interim dividend from A/S, our Danish operating entity to the plc in September, and then we'll continue to file updated plc financials. And those will then cover the remainder of the existing buyback and provide additional headroom. And then sort of as we progress through the year and the time, we'll continue to do interim dividends to generate distributable reserves up at plc and then utilize those for buybacks.

And third question, Trustpilot spent $42.5 million on conventional buybacks and EBT purchases against $16 million of adjusted free cash flow. What minimum cash and liquidity buffer will the Board maintain? And what level of EBT purchases should we model going forward?

So we don't sort of look at EBT purchases and buybacks as materially different. I think we look at it combined. That's why I referenced the GBP 30 million program at the beginning of the year and the split between those 2 is sort of -- we're certainly sort of agnostic to that. The EBT obviously provides a great way to offset dilution from satisfying share grants that have been issued in the past and that are vesting and to mop up excess liquidity upon vesting when employee vesting happens and there's shares sold for tax to cover the taxes.

And so I wouldn't sort of break those out separately. Look, we -- in the beginning of the buyback program in 2024, we had a ton of cash on the balance sheet as a result of the IPO, and the business became profitable and cash flow positive. So we generated more and more cash, and we've used excess cash from free cash flow, but also from the balance sheet to bring down those balances. And obviously, we're not going to get pinned down on a number of what's the right amount of cash.

We're managing this. The business, as you pointed out earlier, has seasonality in cash flow. So in H1, we probably need a higher cash balance than in H2. We're continuing to generate more and more cash flow. And so I think we're sort of actively considering this as the business progresses and grows and continues to become more and more profitable and cash generative.

Our next question is from Joseph George at JPMorgan. Could you please talk about the disconnect between the very strong adjusted EBITDA growth, plus 46% year-on-year, and the slower FCF growth, 5% year-on-year. How confident are you for the free cash flow conversion through full year '26 as a whole?

Thank you. And I'm not sure what's more to say beyond the question I just answered in the prepared remarks, but I can continue to emphasize that, obviously, we have good forecastability of our free cash flow. And in the back half of the year, we are accruing expenses, for example, for the company bonus, which is a burden on EBITDA, but it's not a cash outflow. And in the first half of the year, all that cash flows out of the business.

We also have, obviously, visibility around the timing of tax payments, et cetera, and we have good forecastability on our lease payments. And so I'm very confident with the guidance I gave you for the full year free cash flow of about 1 point below adjusted EBITDA and then the implied meaningful uptick in free cash flow in the second half.

Our next question is from Gareth Davies at DB. Sometimes when a shifting sales model, you get a little short-term disruption. Has there been any detrimental impact in the more established businesses as a result of the sales team shift?

So look, I think our teams have done an exceptional job of performing strongly through the period. And you can see that in our results, constant currency bookings growth. That means I joined the business almost exactly 3 years ago. I remember sitting here reporting a couple of days after joining first half of 2023, we had grown by 16% constant currency bookings. So we've actually -- we're actually growing faster now than we did back then. Now of course, with the sales reorganization, every moment of time and energy that's spent on that is time that isn't spent talking to customers.

So there's always some opportunity cost. And as I say, these things are always something that an organization has to go through and get out the other side of. So we've been doing that. But I'm just impressed that the team has performed strongly through it. As I say, these changes are all about setting ourselves up for scale on the new business side because we've got a vast opportunity to go into, and we need a mixed model to go after that. So I think about the combination of digital acquisition, which we've expanded with the launch of our starter plan that's going very well.

I think about Agentic AI, I think about outsourcing, and of course, in-house capability that we still have. So we've got this kind of mixed approach to go after an opportunity that is absolutely vast. And then on the customer success side, we've got an increasingly diverse and complex portfolio of products. As discussed on this call, a lot of new product coming up. So we really need that segmentation of roles between CS that is all about helping the customer use the product and account management that is more about the commercial side and the renewals. So I think we've done a good job of executing that in the period, delivering strong results at the same time, and setting ourselves up for the future, most importantly.

We have a follow-up from Joseph George at JPMorgan. What gives you the confidence that requisite controls are in place to ensure no further provisions will be required in relation to historical U.S. sales taxes?

Yes. Thanks, George. So [ Joe] -- so we've done a lot of work obviously in the half, leading up to the provision that we made. We retained external advice from a Big 4 firm, and we looked at this together with our auditors and gone through sort of this on a state-by-state basis and looked at sort of the historical invoicing that we had done and where we should have applied taxes. And so we feel that we have a robust provision now on the balance sheet that will cover the potential past liabilities.

Our next question comes from Jessica Pok at Peel Hunt. Is there an incremental cost of having outsourced business development capacity capability? Can you give me color on the shape of the $100,000 contracts and what they normally consist of, e.g., multiple international domains, deeply complex integrations? How are the new clients using your platforms at this level?

Yes, happy to talk to both of those, Jessica. So in terms of outsourcing, the model is more efficient in the sense that you can do more activity at lower cost. And as I said in my earlier answer, the whole rationale of this is to have a model that is suited to the scale of the opportunity. When we look at the size of our addressable market across the geographies, we're still very, very low penetration rates, particularly in the U.S., but also in the U.K. and our other focus markets. So we've got a very, very long way to go. We've got multiple, multiple industries with businesses that have a need for Trustpilot, and we need a cost-effective way to go out and capture that.

As I say, that's not all about outsourcing. It's also about digital acquisition. We're increasingly looking to AI and how we can use AI to go after it more efficiently along with substantial in-house teams as well. But it's all about cost effectively addressing that scale opportunity. And then secondly, you asked about customers paying more than $100,000 a year. So what gets businesses to those sorts of price points are a combination of a few things. First of all, the sheer scale and size of the customer, meaning that our product is going to have very significant impact on them.

Then we've got the grade of the product they're buying. So typically, businesses at that scale would be on our enterprise plan, which contains all of the key features and is the most expensive. Then you've got territories, countries, and then you've got domains or brands that the business wants to use us across. Typically, once a customer gets to that level, they're using us across the full value proposition. So we talk about -- when we speak to businesses, we talk about build trust, grow, and improve.

So you collect feedback, you respond to it to build trust with customers, you showcase your feedback in the Trustpilot brand to help your business grow. And then you look at our analytics products to help you improve. And typically, a customer at that level will be using us across all of those things, making us very important both on the marketing side and on the customer experience side for the customer.

Thank you. We have a couple of questions in from Sean Kealy, so I'm going to break these up. Number 1, clearly, you can provide visibility of citation uplift for brands with an active Trustpilot profile. Where do you get that data from? Does it come in directly from Google or OpenAI, for example?

Yes. So the answer to that is we do 2 things. First, we look at external sources like Seer Interactive, which I talked about a couple of times in the prepared remarks. And secondly, and this is where the 75x figure comes from. We look at our own server logs, which tell us every time we get a request from one of the large language models. Now those internal server logs aren't a perfect representation of citations because the models are training on our data. They're not just doing real-time inference.

And what that means is they're very often citing Trustpilot without it actually hitting our server logs. So the data we see in our server logs is a small subset of overall citations, but we combine those 2 things, the external sources and our internal server logs to give us a good impression of what's going on. The consistent theme across all of that is that relevance, recency, ranking are the key things. So in other words, having a large enough volume of reviews such that the LLM can surface an answer using reviews that is exactly relevant to the user's query, then having reviews that are recent.

So if your reviews are stale and many months old, then it's much less likely to get cited by a large language model. And thirdly, ranking, which is the authority of the source effectively, which is where Trustpilot itself with that domain authority of 100 we're talking about, the authority of us as a source becomes important. So all of that means that basically the more feedback and the more frequent feedback companies are getting, the more likely they are to be showing up in large language model search.

And a follow-up from Sean Kealy. Is there more that you can do to support brands looking to assess their return on investment on their spend? For example, click-through from AI overviews, subsequent purchase behavior, or even tying AI search patterns into spend on the other paid media sources?

Yes. So we are doing more with customers in this area. You'll have seen an ROI study that we did a while back with Forrester that showed a 401% ROI on average on customers' investment in Trustpilot. But we are doing more, particularly with larger customers to help them really quantify the ROI. It isn't, of course, as straightforward as simply things like traffic metrics because so much of the value is from citations or from the growth that they're driving through featuring Trustpilot in their own media, so getting a higher ROI on media budgets.

So it's not as simple as just looking at, for example, how much traffic they're getting through Trustpilot. But when you add up all these sources, it's really substantial value creation. And we think that's only increasing as AI takes off.

And the final question, can you run us through in some detail what has happened with distributable reserves and what the likely impacts are? And if you expect to be able to plan or launch a further buyback in the future?

Yes. So the impact is on the financial performance is nil. It's a technical sort of issue that we need to cure, but it doesn't change any of the numbers or any of the cash balances or whatever. And also, I think what's really important to note and state for the record is that the total amount of buybacks do not exceed the total amount of distributable reserves that were available in the period.

I think what happened is largely an issue around when and what we demonstrated in the plc filings in terms of available distributable reserves and then having done subsequent interim dividends that were not reflected in the filings while the buybacks were ongoing and utilizing those reserves, which happened to other companies as well. As you'll see in the Note 10 in the context of the first buyback, we actually executed the buyback prior to having the reserves, which were then subsequently generated through the share capital reduction in May of 2024.

And that was -- all that was due to a sort of a technical accounting mistake where we had erroneously included share-based compensation reserves in the amount of retained earnings that we deem distributable, which upon further review, we identified as wrong and they shouldn't have been included. And so we were -- we thought at the time that we had demonstrated sufficient reserves when, in fact, we hadn't, and we should have filed interim updates on those plc financials to also demonstrate them.

And so obviously, we will now remediate this. There's a well-trod path on remediation in the U.K. And once it's all remediated, we'll continue to -- obviously, the business is generating profit and retained earnings, and we can then demonstrate such with plc interim financials and full year financials that we continue to file, and that will then allow us to continue to do buybacks.

But I guess the key point there is this has no impact on our trading and on our actual cash or anything to do with future performance.

And a couple of questions from David Nolan from Morgan Stanley. Number 1, North American new business sales were up 76% year-on-year. Is that level of acceleration broad-based and sustainable? Or did H1 benefit from unusually strong large deal timing?

So we were thrilled by the pace of growth. I think a few things to unpick. So first of all, the market opportunity for us is vast, and the growth flywheel, as we call it, is clearly accelerating. So when we look at all of our flywheel metrics, whether it's review volume or TrustBox impressions, they're absolutely flying in the U.S. So we can really see the proposition catching on there. We saw very significant growth in the enterprise segment. There was a tailwind from some of those billing relationships that we mentioned shifting from the U.K. to the U.S. But overall, we expect to continue to perform very strongly in the U.S., which is a huge opportunity for us.

And the second question from David Nolan. Group NDR is 101%, while larger customers historically have stronger retention and expansion. As enterprise mix increases, should we expect group NDR to trend back up from here in financial year 2027?

Yes. So there's a lot going on under the hood with NDR. As you correctly point out, the enterprise segment has the most attractive NDR, and that's part of the reason why it's strategically our priority and our fastest-growing segment. We also, as we mentioned, launched in the period digital acquisition of our starter plans. So that's having a very beneficial increase at the low end on logo growth, but those low-end logos obviously have lower net revenue retention rates.

The way I look at it is we always target NRR of 100% plus. And what's great about the business today versus a few years ago is it's off a base of gross retention that is far healthier. So we're at 87% GRR, where a few years ago, that number was 84%, and that is a much healthier basis on which to deliver long-term sustainable NRR. What gives me confidence in it is the level of product innovation in the business. We talked, for example, about product reviews, which is an add-on module that customers need to buy with huge value associated with it. So that's the sort of thing we're going to continue to do to bridge the 87% GRR with the 100% plus NRR that we aim to deliver going forward.

We're going to go back to Gareth Davies at DB. Accepting that the U.K. is selling globally and would be healthy teens, including that benefit, the 11% felt a little sluggish, penetration only 5%. What gives you the confidence that this is a business that can still grow teens or should we now see this as a single-digit 10% growth profile looking forward? How much does the uncertain macro backdrop impact?

Well, think about some of the stats we've been sharing today, Gareth. I mean we saw enterprise growth of, what, 27%. We can see our GRR gross retention in the U.K. is exceptionally strong, above the group average, 88%. NRR is also ahead of the group average. The contribution margin, as Hanno pointed out, is really exceptional at 67% (sic) [ 60% ]. And what I've also pointed out today is how incredibly broad-based that growth is in the U.K.

So we've got over 40 different industry verticals where Trustpilot makes at least $1 million of ARR in the U.K. If you think about that for a second, I used to run a business where we built the entire company off one of those industry verticals, which was the accounting vertical. We've got paying customers more than $1 million of ARR in more than 40 different industries. And that's why we're very clear that the U.K. penetration is extremely low, around 5%. The business has a very long way to go here.

The way we look at growth across regions is always to optimize group performance as we say. So the question, I think, to ask is how are we going to deliver long-term mid-teens bookings growth globally for Trustpilot, and that's ultimately what we're aiming for and optimizing for rather than the growth of any particular market. And as we've seen in the U.S., a few years ago, it was growing more slowly. Now it's accelerated. So individual markets will fluctuate, but we always aim to deliver and optimize the group performance at the end of the day.

I should also add, we've got next week, our biggest ever customer event in the U.K. coming up, Trustpilot Horizon, where we'll be launching some of our new B2B products that I mentioned on this call. That's had an absolutely fantastic take-up. So we're still very bullish about this market and pushing hard to acquire more new customers.

And a final follow-up from Gareth. In the U.S., can you talk a little around subsector momentum? Any subsector stand out as particularly strong? Any new areas of focus that have opened up?

Sure. Yes, happy to do that. So we saw great growth in insurance, in well-being, in software technology. So we're starting to see as in the U.K. that our growth is catching on across different industries. Often the way it works is 1 or 2 leading companies in the sector will start using Trustpilot. Others will see that, and then it will catch on within that industry. So we're still more concentrated in the U.S. than we are in the U.K., for example.

So financial services in the U.S., insurance, credit, banking, absolutely fantastic vertical for us. But that is gradually broadening. And as I say, we saw great growth in software, well-being, parts of retail like appliances and electronics doing extremely well, too. But one of the lovely things about this company and running it is we're constantly choosing between attractive options. We have many different paths to deliver growth, and that includes different verticals within the U.S. market.

And a couple of questions from Chris Lewis. Number 1, on revenue, H1 came in at 19% constant currency against the full year high teens guide. Given the revenue base for H2 is largely locked in by H1 bookings, is the implied H2 deceleration a statement about booking momentum?

No. I mean, I think it's a reflection on this year's bookings performance. I mean, as you rightly point out, revenue in the back half is largely a function of bookings last year and year-to-date. And I think the guidance remains kind of high teens for revenue. And our bookings growth is mid- to -- mid-teens guidance. And so I think we'll -- and then adjusted EBITDA margin improvement of 2 to 3 percentage points. I don't think it sort of speaks to or implies any massive deceleration in any of these metrics.

And Chris' second question. ChatGPT citations were up 400% plus year-on-year, 75x citation visibility for active profiles. Where does that show up commercially? Is it converting into higher win rates or shorter sales cycles or price? Is there evidence AEO is expanding willingness to pay? Or is it mainly a retention argument so far?

So in a sense, it's all of the above. I mean when we talk about 35% growth in customers paying more than $20,000, almost every conversation we'll have had with those businesses involves AEO at some level, large language models, how they're showing up. Is it supporting willingness to pay? Absolutely. It's helping in those conversations. It's helping with our GRR. I think it's just being -- it's just becoming a core part of our proposition of how we go to market.

And it is -- the great thing is we're not saying anything that is going against the flow. If you look at any kind of best practice around answer engine optimization, what everybody says is you should be engaging with real customer feedback out in the open in a way that the large language models can actually access. So we're helping businesses to do that in practice, and we're operating the world's largest open customer feedback platform. So yes, it's a fantastic time to be doing that.

And final one from Chris. On the U.S., you've given a 60% contribution margin for the U.K. Can you give the equivalent for North America or at least the direction of travel?

Yes. I think we've given a number at the full year, and we'll probably update it again at the full year, but it's continuing to improve as the business grows. Keep in mind, though, in particular, in periods of very strong new business growth, the margin expansion is not as quick because the new business obviously comes at a higher acquisition cost, which is reflected in the sales and marketing costs, while the retention costs are all reflected in our gross margin.

And so we're extremely pleased with the performance of the U.S., the 75% plus new business growth that we've seen year-to-date, which is sort of testament to the ability to monetize the AEO tailwinds. But in periods of strong new business growth, the margin expansion is less pronounced given that sort of that comes with sales and marketing expense and commission expense that are higher in the period, while the revenue from those bookings will sort of then follow subsequently and then drive sort of the margin and retention in the subsequent years.

Our next question comes from Roger Phillips at Investec. Why did you do a sales reorganization in H1 in the first instance? Is top of funnel so good now that Trustpilot has critical mass in the U.S. that you can afford to outsource new business generation? Or is pipeline conversion something to be improved with internal sales resource? In other words, can you phase this direction completely in different ways. So what was the reasoning?

Yes. I mean I think I've already commented on why we did this part that you're referring to on new business, which is all about having a go-to-market that is suitable, that's designed for the scale of the opportunity. And as I say, it's not only about outsourcing, it's having a mixed model that mix is digital, outsourcing, in-house, and we're starting to see good results from adding some AI into that mix as well. So it's a mixed model, but outsourcing was the new component for us in H1 that this reorg entailed.

I think as it speaks to the U.S., it just highlights the need for something like this, which is we're talking about the largest economy on the planet. We believe we've got a proposition that is accessible to ultimately dozens of different industries across that economy. We're never going to have a large enough group of people in a place like New York, which is where most of our team are based to go after as much of that opportunity as we can. So it's all about finding the most scalable, cost-effective way to address an opportunity of that size. And as I said, it's ultimately a mixed approach, mixing digital, outsourced, and in-house, which is what we've now got.

And our final question today is a follow-up from Sean Kealy. In terms of product plan from here, what's next in the business information, research, or operational insights part of the offering? Are there steps that you could take, e.g., to allow customers to access your audience for market research purposes?

So as we said, we've got a lot coming up this year. We've got a major event next week where we're planning to announce some of these enterprise features that we've talked about on this call, particularly multi-domain in the product is important for some of the largest brands because they'll be operating lots of different domains on Trustpilot. At the moment, they have to log in separately for everyone. Now they'll be able to see an overview with all of their brands.

We are improving our analytics products as well. You mentioned how we're planning to deepen those. Well, we're looking at search query insights. So when people are typing something into a large language model, what insights can the business get about that, that's going to help them with product development, for example. So that's the sort of thing we're working on at the moment. We've got a lot of ideas for 2027, and no doubt we'll have another very busy agenda for next year.

There are no further questions. So that concludes today's results presentation. Thank you all for joining. Have a nice day.

All right. Good afternoon, everyone, and a warm welcome to all of you and those joining by webcast. Thanks for being here for what I hope will be an interesting afternoon for everyone.

We have a packed agenda during which our Chief Trust Officer, Shazadi Stinton and members of our Trust team will bring to life the way we make Trustpilot the world's most trusted open customer feedback platform. Our CFO, Hanno Damm; and Chief Product Officer, Ciaran Dynes, will talk about the benefits of being an open platform in the age of AI. And we'll then move on to a Q&A session in the room.

Those of you online will be able to submit questions through Spark Live, which we can answer in the room. We then have some breakout sessions to dive deeper into a few areas with the opportunity to ask further questions. Finally, we hope those of you with us in person will be able to join us for a few drinks at the end.

So let's kick off. Trust is and always has been the most important thing in business. Whatever industry you're in, trust is what makes people want to work for you, invest in you and be your customers. The fundamental ways in which businesses build trust are also timeless, be transparent with information, be credible on by delivering on your promises and treat people with respect as humans.

But in the age of AI, it's easier than ever to look and sound credible. So deciding which businesses to trust becomes more challenging. Trustpilot has a critical role to play as a governance space in between the chaos of social media and the bias of information sources controlled by businesses. As large language models amplify the feedback on Trustpilot, our platform is becoming the critical trust signal for the age of AI.

In essence, Trustpilot products collect customer feedback and turn it into influence over the actions of people and businesses. Because the feedback has influence, people are more motivated to write it and businesses want to engage with it. The more AI advances, the more influence that feedback has. For almost 20 years now, we've continually invested in the know-how and technology needed for this work. From cutting-edge AI to specialist human expertise, we will do whatever it takes to keep the platform trusted as the world around us changes.

While today is an educational event, it's vital to understand this. Trust underpins all of our performance. The evidence you will see this afternoon is what ultimately gives us the confidence in the targets we set out to the market back in March, including reaching an adjusted EBITDA margin of 30% by 2030.

Today, you're going to hear a lot about the work we do to maintain trust in the platform. You'll be wondering how do we measure the success of all this activity? The ultimate yardstick rather than being a single metric is the simple fact that with complete freedom of choice, millions of people and businesses find the feedback on Trustpilot useful.

CEOs get their teams to read and act on the feedback because it's genuine. People turn to Trustpilot again and again when deciding where to spend their money because they actually learn something useful from it. The platform, as you can see here, has grown in scale and influenced more than ever since the rise of large language models.

In 2025, our users submitted more new reviews to Trustpilot than in the first 12 years of the company combined. Our unique data set expanded to 361 million active reviews, representing a 20% year-on-year increase. The volume of activity on the platform isn't just a sign of how much Trustpilot is trusted. It also gives us the data to sustain that advantage.

As we'll see today, the volume of feedback on Trustpilot is critical to our ability to identify and remove content that violates our guidelines and so is itself a valuable competitive moat. Today, we're going to take you through the systems, capabilities and discipline that make Trustpilot the trust layer of commerce in the age of AI.

To do that, I'd like now to hand over to our Chief Trust Officer, Shazadi Stinton, and her team. Over to you.

Thanks, Adrian. Good afternoon, everyone, and thank you, Adrian, for the introduction. As Adrian mentioned, I'm Shazadi, and I'm the Chief Trust Officer here at Trustpilot. Today, I'm going to focus on how we build trust at scale. I lead the team responsible for trust, transparency and platform integrity. Our role is to ensure the platform consistently delivers authentic, reliable information at scale.

This is supported by a trust infrastructure organization of over 200 specialists across policy, enforcement and technology. At its core, our challenge is simple: how do you maintain trust in a global platform operating in real time and at high volume?

Today, I'll cover how trust is built, how it scales and why it's increasingly critical to decision-making. But first, let's define what we mean by trust. We see trust is built on 3 things: transparency, credibility and humanity. Transparency starts with visibility. People trust what they can see and what they can see in full, not just the positive, but the negative and the unresolved. The moment information feels curated or incomplete, trust doesn't build its stores. For us, transparency means exposure to reality, not a polished version of it.

Next is credibility. Credibility is what makes that visibility usable. It's not enough for information to be visible. It has to hold up. That means consistency over time, signals that are difficult to manipulate and systems that behave in ways people recognize as fair. Credibility is what turns something people can see into something they're willing to act on.

And then there's humanity. Trust is ultimately a judgment about intent. Are businesses listening? Are they responding? Do they acknowledge when something goes wrong? Humanity is what turns a transaction into a relationship. For us, they are nonnegotiable, get them wrong and no level of product sophistication can compensate, but get them right and trust compounds over time.

So let me now turn to Trustpilot itself and why it matters more today than ever before. The Trustpilot, we're an open independent platform where consumers and businesses come together around one thing: genuine customer feedback. Since 2007, we've built one of the largest review data sets, over 361 million reviews, with more than 190,000 added every day.

But scale is only meaningful because of what it enables. We help consumers make better decisions at key moments. And we help businesses understand feedback and improve their performance. Our ambition is simple: to become the universal symbol of trust.

To make trust real and scalable, we operate with 5 core principles, which underpin everything we do in trust. So we are neutral, our systems do not favor consumers or businesses. We're open, anyone can share a genuine experience and businesses can respond. We're fair, the same rules apply to all users regardless of who pays. We're transparent, we explain what we do and why. We publish clear guidelines on how we operate, moderate content and calculate the trust score, and we're relevant.

We continuously improve the experience for both consumers and businesses to ensure the platform remains useful at scale. These principles are embedded in how we design systems and enforce policies at scale. So let me ground this in how the system actually works.

First, every business is held to the same standards. There are no preferential rules for paying customers. Second, moderation is based on evidence and consistent rules. It is not influenced by commercial relationships. And third, ratings are earned through customer experience. Businesses that actively engage with customer feedback and use it to improve their service tend to perform better over time. They build stronger customer relationships, increased retention and are better positioned to adapt to changing expectations.

A trust score is, therefore, not just a rating. It is a reflection of real customer experiences and how businesses respond to them. So at the center of our platform is a really simple loop. A customer has an experience, they leave some review -- they leave a review and that feedback is published transparently. Businesses that respond and improve typically deliver better experiences over time. Those improvements then show up in future reviews. Trust, therefore, builds through behavior.

So we provide paid tools that help customers collect and act on feedback, but those tools do not influence what is published on the platform. The system only works because users trust that reviews reflect real unmanipulated experiences.

Now trust is being rebuilt in the age of AI. What was once a moderation challenge at scale is now an adversarial environment where organized bad actors use AI to generate fake reviews, synthetic identities and coordinated the campaigns instantly and at scale. The cost of this section has collapsed. That changes the game. Winning platforms won't just react faster. They'll use AI to stay ahead, detecting patterns earlier, acting in real time and improving decisions continuously.

At Trustpilot, we've built for that reality. So we operate trust infrastructure not just as a review platform, continually assessing authenticity, detecting abuse and enforcing standards in real time. This is powered by our proprietary AI trained on years of unique data designed to both identify bad actors and improve overall platform integrity.

So we balance 3 priorities: protecting consumers, ensuring fairness for businesses and maintaining transparency in how we act. Today, we'll show you how that works in practice. Our safeguarding ecosystem and how we apply our policies at scale, how we address misuse by businesses, how we use AI to detect and prevent fraud and how we stay ahead of a rapidly evolving regulatory landscape.

We'll close this session with a conversation between Hanno and Ciaran on why openness wins in AI. So our belief is simple. Openness will define trust in an AI-driven world. Finally, we will move to 3 breakout sessions covering fake review sellers, business verification and community flagging.

So the key message I'll leave you with is this: trust at scale doesn't happen by accident. It is engineered, tested and continuously improved. And as deception gets cheaper, the ability to defend and strengthen trust becomes a critical competitive advantage.

So I'm now delighted to hand over to Maj, Sona, Thomas and Dominique to take you through the details. Thank you.

Good afternoon, everyone. I'm Maj Santhakumar, Senior Director of Trust Operations. Today, I, along with my colleagues, Thomas Sona, who unfortunately can't be here today, but will be joining in via recording, and Dominique are going to speak and give a sneak peek under the hood of how we protect trust in the platform. Our vision is to be the universal symbol of trust. And over the next 30 minutes or so, we will show you the infrastructure that makes that possible at scale.

Because we are an open platform, anyone can write a review, whether invited or unprompted. With that openness comes responsibility. All users must follow our guidelines, whether they are reviewers or businesses. To enforce these guidelines, every review on our platform is moderated through our 3 pillars, also known as our safeguarding ecosystem: our technology, our experts and our community.

Our technology helps us detect fake reviews, apply our policies consistently at scale and identify potential misuse by businesses. Our experts support this by adding context, nuance and judgment where automation alone isn't enough. And our community plays a vital role by flagging content based on their own experience and knowledge. Ultimately, technology is the foundation that allows us to protect trust at scale. But it's a combination of technology, people and community that makes our platform resilient.

Now, I want to lift the lid on what happens behind the scenes with our technology when a review is submitted. Every review enters a 2-hour posting delay before it appears on the platform. This gives our automated systems time to act and prevent bad actors from learning and adapting to our detection methods.

During this window, reviews go through 2 key layers of checks, fabrication and guideline enforcement. At Trustpilot, accuracy in our detection systems isn't just a technical goal. It's fundamental to maintaining trust across our platform. Some of the best published academic results on comparable data demonstrate around 1 in 100 false positives at fake review detection tasks.

While that may be acceptable in a controlled research environment, applying that standard at Trustpilot scale would have serious real-world consequences. With millions of reviews flowing through our platform, even a 1% false positive rate could result in hundreds of thousands of authentic consumer voices being mistakenly removed. That's why we hold ourselves to a significantly higher standard.

Every genuine review represents a real customer experience and removing it incorrectly risks undermining confidence in our platform. We detect, fabricate reviews using behavioral patterns, such as businesses reviewing themselves or coordinated review seller activity. These models continuously improve through human validation, our own research insights and feedback from user appeals.

At the same time, we operationalize policy at scale using AI. This includes detecting advertising or promotional content, highly sensitive personal data and harmful or illegal material, areas where consistency of decision-making and speed are critical.

Making all of this possible, we have humans in the loop. Our internal experts conduct proactive investigations and validate edge cases, including training and refining our AI and LLM-based decisions. Together, this combination of automation and expert oversight is what allows us to protect trust at scale.

Let's show some examples of our technology in action. These are real reviews caught by our automated technology during the posting delay. AI enables us to catch this content before any genuine user can see them. I'm sure everyone in the room will agree, content like this has no place on Trustpilot. And we work hard to remove this before anyone can see it.

I'm going to pass on to Thomas, who will look through -- who will go through how we tackle business misuse on our platform.

Good. So I'm Thomas -- and the name maybe sounds not really English, I'm not. I'm from the Netherlands. So there is a bit of an accent, I'm sorry for that one. But I'm going to talk about business misuse today because Maj and others have talked about fake reviews. But actually, what we see when it comes to platform, it goes beyond the fake reviews.

There is a whole work when it comes to people that are misusing the platform, for example, the way of inviting. And what I want you to do is to show you actually what that is. So business misuse, otherwise that fraud, what is fraud. Fraud comes in various ways. And that's what I'm trying to visualize here.

We've got review sellers, very visible when we have our protection against them. Fake and illegal businesses, for example drug dealers, illegal medicines that are being sold, scam operations, imposcinating Trustpilot. Those are fraud types that you will see in other businesses as well.

What is specific to Trustpilot is business misuse of our platform. And I touched upon it very slightly, but I would love to dive into it a little bit more. So it's not only about the fake reviews. It is about how are you collecting your reviews. Are you doing that in a fair neutral and unbiased way. The information that you have on your profile page, is that actually right? Are you presenting yourself with the right information?

So business misuse is covering a whole area. And actually, this is never going to visualize at all because fraud is constantly changing. What fraud is today is not what it is tomorrow. It's evolving. It's getting new kinds of fraud, new techniques. So it's our job to continuously follow up, detect and protect.

And when it comes to protecting, we need to have a very clear policy and enforcement. My background is in law enforcement, so I feel very comfortable to set clear guidelines on what is allowed and what is not allowed. And that's also what we have done with Trustpilot. We have created very clear policies. And those are the ones that are reflected as guidelines for businesses and they're in our action we take policy.

And actually if you breach those ones, that has consequences. And that's what you see here on the side. So we start with education. Because, yes, there are people that are doing things that are wrong, but they simply don't know. That's where education is really important. But the bad actors are the ones that will continue and those are the ones that do it deliberately.

So that's where we -- if we continue to detect the misuse, we will move on to the next step. That's a warning. Then, again, if that continues, we go on to the legal notice and then it becomes serious. That is your last chance because if you then continue, we will have to take more clear and strong actions. And that means that we will terminate the contract.

Whether you are a paying customer or not, it doesn't matter. My team is not connected to commercial. So I've got the joy to say, you're doing it wrong. That's out. And that means that we terminate the contract. We limit the access to the platform. We place a clear consumer warning on the profile page. We hide the TrustScore because actually the TrustScore, that's the tool that people use to gain trust. Why do people use Trustpilot? It's to gain a level of trust from consumers. So if we would still show the TrustScore, then they still get the benefit. That's why we hide it.

Another very strong one into that one and especially in the age where we are right now is search data. So once we go this far, once we have detected, once we have terminated, we also stop the data sharing. So people will not be, through our data, visible on Google.

And then finally, there is an opportunity for us to go even beyond and to take legal action. We do collaborate with authorities. We report people. And we have even taken legal action against, for example, review sellers. Specific to the review sellers, we will talk about that in the breakout.

Now let's make it a little bit visible. So I have shown you what kind of actions we take, but how does that look then? This is -- once the business has continued the misuse, this is the final stage. This is where we have transparent and clear told you why we have terminated this business and what they have done wrong.

And actually, if we then look into actions because we do need to talk about actions to make it clear that we really are serious about this. On screen, you see numbers, ineligible businesses, businesses that we believe shouldn't be on our platform, either because they don't meet our core values or because they are illegal and fake businesses.

What you all see is consumer warnings. We see what we have done off platform. Let's dive in a little bit. So we have removed close to 12,000 businesses over platform straight because we believe they shouldn't be on our platform, illegal businesses, for example. We have placed 10,338 warnings to inform our consumers about there is a risk on this business or, hey, this business has misused our platform.

Fake reviews, 7.8 million reviews we removed in 2025. And to show you that we are really serious about this. And this is when my passion comes out, right? We are making this in a much more efficient and more scale year-on-year. And that's what you see in the increases: 184% increase on eligible businesses, 20% increase on where we are actively informing our consumers about the misuse.

Now in the review session, we will go a bit more into detail, so I'm going to swipe on to the next piece. And this is where it's going to be an interesting one because this is where you've got to play a role. We're going to play a quiz, which is called is it real or is it fake?

What are we going to do is you're going to be part of my team today. You are all going to be a fraud investigator or you're going to participate as if you are our software. And what you're going to see is you're going to get 2 reviews. And we start with the content of the review. And along the line, I'm going to show you more insights and more data. And every time I ask you to think, is the first one real or is the second one real, how do we do that?

Slido. I have to have use slido. So I'm a bit new to that. But if you scan the QR code, then it should allow you to get in this one. The screen that allows you to say or to answer every time which is the fake review.

All right. Here it comes. So read the reviews carefully, 2 reviews, both reviews is about a headset, headphones and so on. And now the question is, based on what you're seeing here, you can use the star rating, you can use the content, you can use the name. Which review is fake? Is Review 1 fake, then select Review 1. Or if you do believe that Review 2 is fake, then select Review 2.

And in the meantime, I keep the pause. I keep an eye on the results. I might get you to see if the results are there. So 65%, it's moving a little bit, believes Review 2 is fake. Now I'm not going to reveal it now, but I'm going to give you a hint. And I'm going to talk a little about this because if I look at this review, I see spelling mistakes.

Now spelling mistakes doesn't necessarily tell you whether it's a fake review. Actually, if I write English, I might put in a small mistake sometime. If I write Dutch, I might be better. Review 1, that may be the one that's created by AI. Sometimes AI does give you an indication, right? If the review is way too perfectly written, way too many punctuations, you could say, well, that's an AI-created review. But even then, isn't AI-written review straightaway fake? I use Gemini and ChatGPT to write my notes and stuff like that. So I could use it. Luckily, if it helps me.

I definitely have a better way this one today. Luckily, as in the fraud team and our detection models, we have much more data. So what you got right here is an extra set of signals. You've got the name. You've got the location from where the review is written and whether or not the reviewer has a profile picture.

Again, the question for you is, which is the fake review? Is Review 1 fake, then select Review 1. Is Review 2 fake based on the new data, then select Review 2. All right. I think we're already there. 75% still believes that Review 2 is fake. Now again, I'm not going to reveal it now, but I do want to say something.

Because what I see here is Sofia M., almost sounds like a criminal, Emily Brown and I see a profile picture. Now, it could be that this person, Sofia M., is actually from Spain and this is her favorite spot. At Trustpilot, you are free to select what kind of profile picture you want. Of course, it can't be harmful and illegal, but there is some freedom. So it could be that you selected this because this is your favorite place.

So let's move on. Third set of data points. What you see here is the reviewer history and some additional insights about the location. Again the question for you, Review 1 or Review 2, which one is fake? If you believe it's Review 1, then click on Review 1. If you believe it's Review 2, click on Review 2.

History tells a lot. But now actually what we see is a little sneak peek into our detection ability. What you see onscreen here is that Reviewer 1 is connected to 5 other reviewers. And how do we know that? It's because we use fingerprints. So the device settings, the device fingerprints, allows us to say Reviewer 1 is actually connected to Reviewer 2, 3, 4, 5.

It could be that they all used the same device. I won't go too much into detail about that. What we also see is that, hey, they also reviewed -- all 5 reviewed the same business. While fake -- well, Review 2 is actually just one user that is connected to another user.

But if we look at what e-mail address that is, and we do see that they have the same family. Now does this change your mind? So again, which review is fake? Is it Review 1? Or is it Review 2? And what we see right now is that we went from 75% believes it was Review 2 that was fake, we see it hop to the other side. We actually see that close to 80%, 77%, 78% says that Review 1 is fake.

All right. Let's give a last shot at it. We've got to push quite hard on this one. So another piece, another data signal. What we know based on our detection and the way we look at data is that actually the Reviewer 1 was written from a disposable e-mail. That means the disposable e-mail is just a onetime use e-mail address. It doesn't really exist.

And if you look at how it is created, it's totally random where actually Review 2 used a widely recognized hotmail.com and a very normal e-mail address. Last time, which one is the fake? Fake is Review 1. And what I have tried to show you in a very quickly -- in a very quick way is actually what I'm trying to show you on the next slide.

When people use our platform, they might see around 5 data points. They might see the name. They might see the content. They might see the location of the review. Those are the data points that you have if you look at Trustpilot, as anyone look at Trustpilot.

And what we have actually seen in this trial that we did is that with just those data points, it is super difficult to identify which one was fake. Actually, we all believe that fake -- that Review 2 was fake. This is what Trustpilot looks at. We look at device settings, connectivity, location, steps, e-mail addresses. It even goes as far as that piece of battery on your iPhone.

It's that to say is for all reviews they present, that's a very typical signal for us to say, why would there be so many reviews when it is exactly the same amount of battery. So what I'm trying to say is spotting fake reviews is really difficult. If you just do it with the top of the iceberg, the review content and maybe those other patterns that I've mentioned before, you won't succeed. But that's why we use all the data points that are sitting below the surface. That's why we also use the technology to do this at scale. That is how we are able to detect the fake reviews. And that should be my part.

Then I'm going to hand over to Sona. Well, Sona is not here, but we made a good recording, I believe. And she's going to present more about our AI and our automated detection.

Good afternoon. Fraud isn't a static target. It evolves. There are 4 key fraud behaviors that affect how we think about technology. First, the way fraud looks changes from week to week. So our models need to retrain weekly to keep pace. This runs as an automated process with a decision gate that releases new models only if they perform better on new data.

Second, fraud adapts to our defenses. As soon as we close one attack vector, the fraudsters start engineering around it. We compensate with rapid tactical rule releases, surgical and fast to deploy, sometimes as fast as the same day we spot the fraud pattern.

Third, fraud targets the weakest signal. Whichever area of detection hasn't been reinforced recently becomes the new attack vector. So each fraud test requires its own dedicated defense. But none of these defenses stand alone. They feed each other, share signals and learn from each other. The whole system evolves as a single defense net. That's our way to stay ahead of the adverversory.

Fourth, fraud spread through networks. Bad actors share tactics, tooling and playbooks. So we built early threat detection that flags emerging coordinated patterns before they scale. What you're seeing here on the left is a real time lapse of fraud network evolution from the past year.

From week to week, you can see the fraud pattern rapidly developing before the defense kicks in to contain it. Every new review decomposes into multiple feature types: text content, user history, review timeline, device AD are just some examples of the types of data we assess for each review.

Those features feed our processors, content analyzers, graph builders, metadata extractors, which then feed various types of classifiers running in parallel. One example is our graph neural network, a type of deep learning model known as a graph attention network, where each node importance is calculated using a weighting known as attention, the same key architectural component as the GPT models.

These are great at learning to recognize a wide range of suspicious patterns expressed in relationships between businesses and consumers. In addition, we have models that we refer to as big impulsive trees that use decision trees to handle complex feature interactions and can, therefore, extract meaning from a wide variety of individual fraud signals, combining them to get high confidence decisions.

Other models we use include large language models, sequence models and hand-authored SQL rules. Each classified cast an independent vote. If any of them flags a review, it escalates. A trust check applies a last chance override for reviewers with strong legitimate history and the outcome published or filtered.

An important part of the system that rarely gets the spotlight is what happens after the outcome. Every decision feeds into a pool for audit samples. We check those samples regularly for false positives and false negatives. Then many of these reviews feed into the retraining pool. The models you saw at the top of the screen learn from the system's mistakes and successes every cycle.

To summarize, there are 7 stages in our fraud detection cycle. Platform monitoring at the top triggers alarms when something looks off. Experts review technical SQL rules are written to patch the gap immediately. That models retain automatically, picking up on what the rules are doing to learn new patterns and apply them in a broader context.

Next step is the engine of the whole system, feature and model R&D. That's where our big detection wins originate. Once we release a new model version, we evaluate and then it's back to monitoring.

A few things to call out. First, we don't only act on new reviews. When we learn a pattern today, we go back to past reviews and reevaluate them against the updated model, which happens regularly as part of our weekly retraining cycle.

Second, our research pipeline runs on 2 tracks. The first track is focused R&D, driven by new patterns we've observed in production. Something new shows up, we built a targeted defense for it. The second track is continuous improvement, staying current with academic research and evaluating new architectures.

Many of those research projects don't stay as research, they make it into production. For example, our research project from 2025 on coordinated fraud via sequential pattern sharing in graph. The core outcome of that research project delivered 3x the detection volume of the prior model version.

If we take a step back to look at where we came from and where we are now, it's been 12 years of evolution. We started with juristic in 2014 and built our legacy classifiers, including NLP models a couple of years later. From 2022 onwards, you can see a rapid development in the application of more advanced technology, including the release of our first pretrained transformer-based model in January 2022, senior as the first release of ChatGPT built with the same technology.

The numbers from the past 4 years tell the story, roughly 3.4x the model complexity of where we were in 2022, measured across feature counts, learn parameters, free splits, graph size and roughly 3.5x increase in filtered fraud volume over the same window, showing the effectiveness of technology in action.

A few words on the people behind this. We have 10 specialized degrees on the team, 4 PhDs, 6 masters, every one of them with years of experience in AI and machine learning. As a matter of fact, 62 years of experience total. Our team's objective is not just to keep up with the field, but to be thought leaders that actively contribute to Frontier applied research.

Last year, we presented our Graph Neural classifier work at Machine learning with Europe, a leading conference dedicated to real-world AI applications. We are planning to continue with more conference contributions this year. Which brings us to the question I get asked a lot, doesn't the AI era make all of this easier for fraudsters? And for one specific access, it does. Writing is cheap now. But text was never the strongest fraud signal anyway.

What you're looking at is our actual feature space. These are broader categories that encompass some of the examples you saw in the animation earlier, colored by the signals they represent. The gray cluster labeled content, that's the entire surface area LLMs commoditized, less than 5% of our feature space.

As you saw earlier, we use AI as a tool against fraud, one of many in our toolbox. And we have a research pipeline that continuously evolves our defenses to stay ahead of frauds evolution. So while LLMs are a risk factor, they are not a game changer. We have the tools and the process to adapt just like we do with every other new fraud pattern.

You've seen how our fraud detection technology operates and the outcomes it is able to achieve. Next, Dom will cover regulation.

I'll start by saying thank you, Sona. Well, I'm here to bring the outside world in and talk to you about regulation. So I'll just start whilst hopefully the slide can catch up.

When people hear the word regulation, they often roll their eyes and maybe think of constraints and cost and complexity. But I'm here to help reframe that because for us at Trustpilot, regulation isn't friction. We see it as a competitive advantage. Because at its core, Trustpilot operates in one of the most sensitive spaces on the Internet, trust. And increasingly, what we're seeing is that trust is being chased by regulators as much as it is by consumers.

And so today, I'll cover 3 things: firstly, how the regulatory landscape is evolving; second, how we work to actively shape that environment; and finally, how that positions us for growth. And I'll click.

So I just want to start by zooming out and just taking a look at the bigger picture. This slide is just a snapshot of the regulatory environment we're navigating at the moment. It's a pretty complex web of laws across the U.K., the EU and the U.S. You might not be able to see it very clearly on the slide, but much of this legislation has been enacted in recent years. There's been a marked uptick in regulatory activity. And what's also changed isn't just the volume of regulation, it's the direction of travel. And I'll come on to this shortly.

What you also don't see on screen is the movement within the EU at a member state level. Many of you would have heard of the EU's flagship legislation, the Digital Services Act. This act was intended to create a unified single market for digital services. But despite those top-level EU rules, individual nations seem to be going their own way. And what we're seeing in our space is Italy and Spain have just implemented their own specific national laws to deal with reviews.

And these aren't just minor tweaks. They're raising the bar quite significantly. They're doing things like introducing mandatory ID verification for reviewers. They're also introducing really strict time limits for when reviews can be posted. We know that other countries are watching very closely with what Italy and Spain are doing, and there's a really high likelihood of a fast follow.

And so, for us and for many others, this presents quite a unique and significant challenge because a fragmented regulatory map is harder for everyone to navigate. And it reinforces to us why we can't afford to be passive here. We have to be in these rooms and we have to be in the center of these conversations. It's our job to help policymakers understand how to protect consumers without breaking the very platforms that they rely on, and that's something my team is heavily focused on.

Okay. We've had a quick look at the global horizon, but there are some key themes coming out that I'd like to share with you. But before I get there, I also just want to draw your attention to a quieter, more subtle, but very significant shift that we've seen.

With the new regulation that's come into play, we've seen a move from platforms being seen and accepted as passive hosts to platforms now being expected to be responsible actors. And so, the days of sitting on the sidelines and claiming neutrality are over.

Taking it back to this, we can distill the themes that are relevant to Trustpilot from a regulatory perspective into 3 key buckets: fake review detection, accountability and enforcement at scale and transparency. Taking each of those in turn.

When it comes to fake review detection, it's no longer enough to react to reports. We're required to proactively find fake reviews, manipulation and business misuse, and take action before it reaches consumers.

On accountability and enforcement at scale, there's a massive drive for speed and consistency. Regulators expect that. Best efforts aren't sufficient anymore. Regulators want to see platforms taking immediate action across the board.

And in terms of transparency, regulators are expecting more of a glass box approach. So we have to be able to prove with data that our processes are working, how they work and the actions that we've taken.

Now it might be easy to conclude that regulation is an impending threat to growth and innovation. And for some, it might well be, -- but for us at Trustpilot, we see this as an opportunity because for years, we spent time building the technology that Sona has presented and the governance structures to meet these standards. And the shift in regulation validates our model and our approach.

As you can see on the slide here and as my colleagues have already explained and spoken about today, our model is built around open access. We're an open platform. Active moderation, we have that in place. And we're transparent. We're open about our processes, our rules, and we showcase the actions we take with tailored trust signals.

As I mentioned before, at Trustpilot, we don't just respond to regulation. We help to shape it. We engage directly with regulators, policymakers and industry bodies across all of our key markets.

What does that include? That includes work like providing input on draft regulation and legislation, sharing data and insights on review integrity and helping to define what good looks like in practice. And this really matters because the regulation, although there's lots of it already, is still being written. And our goal is really simple. We want to raise standards across the industry in a way that rewards companies who are already doing the right thing.

Another way in which we do that is through collaboration. We're a founding member of the coalition for trusted reviews. It's a space that brings together platforms, businesses and stakeholders to share information and define shared practices. And that's really powerful for us because trust is the foundation of our entire ecosystem.

If consumers feel the foundation of trust is cracking, everyone loses. But when we collectively raise the bar and standards of integrity increase across the board, consumers can search and shop with more confidence. Businesses can see higher returns and platforms like ours become essential infrastructure for global commerce.

To protect this ecosystem, we've been encouraging regulators to look beyond just platforms and rules and focus on broader education because for regulation to truly move the needle, businesses and consumers alike need to be clear on their rights and responsibilities because impact only happens when everyone knows the rules of the road.

And with that, I'm going to bring this to life with a quick quiz. I don't have a slide though, but the quiz is called, is it legal? And today, when people ask questions like, can I choose who to send invites to? Can I invite my mom to leave a review? The answer to that is increasingly complex. So we're going old school which shows hands. The click will let me. Right. We're just doing 3 scenarios.

Scenario 1, the incentive. You're a business owner. You want more reviews. So you offer a 10% discount or a coffee voucher for anyone who leaves a review. It doesn't matter to you if it's a good or bad review. You just want feedback. On a quick show of hands for yes, is this legal?

Okay. Well, it's very suspenseful. There we go. The answer is, it depends on where you're standing. So in the U.K. and U.S., and the EU, it is legal provided you disclose that an incentive was offered. But in the U.S., the FTC has taken a much firmer stance and it's a hard no. And actually, in the U.S., the FTC have mandated that $53,000 fine penalty can be applied per violation. At Trustpilot, our guidelines go further than some of these laws. We don't allow businesses to incentivize reviews.

All right. Scenario 2, inviting only happy customers. You're smarter than average, that's a given. You only send your Trustpilot invite link to customers who gave you a thumbs up in your private survey. If they were unhappy, you routed them to your private customer service team instead. You're just managing your reputation, right?

Quick show of hands for yes, is this legal? On the fence, okay. It is not legal. This is strictly prohibited. So this is called cherry-picking and it's illegal everywhere, and the penalties are actually much more severe than for the violation I just mentioned. So we're looking at in the U.K. under the DMCCA, fines of up to 10% of global annual turnover, 4% in the EU and the $53,000 fine in the U.S.

Cherry-picking is an industry-wide problem. And as I said a minute ago, the consequences can be really severe here. At Trustpilot, we use a combination of technology, people and community, as you've heard, to try and find the businesses that are only inviting those satisfied customers. And when we find them, we apply things like consumer warnings like the one you saw earlier. And this is an area where we think greater regulatory education is needed, and we're very proud to be transparent in the actions that we take here.

Final scenario, calling this one the product pivot. So you sold a great pair of socks and you managed to get 500 5-star reviews for them. But now you're launching high-end vacuum cleaner. To give it a boost, you move those 500 reviews over to the vacuum cleaner page. It's the same brand. Quick show of hands for yes, is this legal?

Solid, thank you. Let's see. It is not. So this is called review hijacking or catalog abuse. So it's a strictly prohibited misleading practice across the board. We've got systems which are designed exactly to detect this sort of behavior if the content doesn't match the product category.

That was just a quick counter through of some of the scenarios and issues that we're seeing coming through the regulatory landscape. But what does this all mean? I can distill this down to 3 key takeaways.

Firstly, the barriers to entry are rising. I think the days of anyone starting a review site in their garage are long gone. To operate a credible platform today, you need to have sophisticated AI moderation. We need robust governance frameworks and you need a direct seat at the regulatory table. And I think in a high regulation world, scale isn't just an advantage, it's a requirement. And this shift naturally favors the trusted, more established players.

Secondly, trusted value. As standards rise, businesses increasingly value platforms where consumers believe in the content and external stakeholders have confidence in the processes. That strengthens our value proposition.

And thirdly, reducing risk. We're working to derisk our future. By choosing to lead the conversation with governments and agencies early in our key markets, we're moving from being regulated to being the benchmark. And this proactive engagement removes the fear of the unknown. We don't want to wait for the rules to change. We want to help write them.

So in short, we're working to turn the tide of regulation into a competitive advantage for Trustpilot.

And with that, I'm delighted to lead you all on to a 20-minute break.

[Break]

Good afternoon. I'm Hanno Damm, I'm the CFO, and I have the great pleasure to have a conversation without slides with Ciaran, our Chief Product Officer, who's been in the business for a little over a year now. It's been a fantastic impact. And we're going to talk about the -- why openness, and we've talked a lot about why the platform is open and why that wins in the age of AI.

But Ciaran, before we start with that, let's take a step back. We've seen the loops, the flywheels. Explain to us how you, as the CPO, look at these flywheels in the business.

Yes. Great. Thanks, Hanno. And just to set some context because the title kind of reveals all. It's just about that openness. So much of AI and large language models today are based on open content. And so, it's just important to keep that context as we think about some of the things that we're going to talk about.

In relation to the 2 loops, I think hopefully some people in the room have used Trustpilot in their lives and kind of have a developing opinion of when you use it. A lot of it has to do with when you're kind of in a buying journey and you kind of get to that close to maybe making the purchase of a service of a product. And you might just want to triple check your facts and go on.

Trustpilot looks at what other customers, consumers or users of a particular brand have used. And then you might find yourself either leaving a review. Now personally before I joined Trustpilot, I won't mention the name of the brand. It is a wearable device, of which there are many. But it was that kind of notion of having a terrible experience that Trustpilot was like a last port of restitution, and you can leave a review, comment on the particular brand. And hopefully, somebody after many, many e-mails that you've tried, will see the Trustpilot review and respond back.

And that is a journey that lots of people go on is that when you're just at your width end, Trustpilot is a place that you can go and kind of leave a review and maybe that particular company will respond.

But there is another journey, and that's the one where you find either small businesses or small companies, you've had a great experience. You just want to help them and you want to leave a kind of a complementary review and lots and lots of people do that.

And we know that helps, right? Other consumers, other customers go to Trustpilot, they see nice things that people say about brands and indeed the bad things. But they kind of match that together and they kind of see that. And that's that first loop.

The second loop then is businesses inviting their customers to leave reviews. And what that generates for them is lots of insights. They read the reviews, they reply to the reviews. It may make them kind of think about making some changes within their business. They go through that particular loop, they measure and see whether or not that has been successful.

And then what they do is they'll tend to take some of those comments that they've got from the customers, and they will showcase them is the phrase that we use, which is putting things into their marketing materials, into the website. In some cases, I've seen customers in their follow-up e-mails to existing customers of theirs, just as a reminder, maybe they're in a subscription and you say, "Hey, just to let you know, people who have also purchased from us have said these following things.

And sometimes it's just during that renewal phase, it reminds people why they should continue to subscribe." All of that basically is the second loop. And that grows and grows and grows. It's a continuous evolution. And it feeds in that second flywheel where more reviews on the platform due to invitations, more insights drives more marketing and the thing basically catches fire from there.

So you speak to a lot of customers and maybe walk the audience through what are you learning from them and how they use Trustpilot and what that evolution is in that customer value proposition.

So over the last year, kind of joined the business, I was just curious. I was deeply curious as a product person as to why somebody buys the product and why would simply buy into the proposition. I've met some big, big enterprises trying to understand what they do. And what's been interesting is the journey is so consistent and so similar.

So I've met an amazing solar panel company in Spain. They installed solar panels. It's -- Spain still has sun, lots of it. They have lots of solar panels. But in the journey, it was like, so why did you buy Trustpilot? And the story was, well, we started with the score and so many companies will say that. And then it became what next? Well, we started to read the reviews and you go funny that.

And it's so funny. They actually -- you'll consistently hear that kind of statement of we started to read the reviews. And then we thought, hey, there's something is wrong in our business, we should go fix those things, which created an okay or a goal for one of their VPs. Then that resulted in making some changes and fixes. And then the curiosity of the executive team was, did we fix that? Are we still seeing that feedback in our reviews? And the answer was no, we had fixed that.

Which then begets to kind of a reporting rhythm where their executive team, their leadership team is meeting some cases, weekly, some cases monthly, to hear what's the latest trend? What are the latest things we're seeing in the reviews. And at the end of that little [ spiel ] that I got from this particular CEO was like, what's the impact on that in the business?

He said, well, there's 2 things. One is every single person on my team, that was their particular team on the call, their annual bonus gets paid out based on 5 things. And one of those 5 things was Trustpilot. So I was like, wow, it's pretty meaningful.

And so when you hear that you're like, wow. It's like, well, what's it all for, because we want to be the best customer experience in class for solar panels in Spain. I was like, oh, so what do you need from me? It's like, what we've learned is the score of our brand isn't necessarily the thing that matters.

What matters is the plumbers and the suppliers and the installers of solar panels, which has got me thinking like, you got this amazing technological product, and it's down to the person who goes and installs the heat pump or the solar panel at that business. And if they screw that up, what happens is you blame the product brand, which is that CEO. He's like, that's our problem.

So I need the score of every one of my suppliers to then institutionalize such a high quality of installation that my brand does not get negatively impacted. And that story, I think, is universal. I hear that so many times. And of the ones I meet, it's that start with a score, but it evolves into this incredible rich feedback loop of continuous improvement of the business.

Yes. And there's a couple of interesting things. We -- the company bonus component, we obviously live this at Trustpilot as well, as you well know. And we also read our own reviews. And if you read our own reviews, I think the 2 points of contention that we often see are on the consumer side, they're unhappy if we remove a review from them and they had a genuine experience, the false positives we talked about before. And then on the business side, they're unhappy if we don't remove a review that they would have liked to have removed. So how do you, as a product person, think about that tension?

It's very real. Like I think the team obviously brought everybody through some of the key aspects of what we do. And it's very true to heart of the principles that we operate under like things are fair, they're neutral, open, relevant and transparent, and we live and breathe that every day. And we literally make no apologies for some of the decisions that we make on leaving a review on the platform because we believe it is a real review or we basically see no reason why it should be taken down.

And then equally, if somebody is like thinking like, hey, well, this review basically is not representative of my business, but we leave it up. And that process is the one we observe, and it is fair and it is unbiased, and we continuously refine the rules in terms of how we do it. And you heard that from the team in terms of moderation.

I think one thing, again, just back to some of the customer stories is maybe it's not to dispel a myth, it's just to kind of represent what can happen. So last week, I met with an online gardening company in the U.K. Those basically who love gardening, and I do, like Monty Don is a personal kind of -- I think I was going to say Monty Don is my favorite Britain. Is that okay to say in Britain, but he is. I love Monty Don. I think he's just an amazing -- he walks like -- back to the story.

But I met this garden online company. And what was interesting is when they get a 1-star review, they walk that down into the shop floor of the business. Now it's not there as a stick to beat people with, just to kind of mislead you, but they wanted to say is like, hey, we had this review, and it was about moldy seeds. So why were we shipping moldy seeds?

Now if you are a gardener and you've seen what's been happening in the otherwise known as the British winter and spring, it's the rainy season now recalled. It has been unseasonably wet and damp. And the -- one of the team members said, we received a bunch of seeds in from one of our Dutch suppliers. And they noticed they were subtly damped.

And even though they were in the date for the seeds being still valid, there was a concern, should we ship these out? And of course, they did. A couple of days or a couple of weeks later when people received those seeds, they are moldy and those moldy seeds you cannot use.

And it was just that decision-making that said, what do we want to do the next time that happens? And it was empowering the team to go, you shouldn't have sent them. And that's so powerful. And that's what I mean by that continuous loop. You get that with Trustpilot.

And similarly, just to kind of close off the good news story, when somebody's name is used as a such and such fixed my issue, they also walked down to the team and go, "Hey, Hanno, somebody mentioned you in a review, and they all give each other a little clap, right? So I thought it's a nice way of thinking about what happens.

That's great. And so let's pivot a little bit to AI now and why openness becomes even more relevant in the age of AI and how these models actually digest our data.

So as we started, we said it was about so much about open content. There's kind of 3 things, we here call it the 3 oars, but just to kind of put it in context that so much of large language models and AI is based on relevant content. So I'm sure you've all done it. You type into OpenAI, Anthropic or Gemini, whatever your tool of choice is, a query and the content that comes back, it's relevant to that query. That's what large language models have revolutionized.

What's also interesting is the more recency of the content. Large language models are tuned to try to find new content, dynamic content, the latest content all the time. You see that as part of the way the strategy works. So you've got recency as kind of a key driver for that.

And the last one then is the ranking. So AI large language models use AI search, which is built on the 25 years of SEO ranking. They actually use this as part of the way they find information. And therefore, the ranking and domain authority of the content of the site becomes really, really crucial.

So in this new AI world, Trustpilot's content is at 94 as a domain authority score. That's off the charts. That is 0.01% of websites in the world are in the 90-plus number. So we're up there with Google and YouTube and Wikipedia and these types of websites as a place where large language models consume the information and trust that information more because it has been connected by so many more websites.

That's kind of what's going on. So therefore, the content, the citations, the mentions of the information on Trustpilot is just like large language models are hungry for that information. And therefore, when you're typing your prompt, as long as it's relevant and it's recent and it has a high domain ranking, it's more likely that's how you get citations and mentions of the content.

And so we -- obviously, we show up in all in these answers now more and more. And all this is underpinned by this massive repository of reviews that we've accumulated over the 18 years plus. And how do you think about this as an asset? And what are you going to do with this as a product?

It's an incredible database. And it's -- when you listen to the team and some of the things they talked about, when I first met Sona and the team, it was kind of funny they were like you know Sam Altman. I said I don't know him personally, I wish I did.

But when you think about the history of large language models and transformer models, the AI team at Trustpilot was doing the same work. They were building our own AI language models 6, 7, 8 years ago. At the time, it started off with blasphemy laws and these types of things that were kind of key to do with some European legislation. But now you fast forward where it is today, every single business in the world is using transformer logic, which we know is large language models.

So you have this incredible database, but the curation of that is amazingly complex. So if I was to say the word to you something is too expensive, you might say, well, that's time to value or you might say it's priced or cost too much. There are all these kind of like synonyms of words that we kind of take for granted as humans. And then when you factor in that's just in English and you throw in Dutch, as Thomas mentioned, you have German, I have Irish somewhat. And you mash all that together, that database is incredibly valuable.

And it can be used for peer reviews, marketing research, different ways for businesses to understand what's going on. But the ability for it to zero in on what's broken in your business, it's actually harder than you think because people use different ways and different nouns and different words to describe the same thing. And we've effectively been curating that for such a long time. We have this thing that you can now embed into a business.

Yes. And I remember a story where we helped a company that was sort of doing marketing as a vegan -- for vegan products. And in the review context, all the reviewers were talking about meatless or meat alternative and no one used the word vegan. And so I think that's actually another real valuable insight.

Now this is about trust and not about the product, but it's in the have you. We may do a more extensive product teaching at some point. But can you give us a little teaser of how you're thinking about evolving the product in the age of AI in particular?

I think where we'd like to take things, we've seen some of our customers do this with Trustpilot is how do you take that database and how do you embed that into every single action every day. And those examples where we talk about somebody walking down into the building and saying, who got this one star review, can you tell me more about that? There are another group of customers that have automated that thing to an nth degree, and it's like eye wateringly good.

So I won't exactly talk about what we're going to do on the product side. I just think the opportunity that you could just imagine that if every single day, every decision you make that happens with the customers, whether that's delivering things, changing the offerings, even when you launch a product, you could come back and go, how did we go with the launching of that new product? That was supposed to fix the solar panel installations. Did it work?

When you're dealing with thousands and thousands of businesses and it's over a long period of time, how do you put that together? And I'm not going to say anymore, but that's what we're going to come back and talk about.

And how do you think about the world of agentic commerce that's upon us at some point?

We are working with some of the largest marketplaces. So, I think when I look back at like 20 years ago, I remember the time that Netscape was still a company. And if you remember the time you took your credit card and you put it into an SSL, because this is all the terminology we used to back then, but you put it into a form and you click the button and you hope somebody didn't rip you off, the experience you have today was defined by Amazon's one-click patent back in 2002.

And we just take it for granted that you go on to the shop app, you click your credit cards is there and there's no questions asked. The new evolution of where we're going to go from the one click is what that's called to the buy for me, that requires a very tight loop where the vendor has some extra third-party signal that they are legitimate and that legitimacy is based on customer reviews. It's based on a whole bunch of other factors.

When you start to unpick how the Internet works today, you need to basically rewire for this new world where agents are buying things -- and therefore, where else would you go other than to ask the customer base of that brand, hey, is that brand legitimate? What's the service like? Do people get things delivered on time? Do the moldy seeds of Britain gate get restored, the big problems of today? That's what's so interesting to us, and we're going to be talking a bit more about that the next time about who are those marketplaces and how is Trustpilot embedding into those.

Excellent. Well, that will be fun. We're now going to have Adrian and the rest of the team come up and open the floor up for questions for some Q&A.

All right. Thank you. I think we're going to start in the room, and then we might have some coming in from folks online as well. But I'll just take hands up, please. Yes.

This is Hai From UBS. I have a couple of questions, please. The first one is on -- so you removed 7.8 million reviews last year. But what I'm trying to gauge is where do you see the problem is? Is this kind of a ceiling or the floor of the scale of the problems that you have or you as an industry have? That's #1.

And the second one is on the cost of combating that over time as AI makes it easier to right-click reviews with scale, do you expect costs to ramp up as well as it's a race essentially for you to have better AI to combat?

Sure. So maybe I'll take your first point and then hand to Hanno for the second on costs. So 7.8 million, as we said, was a very substantial increase on the prior year. I think the way to think about that, it was over 77% more than we removed in 2024. So 7.8 million was the total for 2025.

I think the way to think about that is it's a combination of more aggressive bad actors and improved detection on our part. We removed everything that our systems believe is fake. And I think we've given you a pretty detailed account today of how our systems have evolved, how our systems have got better and how we think about the various trade-offs involved, of which there are many.

As I said right at the beginning, I think the best way to think about our effectiveness in doing this is the way people vote with their behavior. Millions more people are using our platform. More and more businesses are using our platform. If you think about the examples that Ciaran and Hanno was sharing earlier, the companies wouldn't be doing the sort of root cause analysis of were the seeds moldy or what words are people using to describe solar vegan food or solar panels or whatever. They wouldn't bother doing that if they didn't fundamentally at some level trust the content that is there.

So I think the most useful way to think about the efficacy of all of this detection activity that we do is are people finding the platform more useful or less useful over time. And all the evidence, as we can see, is that people are finding it more and more useful. And we are getting every year more and more sophisticated in how we deal with the various threats that are out there.

So maybe on costs.

Yes. So I mean, I think both Thomas and Sona illustrated very well how writing is only part of the detection. And so AI writing is actually cheap, I think is what Sona said, but everything else is still expensive. And that's where the vast majority of our fraud detection actually and our fake review detection resides.

And over the years, we'll continue to invest more and more into that. The teams are growing. But with the use of technology and all these sophisticated models that we've developed internally and are continuing to develop, we continue to see that this is not growing faster than revenue. And so we're very much unable to generate operating leverage even in that area.

And AI is helping us certainly deal with a lot of the manual parts of this in the sense that people are -- consumers are flagging reviews and someone has to take another look at this. Someone has to read this now. We can translate it, for example, out of any language into English. And so we only need English-speaking agents to actually then read it because the AI has translated it. And so there are a lot of ways that technology is actually making this more efficient for us.

Any other questions in the room? Yes.

It's Tim Ramskill from Bank of America. A couple of things from me, please. I guess maybe it's a bit tricky, but I thought perhaps if we use the situation of the Italian regulators kind of review into what you guys have been up to. And I thought -- because I felt that the regulatory section, it felt like you got all bases covered off the sort of the 3 kind of key considerations you talked about.

So when we look at that chart of all those different regulatory considerations, are the regulators understanding what they're trying to fix for? Is that -- I mean, again, I appreciate you might recap what you say, but is that part of the challenge? And again, I know you came back very robustly to the Italian regulator and said, look, we don't think there's an issue here. We're going to kind of keep pushing. So maybe just to sort of talk around that regulatory discussion, but if you wish to use that as the example. Second.

Sure. So I think a couple of points on that. First, we share the same fundamental goals as the regulators. As I think Dominique pointed out really well during her presentation, we have been for years calling for the things and agitating for the things that have since become laws in the various jurisdictions where we operate. So we have exactly the same goals as the regulators in terms of consumer fairness, transparent information, all of the principles that we've outlined today are exactly the kinds of principles that you will hear the regulators propounding.

Now as you say, we are contesting rigorously what the AGCM have said and we're appealing the decision. And we believe that when the nature of how we work is really deeply understood, then that process will come to the right conclusion. But I've got nothing more to say about that at this point.

And then my sort of second related question is, again, I think you, in the same section referenced the kind of dynamic around sort of verification before reviews can be left, which does sound like something of an impediment to consumer engagement. So again, just interesting thought on could that likely take hold? Surely that must be a concern of some degree because it will surely slow things down.

Yes. So that's not the way we work at the moment, to be clear. We have the functionality. It's there in the product. Some users verify themselves with that functionality, but it's not something that we are implementing across the different markets where we operate.

And I think that if you look at the trade-off involved, implementing those sorts of barriers would result in fewer people choosing to leave feedback. And we think there's huge value in operating an open customer feedback platform where we keep the barriers as low as is consistent with the sort of work we do that you've seen today to keep the platform trusted such that businesses get as much insight as possible. And we think that's ultimately in everybody's interest to hear from as many consumers as possible.

So that's the way we work across our market. And I think if you look at the key regulators in -- we talk about the European Commission, the CMA in the U.K., the FTC in the U.S., they're very much on the same page on that stuff as well.

And I think just sort of -- because I think you picked up on something that Don had mentioned in her remarks around the pending legislation in some of the markets that are being discussed. I think as Adrian pointed out, we would not be supportive of that. And it's a question whether that's even like sort of compliant with EU Omnibus directives, et cetera. So jury is out there.

Just quite a sort of wide-ranging one really. You guys accept reviews in sort of a very broad range of languages. Just a sort of very open question. How does that complicate some of the models and approaches you use for assessing sort of what content can stay on the platform or not?

I've got this mic. Is it working? Do you hear me? Yes, because I can't hear myself from here. So this is where it comes down to the content piece, right? Like if we look at how do we detect the fake reviews, then actually the content piece is just a very small piece, and that's also what Sona tried to visualize with this around 5% of the data signals. That industry, that's content.

So -- when I look at fabricated reviews, I even don't look at the content. I look at how is this review written and how did it came on to the platform. So we look, for example, at the connectivity proxy usage. We look at what kind of device are you using. So for me, that language piece or a fake review -- fabricated review piece is actually not so important. Of course, you can get insights out of it, and we will use it, but it's absolutely not the main feature. That's why 5% of the content is only there to support the detection.

I think it comes back to that sort of tip of the iceberg concept that we were talking about and how the vast majority of the relevant information lies beneath the surface of that iceberg.

If I can just. Just to continue on that threat. I guess the piece I'm interested is less actually about fake reviews here, more about applying it to the sort of content that you will accept. And so assume that the stuff that comes through is accurate. I'm just really interested to hear about how you go about taking the language issue.

Yes, I can say that. We'll go through it in one of the breakout sessions to talk about how our community play a role here in reporting content as well. But Hanno touched on it earlier on. We actually use AI in our translation tools as well, which is really accurate. We validate a lot of the results as well. We do have certain local language expertise across the teams. So it's kind of high priority for us to make sure that we're making accurate decisions alongside our policies that exist across that match to our guidelines for our reviews.

Ross Broadfoot from RBC. The content piece, as you've said, is small, but it is big for the perception of the platform and certainly for those of us looking on that. Do you or should you have any standards for the text of a review? I can have had a 5-star experience, but not been feeling very articulate that day. My submission may look fake to other people and undermine the credibility of the platform. How is that a line that you tread?

Yes. So we have to tread a fine line here because we don't want to put ourselves in the position of being the kind of editorial standards body for consumers as to what constitutes strong content. But the sort of thing that we do is we -- as we discussed today, we detect what our systems regard as being harmful, illegal content to get the worst stuff off the platform. We have guidelines for reviewers that encourage people to write longer, more informative content. We have a flagging system, both positive and negative. So you can thumbs-up something you find particularly useful or you can flag something that you find concerning that you think shouldn't be on the platform.

But in general, we take an approach whereby we're not putting ourselves in this kind of editorial position. And to be honest, I think that's one of the things that consumers really like and appreciate about the platform that it has that incredible kind of authenticity to it. And what we don't have a Trustpilot, which I think is an important distinction with social media, is the concept to follow us. So if my mom and Elon each write a review on Trustpilot, the system treats them in exactly the same way. And I think that is a really important distinction between the way we work and the way pretty much all of social media works in giving everybody a voice.

I've got 2. And just to continue with this trend. Of the 7.8 million reviews that we've kind of removed, how many of them were, dare I call it, real or genuine? In other words, what is the accuracy of this machine?

Yes. So we think about this trade-off constantly between what we call false positives, which we referred to in the presentation and the opposite, which is this false negatives. And we're constantly trying to widen the space between those things. We do not publish or publicize what ratios we're working towards at any given moment because for obvious reasons, I think that would give a strong signal to people trying to misuse the platform. And it's also -- it would suggest that we think anything is acceptable, which we do not.

So when we detect, we remove and we're very -- as the team have outlined today, we're very sort of decisive in our approach there. But the effect of what we're doing with technology and the advances mean, as Sona said in her presentation, I thought very well, we are going well, well beyond the sort of academic benchmarks that you might see out there. And I come back to the point that the best way to actually judge as an outsider how effectively we're doing this is how useful people are finding the content over time and how authoritative it's regarded by the likes of LLMs.

I've got a second one, but I'll catch Patrick offline.

Yes, we will have breakouts later, of course.

Dan Ridsdale from Edison. On the subject of regulation, if the regulatory environment is potentially getting more fragmented and reviews are getting more regulated, I guess first question is how much you already customize your platform for individual geographies? And secondly, if that fragmentation starts to get more significant, how well placed you are to continue with that adaptation, for example, if the U.S. does something different from Europe to Spain, Italy and so on?

Sure. Do you want to have a stab at that?

Yes, absolutely. In years gone by, we did have a one-size-fits-all approach, and it works. But as you say, there is fragmentation afoot. So if Spain is looking or has enacted, Italy is looking to enact. For us, it comes down to a cost-benefit trade-off, as Adrian said before, we're moving in the same -- we want the same things with the regulators. So if there is something in an upcoming piece of legislation in Italy that has the potential to have a positive impact across, we take everything on a case-by-case basis and see if it works with the product and we take a bespoke approach.

And generally, I would say we look for our product and the way we work to run well ahead of regulation. As we said earlier, we've got aspects of how we work that we have been advocates of long before it actually got encoded in regulation. And I think a really important point to bring out is that the conversations we have with businesses are often along the lines of us educating them about the regulatory framework that they operate in.

Back in the day, 5 years ago, it would have been us saying, look, here are the Trustpilot principles that we expect you to live by. Now we're not just saying here are the Trustpilot principles, we're also saying these are actually the laws of the land and you need to live by them, which is very helpful to us in those conversations with businesses and generally in keeping the platform trusted.

We've upped a quota.

One for Ciaran, and I might be jumping the gun a little bit, so apologies if I am. You touched on agentic commerce. I think one of the ways to think about how that might evolve is to look at the parallels with when e-commerce started. And as we know today, e-commerce is varying levels of popularity in different verticals, et cetera. I'm just interested, is there anything you can share as to where you think agentic commerce might go and where -- yes, I'll leave it there actually.

Yes. Well, I think if you look at amazon.com today, you can find the buy button in certain geographies already. I think what's interesting is in that evolution like the Netscape evolution to the buy -- the one click, the Ts and Cs were critical, right? So if you look at Amazon Prime, I'm sure many people in the room use it, the ability to return something after 30 days and not cost you a penny was like that was revolutionary. Because that notion of I can buy something online or somebody in my family can buy something online, and I have the ability to return the goods. One of the things that's problematic with agentic e-commerce is that that's just not going to cut it.

So if you imagine some luxury cruise that you want to go on and you want to find the best price, let's imagine it's GBP 20,000. It's a pretty expensive thing. You want to bring the family. What you want to do is find the best discount online, and there's probably 1 or 2 cruise liners that you want to be that one, not the other one. And you could imagine agentic e-commerce pretty straightforward solving that particular challenge.

Now the question is, did it buy for you and after the fact to come back and say, I bought the luxury cruise. And it could do. But the question you have is if it was like an airline today, take any one of the airlines that you know and love, you wouldn't get your money back. There would be no refund.

And without unpicking those kind of today blockers, agentic would fall apart, right? Because then you'd say, I don't trust it anymore because it hasn't got the right for refund. Those things have to be changed. And my expectations just in the conversations we have with Google and Shopify and some of these larger marketplaces is they have to solve those problems. So where does it start? It starts with the merchant sign-up process.

So when the merchants sign up to a large marketplace, they're going to have to adopt those Ts and Cs, right to refund, all these types of things. And they're going to have to build their, we would call it reputation, but they're going to have to build in a signal that says, my business is legitimate and it has a high quality of service. And that could be down to working with Stripe or some of the payment providers because they see the credit card transactions, which is really interesting. And also customer sentiment and feedback about the service.

And for things like luxury liners like $20,000 payments, that's where it really has to work. Buying a coffee is trivial, but spending $5,000, $10,000, $20,000 in a liner, a pension fund, an investment fund, you start to get to very interesting territory, and those are complicated purchases. Therefore, you're going to have to unpick the Ts and Cs for this whole thing to evolve. So that's where I think it has to go.

I think agentic e-commerce online purchasing, the Temus, the Sheins, all those brands, I think they will solve it pretty quickly. But the -- is the customer satisfied and happy piece is one thing we think is really interesting to that loop.

It's Thomas Brown from Premier Miton. Does Trustpilot have regulatory risk in the event of bad action on the part of your customers?

Can you elaborate?

So supposing one of your customers is cherry-picking, is that then liable for a fine or you for it happening on your platform?

Well, it's a good question. And as I said, it kind of depends where you are. But yes, it is a shared responsibility in many ways. And so for us, in the instance of cherry picking, as I mentioned, it's an off-platform behavior. So from our perspective, our focus is on ensuring that we're really clearing business expectations. We're communicating clearly through our terms and guidelines. And when we're selling our product, we're setting out those expectations too. And we have the systems in place to detect that and feedback.

So, yes, but for cherry-picking specifically, the fine as drafted in the legislation at the moment applies to the business, but there will be -- there is an expectation on platforms to be doing work there as well.

And then with respect to your comments about it's not enough to be passive anymore. I imagine there are some other review aggregators who are pretty passive at the moment. Do you -- as you see the wave of regulation, do you see them having to shut down if they can't be bothered to invest in staff because it's a rather modest part of a large business?

I mean, look, I think the overall way we see it is that we're on the same side as regulators. And we've always, as you can see, taken extremely proactive stance to the stuff. And we've -- year-by-year, we are becoming ever more sophisticated at how we set these patents. The fact that we have so much data is what enables us to be very, very good at that.

And I would say, yes, it will be extremely challenging and wrong, frankly, to be a passive actor in the face of all of that because ultimately, this is about people and giving them tools, the useful information they can trust. And I think it will be wrong to expose consumers in the way that it would if platforms were completely passive in the face of all that.

Obviously, it's not for me to comment on other businesses, but this is why we take such an active approach because we think it's the right thing to do.

Do we have any questions online?

Yes.

So Jeff Pocket, Peel Hunt has asked, what happens if someone disputes a review taken off the site? What is the process for that? Second, given your product ambitions, is your second product team rightsized for the road map? And thirdly, how do you solve the cherry-picking issue of review invites? Or would it be more about better education so companies self-regulate more?

So maybe on the first and third points, I hand to Maj and then Hanno for the second, if that's all right?

I can take the user appeals piece. When a customer or a user of our platform disputes a decision, that will come through to our content integrity team. We've got a team of internal experts who will assess each case independently against all of our guidelines. We use technology to assist in the decision-making depending on the reasons that it's flagged for. But ultimately, it will be made by an internal expert making an independent verification on the outcome.

I'll pass on to you for that.

So when it comes to the whole invitation approach, that's where there are multiple pillars that are very important. It's about educating the businesses, and that's what we do through our policies and guidelines. There is more that we believe that we can do as an industry in total. We, as Trustpilot and the Coalition for Trusted Reviews, we believe that we want to educate more and want to get authority involved, but also social media platforms, payment providers. Because we do believe that if we stand together in that piece, we can much better protect the consumers against any type of issues, whether it's invitation issues, whether it's fake reviews.

And yes, there was also a part where there is a responsibility for the business, and that's why I personally am very happy for the regulatory input because it does make now very clear that in some countries, it is illegal. So that clarity is not only given now by us, but also by the authorities.

And for the investment question, I think if you look at our capital allocation framework, the first priority is always to reinvest into the business in the right amount. And fortunately, we are blessed with the business with very high gross margin. So the incremental revenue that we're generating every year by the sort of mid-teens plus revenue growth rates that we're delivering consistently drops through at a higher rate, and that gives us quite a bit of room to continue to invest into the business.

Now on top of that, I think we're all extremely excited with the efficiency gains and productivity gains we're seeing from AI tools such as Claude, Anthropic, et cetera, that the engineering and product teams are using. And so I think not only me, but also Dave and Kieran say that we're continuing to invest into the business in an appropriate fashion to be able to deliver that ambitious product innovation agenda.

And I've got one from George O'Connor who is asking whether you can learn from how AI makes recommendations. So for example, when booking a holiday and use that understanding to figure out how AI could produce reviews that people trust.

I can pick that one. So one, our TrustedTech team and my thoughts on what we do is we have like research. And that means that we -- every month, depending on what kind of timeline we put to it, 4 weeks, 6 weeks, sometimes 8 weeks, we do research. And those researchers are exactly the ones that we use to then see, okay, how is the latest technology being used and what kind of information can we get out of that. And that means also looking into how our recommendations done and how can we use those kind of things in our benefit.

That have also led -- if you looked at the timeline and the growth of our technology and how effective we are, it is because we are using the technology and getting the learnings out from it, whether it's from new technology, whether it's from the things that we learned through our own analysis.

Okay. I think that's it for questions. So just to say a couple of things before we go into the breakouts. So look, I opened the afternoon by talking about how trust is the most important thing in business. With the rise in AI -- if you can go to the slide, please, sorry.

With the rise of AI, deciding which businesses to trust becomes challenging, and we've outlined today how we have a critical role to play. Our platform is becoming the critical trust signal for the age of AI.

Just to summarize what you've seen over the last few hours, you've heard Shazadi and the team describe a governance model where commercial relationships are independent from content decisions, not as a policy decision, but as an architectural fact built into the way the business operates. You listened to Maj, Thomas and Sona take you inside the detection system that combines this graph network of millions of nodes, weekly model retraining, human experts who are actively infiltrating fraudsters operations and removing millions of fraudulent reviews every year.

And of course, you've had a chance to see for yourself how hard it can be sometimes to determine at face value, whether a review is genuine and how we actually identify it through device fingerprinting, IP clustering and account behavior. And then we heard Domonique talking about how regulation can be a competitive advantage and our role in shaping it.

Hanno and Ciaran may clear the case for openness, talking about how many of our reviews come from people who were not invited because it's an open platform that businesses don't ultimately control. And because of that, no closed system can really produce the insights that this open system provides.

Every product we build is derived from those 2 feedback loops, staying honest. And all I want to leave you with today is the simple idea that all of this compounds, it gets bigger, it gets better as we get bigger. More reviews make the signal stronger. Every review process is a data point that sharpens detection, improves our models and makes the next fraud attempts harder to land. Being open makes the signal more valuable.

The choices we've made to let anyone publish a review, to publish our methodology, uphold 1-star reviews even when they're challenged by our largest paying customers is precisely what makes the platform trusted and the data something everyone can rely on. Consumers, regulators, AI systems and businesses. The transparency is the product, and that trust signal is becoming a layer that commerce runs on.

Infrastructure embedded in the tools and platforms where decisions are already being made, whether that's Google search results, AI answer engines, retailer product pages or comparison sites. Every one of those surfaces is a distribution point to share the genuine experiences that are recorded on Trustpilot.

Okay. And with that, we have 3 different breakouts. So those are going to take place in the room right behind my hand called Cosmos. There's another one on the far side of the building right over there. We'll help you get there, a room called Milky Way. And the third breakout is going to be in here.

Each of you has been assigned by name to one of these 3 breakouts. So business verification in this room, a case study about review sellers over in Milky Way and community reporting that's going to take place in Cosmos, the big room right behind me. So each of you has been assigned to one of those by name. And of course, you will all get to attend all 3. So no one is missing out on anything, and there'll be a chance for further Q&A in the rooms as you go around.

All right. With that, thank you to my colleagues. Thanks all of you for coming along. Cheers.

Hello, and welcome to Edison TV. Today, I'm speaking to Adrian Blair, who is Chief Executive Officer at Trustpilot, a U.K.-listed business who are the world's leading open access customer referral platform. Adrian, thanks for joining me today.

Great to be here.

And starting off by introducing Trustpilot. Obviously, your business has evolved quite a lot over the last few years. Can you just give investors an update on the company's business -- the company's business model and how that's changed?

Of course, yes. So as you said, we're the world's largest open customer feedback platform. What does that mean? It means that you can go on to Trustpilot right now and you can record your experience, you can leave feedback on your experience with any business with whom you've been a customer in some way, shape or form. What that means is we're collecting a huge amount of feedback from a vast range of businesses right across the world. And what we can see, particularly with AI now is there's huge value in that feedback. So businesses learn a huge amount from it. They can use it to grow faster. They can use it to learn lessons and become better businesses. We make money from subscriptions. So businesses pay us for the ability to use our brand in their own advertising or on their own websites. They pay us for analytics tools. They pay us for the ability to operationalize feedback collection and automate feedback collection. So there's a bunch of things they're paying us for. We have a very large consumer audience, but we're not really monetizing the consumer. We're not an advertising business. We're a subscription business, built off a huge amount of proprietary data. And there is more and more of that data coming in every day. We get about 200,000 new bits of feedback are written on Trustpilot every weekday. We got 62 million reviews last year, 20% up year-on-year. So we're getting a huge amount of feedback coming in, and that's really where the value resides.

Interesting. And that's obviously translating to growth. Last year, your bookings grew 18%. You saw margin expansion. Can you talk through the -- I guess, the key levers that are driving that growth?

There's 3 big things I'd highlight that are driving that. The first is I joined Trustpilot in 2023 and said we're going to focus on the high end, so on large enterprise customers. That was the first key driver of that growth. So we saw the number of businesses on Trustpilot paying us more than double the average to more than $20,000 a year, was up by 35% year-on-year. That's now our largest customer segment. And all of the economics -- all of the different economic indicators of that segment are better than lower-end customers. So a lot of our growth is coming from those large enterprise clients. They cover a huge range of industries. So some of the businesses we talked about in our earnings presentation that we won in 2025 include Liberty Mutual, a huge insurance business, Wayfair. We've got technology businesses, travel businesses. So a lot of very large companies around the world realizing they can get closer to their customers using Trustpilot.

Second big thing was AI. So we saw a surge in the citations of Trustpilot reviews on large language models. That's making more and more businesses realize that they want to engage and be very active on our platform. So AI has been a real tailwind behind the business, particularly in the second half of last year, we can really see that accelerating. The number of visits that we're getting on Trustpilot from AI tools was up by something like 15x year-on-year. So a huge increase in AI.

And then the third thing is growth in the U.S. So we're a Danish-founded business, Danish headquartered, but listed on London Stock Exchange. We've always had a huge presence in Europe, but we've been in the U.S. now for the last 10 years or so, and that was our fastest-growing market. It's about 20% of our revenue overall, but it's -- we grew bookings in the U.S. by 21% year-on-year, and we can see a huge surge in interest in our brand because there isn't really anything like Trustpilot in the U.S.

Interesting. And then turning to trust. You're an open platform, you're funded by the corporates. And you've put a lot of work -- you've got a lot of data to show how you are growing that trust and you're working on improving those mechanisms within the business. Yes, can you talk about how you are dealing with and improving your ability to manage trust and improve trust?

Yes. So first of all, I mean, it's absolutely vital to us. Millions of people are trusting Trustpilot to be a place where they can go and read about other people's experiences with businesses and see real stuff, real human experiences, not fake content or the kind of stuff that you might find on social media. So I think we have a very important role to play as a kind of governed system, not controlled by companies. It's ultimately up to consumers, what ends up on Trustpilot, but also not the wild west of social media where anything goes and the loudest voices get the most followers and it can be very hard to know what to believe. So we have a very important role to play. It isn't easy to get it right when you have as much feedback coming in as we do. But we do a huge amount with AI to protect the platform and ensure that the content is trustworthy.

In 2025, we removed 7.8 million fake reviews from Trustpilot. That was something like a 70% increase year-on-year. So we're doing a huge amount to be proactive and protect the platform. We also take businesses off the platform, put warnings on their profiles where we have concerns about the way that businesses are interacting with Trustpilot. So there's a lot that we do. And all of the decisions that we make about this stuff are taken within us, within Trustpilot by the team of our Chief Trust Officer, which is effectively our General Counsel. So someone called Shazadi Stinton, and it's her team that's really governing the platform and making these decisions about what stays up, what gets taken down. So we have a clear kind of church and state separation with the commercial organization.

And turning towards AI, it's quite an astonishing sort of statistic that Trustpilot is the fifth most cited domain in ChatGPT. And obviously, we're seeing a huge change in the way people are accessing information, people are buying. Can you talk about, I guess, your attitude and how you're adapting towards this fundamental change in behaviors?

Absolutely. So we've taken the view that we're an open platform and therefore, the reviews that are on Trustpilot, we want them to be accessible to the leading large language models. So ChatGPT, Gemini, Perplexity, Claude, all of them can access the Trustpilot reviews and display them through their own surfaces, through the large language models. That's what led to -- so we've opened up our reviews for training, for inference by the leading LLMs. And that's what led to a surge, as you say, in the volume of times that Trustpilot is being cited by these tools.

Now the reason we're doing that is because our whole job as a company is to collect feedback from users and then to make it useful and to make it have an impact out there in the world. So we don't want -- if Dan comes and leaves feedback on Trustpilot, we don't want to be shouting into a vacuum. We want that to be consumed and useful in having an impact out there in the world. So the more surfaces we can find to do that, the more ways that technology opens up to do that, the better. And all of that is helping our customers because if you're a business who's really kind of invested time and energy in our platform and generating huge volumes of feedback, you want to see that feedback showing up where users have relevant questions. So all CMOs at the moment and CEOs are thinking how is my business going to show up in ChatGPT. I used to worry about SEO. Now I have to worry about GEO or AEO. Well, Trustpilot is a very important part of the answer to that question. And that's why I think these numbers show.

It's a fascinating statistic. And turning towards, I guess, the other side of the AI deployment argument, you've upgraded your margin guidance. How much of that is due to your ability and your use of AI to improve internal efficiencies?

So we already had a lot of confidence in our ability to improve margins. I think the best evidence of that is the year before I joined Trustpilot 2022, it was a loss-making business. We just reported for 2025 full year 15.6% EBITDA margin. So in the space of 3 years, we've gone from loss-making to plus 15.6%. What we're saying now is we think we can continue that sort of progression and get to 25% by 2028 and 30% by 2030. And we're very confident we can do that because we have a very high-margin business, 83% gross margins. So what we're able to do is kind of as the Americans would say, walk and chew gum at the same time. We can spend more dollars on technology, on sales and marketing as we grow while also delivering for shareholders. We grew free cash flow by 173% year-on-year. We grew EBITDA by 69% year-on-year in 2025. So we've shown we can grow well and become more profitable at the same time.

Now you asked about the impact of AI. That just makes us more confident in our ability to do it. One illustration on our business products, historically, for the last couple of years, we've had one major release window in April for our B2B products. This year, 2026, we're planning to have 2 major release windows, April and September because AI is enabling us to build products, to build software much quicker and better than we ever could before.

And turning towards, I guess, the more nefarious side of AI, it's making fake reviews much more easy to generate. Can you talk about the work you're putting in to make sure that you are supporting the protectors and the genuine reviewers and filtering out the bad actors?

Well, the good thing is AI is helping us to spot patterns and the data that enable us to do that really well. So you're right to say AI makes it easy to write text, right? We all use AI sometimes to write paragraphs or sentences or whatever. AI is great at writing text. But what AI can't do is copy the digital fingerprints of a human being. So when we look at reviews that come into Trustpilot, each one is described by hundreds of different metadata points, things like the IP address, the time of day, how long it took to write, et cetera, et cetera, et cetera. There are literally hundreds of them for every review. And what we do is based on all the history that we've got, we look for suspicious patterns in what's coming in. And we use AI to do that. It turns out it's absolutely fantastic at that. We're always developing our capability, developing new models, improving the models that we've already got. So net-net, I think AI is really helping us to do a better job with this. And that's one of the reasons why we removed so much more content in 2025 than we had in the prior year because we're able to do this more effectively than ever, thanks to AI.

As I said earlier, though, I think the best way to look at this is just to look at in practice are people finding the content useful. We had -- as an example, we had John Roberts from aoworld.com, the founder in a little video in our earnings call, and he was saying he gets his team to look into every review that it's not a 5-star review to understand the underlying causes and get to the reason why so that they can become a better business as a result. He wouldn't have his team doing that if the content was fake. It will be a waste of their time. So he doesn't have to do that. He's doing it because the content is genuinely useful, and that tells me that we're doing something right.

You mentioned AO there. The enterprise -- the growth of your enterprise customers is a significant part of your growth story. And you've now seen for enterprise customers, 43% of bookings come from customers with value of about $20,000. Yes. Can you talk about that? And I guess, the growth of that customer base, but also beyond the growth, how are those customers operationalizing Trustpilot and how sticky is that service towards -- to the enterprise customers?

Yes, totally. I'll give you an example just from the other day. I mean, I was speaking to one of our largest customers in the world, a hugely successful global fashion business. And they were saying to me that until now, they've always asked for NPS feedback from their customers after placing an order. Now they are replacing that with Trustpilot feedback. And the Director of e-commerce was saying to me that the reason they're doing that is that the Trustpilot feedback he knows will help this business to grow because they can use it in their channels to tell people the story of the quality of service they're providing. And it appears, as we've been discussing, it helps them show up in ChatGPT and in other LLMs, whereas the NPS tool that they're replacing it with was invisible to all of that. Like consumers, it doesn't help you build trust with people if people can't read the results. It's totally invisible to LLMs.

So we can see even with large, well-established businesses, this idea of dealing with customer feedback out in the open instead of in the black box is something really powerful. It helps you to drive growth. It helps you to improve customer service because as per my example from AO earlier, you can actually understand the root causes of issues that you wouldn't otherwise have found out about. But what I also see with talking to particularly senior people and other CEOs is they use it to drive accountability through the organization. And if everyone in your company can read what the customers are saying, that's an extremely powerful thing. And it doesn't matter what industry you're in, listening to your customers is a really healthy thing. And I think our product by being public out in the open, it enables businesses to do that in a way that nothing else really does.

And turning towards North America. It's been a key part of your growth strategy for a while. It's your fastest-growing geography. Yes, can you talk about that growth? Are there any particular verticals that are driving that growth? And then looking medium to long term, are there any reasons why your North American margins can't sort of converge on your margins in Europe?

Yes. So you're absolutely right. It's a huge opportunity. If our U.S. business was as big relative to the economy as the U.K. -- as the U.K. already is for Trustpilot, it would already be a $1 billion-plus annual recurring revenue business, right? So it's an absolutely huge opportunity for us in the U.S. There isn't anything else quite like Trustpilot in the U.S. economy astonishingly. So we are going to be -- we already are the Trustpilot in the U.S., and we're going to continue growing fast there. And we can see a lot of evidence now that the brand is becoming better and better known and is really catching on.

You asked about key verticals where we're making progress. So we've always been very successful in high lifetime value services. So I think, for example, of banks, insurance companies, credit card businesses, education businesses, health care businesses, some sectors of tourism and travel, some sectors of retail. So we've got a huge variety of verticals, but there are some particular ones where we've just seen really, really great traction in the U.S. And we can see that all of the indicators that we measure for what we call the growth flywheel, which is businesses invite their customers to write feedback on Trustpilot, then more people learn about Trustpilot, they start using it themselves, then more businesses want to use it. That growth flywheel, all of the indicators we can see show that, that's accelerating in the U.S.

So what's coming up? How -- with all of the innovation that's happening globally, but also within the business, how do you ensure that Trustpilot remains that trusted layer in interactions between customers and corporates?

Well, look, I think if you ask any tech CEO at the moment what's coming up and you're speaking in where are we in April 2026, they're immediately going to talk about AI. So I will follow that pattern. And I would say what's coming up for us? Well, we're doing a huge amount with AI inside our operations, with AI inside our products, but also that, that crucial thing for Trustpilot, making sure that we are being projected out into the AI tools that everybody is now using. And I think it's our success in doing all of those things that you can already see coming through in our numbers in 2025.

And I think from an investor perspective, that's a penny that really dropped when we did our full year results just a couple of weeks ago. I think investors -- what we did was we put in front of people the evidence that Trustpilot is winning as a result of AI. There were a lot of headlines after that saying Trustpilot is an AI winner. I think investors can now really appreciate why that is. So we just are going to continue to double down on that.

Adrian, many thanks for coming in. Fascinating discussion and really a business at a really interesting point in the marketplace right now.

Thank you.

Good morning, everyone. Thank you for joining us today for Trustpilot's Full Year 2025 results, and a warm welcome to everyone joining on the webcast. Hanno and I look forward to walking you through what was an excellent year. Our performance was driven in no small part by the rapid evolution of AI and the increasing value of the human trust signals that Trustpilot collects. I'm looking forward to taking you through some operational highlights before handing over to Hanno for the financials. I'll then come back with a deeper look at strategic progress. Three key takeaways from today's results. First, great execution. We grew bookings 18% in constant currency and scaled highly profitably, driving a 69% increase in adjusted EBITDA and returning $72 million to shareholders through buybacks.

Second, the expanding strategic advantage. The growth flywheel is thriving, fueling a 20% increase in active reviews to 361 million. By opening our proprietary data set to large language models, Trustpilot is now an essential part of the rapidly evolving area of answer engine optimization. Click-throughs from AI search grew nearly 15-fold year-on-year. Large businesses care deeply about their exposure in AI search. So this was one of the key drivers of success in the enterprise segment. Finally, upgraded guidance. 2025 has given concrete evidence that AI is increasing the value of Trustpilot feedback and therefore, widening our competitive moat. This, coupled with the operational efficiencies we can already see AI delivering gives us immense confidence in the future. So today, we're upgrading our medium-term outlook to 25% adjusted EBITDA margin in 2028 and 30% in 2030.

Let's take a look now at operational performance. In essence, Trustpilot products collect customer feedback and turn it into influence over the actions of people and businesses. Because the feedback has influence, people are motivated to write more of it and businesses want to engage with it. In 2025, our users submitted more new reviews to Trustpilot than in the first 12 years of the company combined. Our unique data set expanded to 361 million active reviews, representing a 20% year-on-year increase. The scale of this unreplicable data set and the ability of AI to drive more value from it is a key defensive moat for the business. The healthy growth flywheel drove excellent financial and operational results.

Bookings grew 18% on a constant currency basis with momentum accelerating in the second half and businesses paying us over $20,000 per year, growing 35%. Growth was particularly strong in North America at 21%. Because of the inherent operating leverage in our model, 20% constant currency top line growth resulted in a 69% increase in adjusted EBITDA to $40.7 million, delivering a margin of 15.6%, comfortably ahead already upgraded expectations. Finally, on the AI front, authority continues to compound. In January this year, PromptWatch ranked Trustpilot as the fifth most cited domain globally on ChatGPT. This means ChatGPT is directly referenced or linked to Trustpilot as a source of information when answering a user's prompt. This is driving heightened interest from enterprise clients. Increasingly, our sales teams are leading with AEO as businesses look to secure their reputation in AI models.

Let me touch now on the regions individually. In the U.K., our largest market, we delivered 16% constant currency bookings growth to $116 million. The network effect here is particularly well established, resulting in the highest regional contribution margin at 65%. This shows what the wider group can look like at scale. Because the brand awareness and network effects are so deeply embedded, we were able to drive excellent profitability. As other regions continue to mature, the U.K. proves the long-term margin potential inherent in the model. The strategy to focus on larger enterprise accounts continues to deliver. We welcomed a wide variety of flagship brands in the U.K. in the second half of the year, including Sky, Samsung and United Utilities. The share of ARR from businesses paying over $20,000 rose to 47% by the end of the year.

With market penetration of just 5%, the runway for growth remains significant. Turning next to Europe and the Rest of the World. We delivered strong bookings growth of 20% at constant currency to $113 million. A few notable wins in H2 included Enco, TotalEnergies, Costa Cruises and Canada. The region demonstrates what happens when we focus go-to-market efforts into specific geographies. Growth in both Germany and Italy, our focus markets was well ahead of the regional average. The enterprise segment is particularly strong in Germany, where by the end of the year, businesses paying more than $20,000 accounted for 58% of ARR. This is supporting a robust regional contribution margin of 55%. Moving to North America, which comprised of the U.S. and Canada, it was the fastest-growing region, delivering 21% constant currency bookings growth to $62 million, building on an already strong comparator last year.

We're seeing accelerated enterprise velocity here, helping to drive a 4 percentage point improvement in contribution margin to 38%. Both sides of the flywheel are growing well with reviews submitted in the year rising 27% to 13 million. Brand awareness is also growing rapidly, aided by initiatives like the Inaugural Writer Review Week in October, which delivered our highest ever U.S. traffic week. On the business side, TrustBox impressions rose to 32 billion, up 38% on the year, naturally embedding the brand across the North American digital landscape. We welcomed notable enterprise clients in the second half, including Liberty Mutual and Squarespace. The U.S. remains a substantial opportunity for Trustpilot, and we'll touch on this again later.

I'll now hand over to Hanno to take you through the financials in detail.

Thank you, Adrian, and good morning, everyone, and happy St. Patrick's Day. 2025 was another record year of bookings, margin expansion and exceptional cash conversion. Let's dive straight into the numbers. I'm happy to report that FY '25 bookings grew 18% in constant currency, reaching $291 million. As you can see on the top chart, we've established a clear consistent track record of top line growth and have by now surpassed $300 million in ARR. As shown on the bottom chart, we're scaling profitably, and we have transformed the margin profile of the business. In 2025, we delivered a 4.2 percentage point improvement in adjusted EBITDA margin to a record 15.6%, generating $40.7 million in adjusted EBITDA and $46.6 million in adjusted free cash flow.

Looking at the retention rate in more detail, net dollar retention was 102%. The slight moderation from 103% last year was entirely expected as we annualize the one-off package migration benefit from 2024. We landed major new product features in the year like visitor insights and review follow-ups, while simultaneously driving an improvement in the gross dollar retention rate to a record 87%, up from 85% in the prior year. There are a number of factors driving this improvement. Starting at the top with the growth flywheel and product innovation. As awareness of the Trustpilot brand grows, the value of the businesses being active on the platform also increases. You'll hear this firsthand from a customer later. Net expansion remains strong at 15% and product innovation fuels this. We're increasingly weaving AI and LLM capabilities directly into our tools.

Below that, you see how we operationalize those products through our go-to-market execution and enterprise strategy. We have embedded a globally consistent sales methodology and shifted to a proactive data-driven customer success model, which has reduced churn with early warnings for those customers that are at risk. Moving on. The chart on the left perfectly visualizes the compounding power of our subscription model. The revenue base is incredibly stable with each new cohort stacking on top of the last. Our big growth drivers are customers paying more than $20,000 per year. As we push further into this higher-value segment, we're bringing on larger customers. These customers stay longer and provide resilient compounding value. As you can see on the pie chart, the share of total bookings coming from this segment has grown from 25% of bookings in 2022 to 43% in 2025.

The underlying unit economics of these accounts are excellent. Gross dollar retention for the segment is 93% and net dollar retention is 111% in 2025. Let's take a look at the income statement from a management view down to adjusted EBITDA, excluding stock-based comp and D&A. As always, an IFRS statement and reconciliations can be found in the appendix of this presentation. We delivered revenue of $261.1 million and a gross margin of 82.7%. The improvement in gross margin reflects the normalized sales commission we discussed in the first half as well as operating leverage in our cost structure as the business scales. Network and infrastructure costs grew at a fraction of revenue growth in 2025. This is also the result of active optimization work carried out through 2024 that we fully realized in 2025.

Support costs also fell year-on-year despite higher volume. Sales and marketing remained relatively flat as a percentage of revenue at 27.4%. While we faced an accounting headwind here due to the amortization of capitalized sales commissions, underlying cash efficiency is actually improving. We continue to make conscious deliberate reinvestments into new customer acquisition and improving efficiency every year. Our main focus is funding the enterprise sales motion where the underlying economics are fantastic with customers exhibiting higher retention rates. The group LTV to CAC ratio expanded slightly to 3.6x, up from 3.4x last year. Where we saw the most significant leverage was in technology and content, which dropped from 25.3% to 23.6% of revenue. This was driven by AI-enabled efficiencies across technology, where the teams are using Copilot and now also cloud code in our content integrity teams.

G&A also dropped to 15% of revenue as we maintained disciplined cost control. Finally, I want to highlight the improvement in impairment losses. As a proportion of revenue, they accounted for 0.6%, down from 1.2% in the same period last year. As remarked last year, 2024 was unusually high as we wrote off aged receivables from the COVID period, which had previously been considered recoverable. Overall, we demonstrated strong margin expansion across the entire P&L. This bridge clearly illustrates the mechanics behind the 4.2 percentage point margin expansion. Thanks to our subscription business model, the long-term margin potential of the business is significant, and we delivered leverage across the P&L. This includes CAC, if you normalize for the impact of capitalized sales commissions, which were EUR 2 million in 2025, down from 3 million in 2024.

Product innovation remains a crucial part of the business because it directly drives retention and expansion. As we develop these features for a rapidly growing customer base, it naturally generates structural leverage over time. Trust spend combines elements of all these buckets, except CAC and grew in 2025 as a proportion of OpEx as we double down on our commitment to trust. Specifically, total spend on trust increased substantially as a share of OpEx. For this year, we expect further operating leverage to flow primarily through G&A with some leverage in sales and marketing as these newly acquired enterprise cohorts mature and renew. Moving down the P&L, let me touch briefly on the noncash IFRS stock-based compensation charge of $12.5 million, up from $9.5 million last year. This increase reflects new necessary share awards to attract and retain executive talent and high performers. However, the P&L charge does not tell the full story of shareholder value.

I want to draw your attention to the actual impact on dilution. Grants will be on average 1% dilutive per annum in each 10-year period, as you would expect. Our active share buyback program more than offset shares granted and total diluted share count fell 4% from 450 million to 431 million by year-end. The clearest measure of underlying performance is cash generation. We generated $59.2 million in adjusted operating cash flow, driven by business growth, improved profitability and a benefit from more customers signing up to annual upfront payment. In particular, as we're signing up more enterprise customers, annual prepayments are a standard term, and we estimate the improvement in average prepayment across the portfolio pulled forward almost $10 million in cash flow. This is a structural recurring improvement in the working capital dynamics of the business.

After capitalized development costs and leases, we delivered adjusted free cash flow of $46.6 million or an adjusted free cash flow margin of 17.8%. The increase in adjusted free cash flow reflects a 173% increase year-on-year. On a per share basis, adjusted free cash flow was up 174% to $0.107. This proves the exceptional cash-generative nature of our model. We closed the year with $47.6 million in cash even after returning $71.6 million to shareholders in the period. Since we started the buyback program 2 years ago, we have returned $115 million and 9% of the diluted shares outstanding at the beginning of the share program. This brings me to the capital allocation framework, which remains unchanged.

First, we invest in organic growth, deploying an incremental $24 million last year whilst keeping product investment at a steady 3% of revenue. Second, we retain flexibility for strategic M&A that can accelerate our road map. Finally, we return excess capital to shareholders. Therefore, given our strong cash position and confidence in future cash generation, we intend to purchase a further GBP 30 million of shares, of which GBP 7.5 million will be via our employee benefit trust to satisfy future share awards. Turning to the outlook. Based on the strong bookings momentum in 2025, which is a leading indicator of revenue, we expect to deliver high teens constant currency revenue growth for 2026. Alongside this top line growth, we expect operating leverage to continue flowing through the business, delivering a further 2 to 3 percentage point increase in adjusted EBITDA margin.

The ongoing share buyback then compounds this value and drives further improvement in the free cash flow per share. And this brings me to our upgraded outer year margin guidance. We have always talked about 30% margins being achievable. Today, we're putting a clear time frame on this. Over the past few years, we have transformed the margin profile of the business. We have strong top line growth and structural operating efficiencies, which combined with the benefits that we're beginning to see from AI, gives us confidence that we will reach a 25% adjusted EBITDA margin in 2028 and 30% in 2030. In addition to our strong track record of growth and margin improvement, AI gives us the confidence to put this time frame on the margin progression for 3 key reasons.

First, AI is driving the value proposition within the enterprise segment where we see the highest margin. Second, it allow us to deliver higher-value products quicker. And third, it contributes directly to internal operating efficiency. With a high gross margin, we have a choice about where to deploy capital, and we have demonstrated over the past few years that we can deliver operating leverage across the P&L. And with that, I will hand it back to Adrian.

Thanks, Hanno. So in 2025, we successfully executed against 3 core priorities: trust in the age of AI, enterprise growth and product innovation. Let me walk you through exactly what we did in each of these areas. Let's start with trust. Trustpilot's relevance in the age of AI is becoming increasingly clear. Whatever capabilities AI developed in the future, there will still be people and there will still be businesses. People will always be interested in and want to share their experiences with those businesses. As you can see from the chart, the rise of AI has coincided with the surge in the number of reviews people are writing on Trustpilot and the number of businesses they cover. Since the launch of ChatGPT in late 2022, cumulative review volume has grown by 47% in just 3 years. The 62 million reviews written on Trustpilot in 2025 was more than in the first 12 years of the platform combined.

A key reason for this is that the rise of AI has motivated more businesses to ask their customers for Trustpilot feedback because that feedback influences how they show up in AI search. The growth in reviews provides the verified human data set required to train safe AI and deliver the trusted citations that power modern answer engines. But volume without trust is meaningless. And in 2025, we removed 7.8 million fake reviews, an increase of 74% year-on-year. Let's walk through how we protect the platform using a connected system of technology, people and community, all underpinned by increasing regulation. When a review is submitted, the first line of defense is technology. Every single review undergoes automated multi-signal screening where AI models scan device, network and behavioral metadata such as location indicators and time stamp patterns.

Given the volume of incoming data, we use a sophisticated range of techniques from AI, machine learning models and neural network analysis. Out of all the fake reviews we catch in 2025, 91% were removed by our automated detection efforts. The volume of historic reviews on Trustpilot with all the accompanying metadata is what enables our technology to be so effective. Technology is supported by people who conduct specialist assessments and perform testing to continuously improve detection. This is all reinforced by the Trustpilot community. Any consumer or business can flag a review they view as suspicious. These reports act as a vital feedback loop triggering further checks that initiate our standard automated and expert reviews. We never claim to be perfect and operating at our scale, it's inevitable that some misuse still occurs. But the evidence is that AI is helping us more than it's helping bad actors, meaning the platform is becoming safer and more useful than ever.

Integrity also means applying the same moderation standards to all reviews regardless of whether the business is a paying customer. The chart on the page shows the percentage of reviews we remove across different star ratings. You'll notice this distribution is virtually identical whether a business is paying us or using free tools. The vast majority of fake reviews we remove, around 75% are actually 5-star reviews. The algorithms are blind to a company's commercial status. They only care about the authenticity of the data. Protecting platform integrity is work that never stops. As we improve our existing models and develop new ones, we first apply them to new reviews coming on to the platform. And once we're confident that they are performing as intended, we run them back over historical reviews.

This is the absolute core of our business, and we have a lot more to share here. Therefore, I'm pleased to announce we'll host a dedicated trust-focused event on the 6th of May. Here, our Chief Trust Officer, Shazadi Stinton, who joined us recently and was formerly General Counsel at MoneyGroup and her team will take you through what we do in more detail. And I'm delighted that Shazadi could be with us in the room today. All right. Moving on to the second priority of enterprise growth. When we talk to business leaders, the conversation starts with a really simple reality. Private customer experience data that they collect like an internal NPS or CSAT score is incomplete. It's invisible to large language models, and it does nothing to help you win your next customer.

To get a complete picture and to grow, businesses need to operationalize public feedback. We give them the tools to do exactly that. Let me set out here exactly how Trustpilot helps large businesses, those enterprise customers succeed. First, build trust. Potential customers, investors and employees can all read your Trustpilot feedback. Engaging with the platform builds trust with all of these groups. Numerous businesses include Trustpilot scores in their Board decks and bonus plans and increasingly, Trustpilot appears in annual reports, including those of many FTSE 100 constituents like Admiral, BT, Centrica and Lloyds Banking Group. Next, Trustpilot feedback directly helps businesses grow faster by boosting their presence in Google and on large language models and by improving conversion and engagement with marketing assets through social proof.

A major U.S. cybersecurity business told me they gained market share by using Trustpilot reviews throughout their conversion funnel for new customers to accelerate sign-ups. Third, Trustpilot helps businesses improve operational efficiency. A large logistics business I met found that thanks to their huge volume of Trustpilot feedback, they can identify faulty physical lockers quicker than their internal maintenance team can. Finally, Trustpilot galvanizes entire organizations to care about customer service. And because the reviews are public, it enforces accountability. I gave you an example at the half year of HSBC. Another great example is the CEO of a major European car leasing business who told me he holds the managers of each of over 500 branches accountable for Trustpilot feedback at their location. And part of their job is responding personally to unhappy customers through our platform.

The focus on larger customers is delivering tangible results. Because of the immense value they get from our products, we have more customers paying us large amounts of money and getting a fantastic return on their investment. If you look at the middle chart, the number of paying customers between $10,000 and $20,000 a year has grown at a 25% compound annual growth rate over the last 3 years. In fact, we've nearly doubled our customer base in that segment since 2022. The number paying us over $20,000 a year, which make up the largest share of total ARR has grown by around 36% a year over that same period. To put that in perspective, we've grown this critical segment by 150% in just 3 years, scaling to nearly 3,000 customers today.

This strategic shift towards larger customers improves unit economics, enhances the quality of earnings and accelerates the growth flywheel faster because big businesses invite millions of people to write feedback and showcase the Trustpilot brand at great scale. One business you'll all know that's embracing every part of the Trustpilot value proposition is the leading U.K. retailer, AO. They are now very close to hitting 1 million Trustpilot reviews. For an online-only business selling high-value items like washing machines, trust and quality of service is their primary differentiator. AO uses Trustpilot as a key operational tool. They mine scores, reviews and trends to actively manage their customer service and inform their core business propositions. They've integrated Trustpilot into national TV campaigns and their core brand positioning.

Who better to bring this to life than their Founder and CEO, John Roberts, in conversation here with our new Chief Trust Officer, Shazadi Stinton.

[Presentation]

There we go. Feedback is a gift, folks. So a great example there of how Trustpilot is playing a central role in how a large enterprise business builds trust grows and improves as we're now doing for many thousands around the world. On this slide, you can see how focus on higher-value customers is transforming our footprint in the U.S. By selling premium tools and integrations, we're moving upmarket. U.S. customers paying over $20,000 a year have grown at a 48% compound annual growth rate since 2022. The ARR from this segment has grown at a 51% compound annual rate in the same period. As the U.S. growth flywheel accelerates, go-to-market efficiency is improving. North American contribution margin expanded to a record 38% in 2025.

North America continues to represent a huge opportunity for Trustpilot. We remain less than 1% penetrated into the addressable market. Now moving on to the third priority we delivered on product innovation. Our road map is focused on a fundamental shift in how the Internet works from traditional search to AI-driven answers. Large language models can't afford to hallucinate about brand reputation. They need structured, verified third-party data to ground their answers in reality. Trustpilot provides that essential layer of human insight. LLMs care about the quantity and recency of content as well as its trustworthiness. All that means that for businesses which care about being visible in AI search, being a Trustpilot customer is going to become increasingly important. We're seeing that reflected in the conversations we're having with new customers and in the strength of new sales.

In each of the last 2 years, we had one major release window for B2B product innovation. These releases help businesses get actionable insights from their customer feedback and turn that feedback into growth, for example, with new visual assets featuring the Trustpilot brand. The outcome was the strong net revenue retention numbers we reported for both years. In 2025, following the arrival of our new Chief Product Officer, Ciaran Dynes, we established a multiyear product road map to build trust in the age of AI. This year, 2026, the road map is focused on greater AI visibility, expanding review collection methods and platform trust and helping large enterprise clients operationalize customer feedback across multiple domains. You'll see many of these specific features launched in just a few weeks in early April. And there'll be plenty more this year. Thanks to AI accelerating our ability to release new products, we'll have a second major release window in early Q4.

We see our advantage through 3 structural pillars that together form an unreplicable moat. First is the vast proprietary data set. We have 361 million active reviews on the platform across 1.3 million claim domains, adding around 200,000 new reviews every weekday. Because people can see that Trustpilot feedback has influence over other users and other businesses, they're inclined to leave more of it. This isn't just a basic star rating. It's often deep, rich, descriptive human content that is unique to us. AI increases the value of this data set in important ways. We're becoming an essential reference point across new surfaces. We've mentioned a few times Trustpilot's importance to large language models. But with the rise of agentic commerce, Trustpilot's role will become even more critical. Autonomous agents will need information to determine which merchants to trust.

The best agents will be the ones that use Trustpilot's feedback and our other proprietary data points to make their choices. A key proof point of this in 2026 will be showing our ability to secure merchant trust data partnerships with leading commerce platforms. Finally, AI increases the quality of insight businesses and consumers can get from Trustpilot reviews. By using our APIs, businesses get deeper, better and quicker insights by fusing our public feedback with their private CX data. Our AI summaries also allow consumers to get richer insights more quickly. So to wrap up, I started out by saying that at its core, Trustpilot products collect customer feedback and turn it into influence. As we've outlined today, AI will multiply that influence. In 2026, we've got 3 clear priorities: scaling trust, accelerating with AI and growing enterprise.

Our strong execution and structural leverage, combined with the benefits we already see from AI, give us the confidence today to upgrade our margin targets to 25% adjusted EBITDA in 2028 and 30% in 2030. And with that, thank you, Hanno and I are ready to take your questions.

It's Jessica Pok from Peel Hunt. I'll go with the usual 3. In terms of the product road map for the year, how are you thinking about the monetization? Is it a matter of some of the products going into the top packages? Are you going to monetize separately? can we get some idea of that? And also thinking about the future, new pricing packages, was it last year. Is it a matter of a couple of years, you add another top package? Or how are you thinking about those pricing packages and renewal of those? The second one is just on Trustlayer. That was launched at the end of Q3 last year, if I'm right. Just on the progress of that. And then the final one, just on costs. I mean, very clear in terms of your targets for the margin going into 2030.

How do we think about the different lines? I mean, clearly, it's going to be sales and marketing, which will be as a percentage of revenue will reduce over time. But the other 2 cost lines, the tech and also G&A, especially as you've mentioned that the number of fake reviews have gone up, but you're managing that quite well. Could we expect those lines to edge up a little bit as a percentage of revenue whilst sales and marketing come down? Just some guidance would be good.

Yes. Let me take the first 2, and then I'll pass to Hanno for the cost question. So road map monetization, I mean, we really think about this in terms of start with value creation, which the features need to deliver and then think about value capture and the different mechanisms for capturing that. When we roll out new features this year, there will be pricing obviously associated with those. And for the most part, customers won't get the new features until they've renewed on to higher price points. But we're sticking with the same essential different packages. So it's not a repeat of 2024 where the whole structure of how we do pricing changed. In terms of data solutions, we don't break that out as a separate line in our financials. A lot of some customers buying the Data Solutions API are also core Trustpilot customers for the business proposition.

But the key thing that changed last year is we made the API accessible as a product in its own right. So businesses like consultancies or investment firms can buy the API that gives them access to those 361 million reviews with all of the insights in it without having to buy the rest of the Trustpilot business proposition. We continue to see growth in that, albeit from a low base. But I hope you can see with everything that we're saying about AI and the potential value of this data, for example, in -- as a merchant trust signal across different commerce platforms and for agentic commerce, I hope you can see the sort of strategic importance of it. I don't view data solutions as a kind of ancillary revenue driver purely. It's really about strategically how do we make the most of the data set that we've got.

And all of that helps the growth flywheel because as I've been saying, the more impact, the more influence the feedback has, the more motivated businesses are to engage with the platform, the more motivated consumers are to write feedback.

And then on the margins, so 30% by '30, obviously, we've always said 30% is very achievable. This is a high gross margin business. And ultimately, if we don't add cost, the natural progression of the margin flow-through is pretty quick. And so the question is, where do we make the decisions to add costs in the next number of years, and we'll make those decisions, obviously, in these areas where we see the highest return. And so I think what you would expect is G&A to have a natural sort of operating leverage flow through. And then between tech and sales and marketing, we don't want to sort of predefine a shape of the P&L in the next couple of years, but rather sort of look at this because ultimately, whether it's going to be adding humans or tokens, it's going to be somewhere in those buckets.

It's Tim Ramskill from Bank of America. I have 3 questions also. I guess you've been very consistent about your expectations on medium-term top line growth. I just wondered if you could share with your thoughts on how that shape might evolve in terms of does the gross retention you expect sort of to nudge higher. Obviously, you've shared a stat today around what that looks like for enterprise and everything else that follows. So just the sort of shape of things going forward. And then from a working capital perspective, there were clearly some sort of pretty meaningful movements in contract liabilities, which again, I recognize reflects sort of an evolution of the customer base and the billing dynamics.

Can you just give us a sense as do you expect more of that going into 2026? Or have we kind of seen the bulk of it, just some sense of scale? And then I guess, just interested in the North American growth opportunity and how you go about tackling specific industry subsegments. It feels like in a market like the U.K., there's almost been, if you pardon the phrase, a little bit of FOMO among some of your customers. So x number of businesses adopt. And then I guess, if you're not adopting, there's perhaps a bit more pressure to think about it. So just how do you go about kind of building that momentum and where are you starting to see success? I know you highlighted Liberty Mutual as a recent win in the insurance space.

Let me maybe take the first and the third, and then I'll pass to Hanno for the working capital question. So top line growth, you can see we're guiding to high teens revenue growth for this year based on last year's bookings. We're not changing our medium-term guidance of mid-teens growth. And you can see that the shape of it, as you say, evolved this year with gross retention improving to -- in 2025, improving to 87%. That's actually a really great stat because it shows that the customer base is becoming healthier. We trended up. I think it was 84% when I joined the business 2.5 years ago. So we trended up quite meaningfully to 87%, which is the result of a lot of effort by the teams, like it's been a real focus in 2025 to improve that gross retention number. And that's obviously a big component of the 102% net revenue retention that we delivered.

In terms of North America and the approach, I mean, I think what the evidence shows that we put in front of you today is that the approach is working. So we're not changing it. So we're focusing on large businesses. We're finding strong traction in particular verticals like financial services, health care, education, software, and we're going hard at those verticals as you would expect. We have a playbook for how we do this that has served us well in the U.K., is serving us well in Germany and Italy as well. And we're going about it in the U.S. in that way. We can see as we grow stronger in a vertical, it becomes easier to win additional customers in those verticals because people can see their competitors benefiting from all the great stuff that we do.

Yes. And then on working capital. So I think there's a couple of things that happened. Firstly, we talked about this earlier. We introduced an incentive to the sales team to focus on annual prepayments rather than sort of quarterly or monthly. And especially as we're shifting more towards enterprise customers, they're very much used to paying annually upfront. And so the share of enterprise is growing, the share of customers paying us annually upfront and a new business sales last year was growing and increasing. And that's basically just pulling cash flow forward that is now going to be a recurring benefit because ultimately, these customers renew, especially the enterprise customers tend to renew at higher rates.

So over time, you should see a mix shift in the entire portfolio. And we said last year, the benefit was about $10 million, and so you would expect to see that sort of continue and compound over time. So structurally, the working capital dynamics of the business have improved.

Should we go to the back over there and then we'll come to this side.

It's Hai here from UBS. Congratulations on the strong results. I have a couple, please. So I guess, first on free cash flow and buybacks and capital allocation, right? So you returned north of $70 million last year. You have $30 million for this half year. How should I think about capital allocation going forward, given your free cash flow is growing strongly. And you mentioned M&A is the second priority of your capital allocation. What kind of targets -- what kind of M&A targets are you thinking about here? And why is the buyback not the same run rate as previously?

Let me take the M&A part of that and then Hanno for the buyback. So nothing's changed as far as M&A is concerned. There's nothing on the agenda right now. And I've always said in these forums that were we to do it, it would be product add-on, small-scale product add-on kind of M&A. It wouldn't be -- there isn't any sort of large-scale target that we've got in mind.

Yes. So basically, the allocation -- the capital allocation framework remains unchanged, like we said, right? And so if you think about, we've been very consistent in not only sort of returning cash to shareholders from the free cash flow that we're generating, but also looking at the balance sheet and looking at how much cash do we actually need on the balance sheet. And certainly, as the business becomes more and more profitable, that sort of margin goes down, and we're able to return some excess cash also from the balance sheet. We've done that successfully last year. I think in terms of quantum, we've been very consistently sort of doing GBP 30 million buyback. The nuance this year is not all these shares getting canceled, some of them getting put into the EBT to offset sort of future share issuance, but the net impact to shareholders is basically the same. It's just the technicality.

And so I think we had a -- we took advantage of a dislocation in the share price last fall and concluded the buyback sooner than anticipated and then sort of did a small top-up earlier this year. But the run rate, and if you look at the sort of the announcement, it's been very consistent GBP 25 million, GBP 30 million every 6 months.

My second question is on the AI.

You had 2? that's [indiscernible].

So just bigger picture, right, in the age of AI, it's easier for mass -- well, AI-generated reviews [indiscernible], right? So how do you see the cost of combating that using your own AI increases over time? How do you see that dynamic? And is that factored in your midterm guidance already?

Yes, it's absolutely factored in. And you can see in our 2025 results, how that's played out. We've delivered rapid margin expansion while doing more than ever to keep the platform safe. And a lot of that is thanks to AI. As I said in my comments, AI is helping us more than it's helping bad actors to do what we do to get better at it and to do it at greater scale with cost efficiency. Let's go over to Gareth, please.

Gareth Davies from Deutsche Numis. A couple around the U.S. for me. The first, a healthy step-up in terms of the U.S. contribution margin, 32% in '23, up to 38%. How should we think about that progress going forward? And I mean, 65% in the U.K. already, is there any structural reason why you think the U.S. won't get there over the next few years? And then sort of the related point is really around the top line opportunity in the U.S. and you're only 1% penetrated into that customer base. Can you just talk a little bit about that balancing act from your perspective in terms of running harder at driving the top line in the U.S., building up penetration versus managing that operating leverage?

Absolutely. Yes. So this is a high-quality problem to have, having a vast addressable market with such a strong value proposition that is unrivaled by any other business in the U.S. economy. So -- but you're absolutely right. It does mean that we have to make a decision as we go through every budget cycle about capital allocation, how much -- particularly how much we've put into the U.S. compared to other geographies like Germany, Italy, U.K., where we're also seeing strong returns. So the way we think about this is we're trying to deliver the best possible performance for the group. So we look at the return on those dollars. We look at the efficiency of our sales and marketing spend, and we take a view about taking everything into account, what will deliver the best possible performance for the group.

Now as we've said today, the growth flywheel is improving. It's accelerating in the U.S. as we've seen from other markets, that's one of the key drivers of the efficiency of our sales and marketing activities. So all of that all is in favor of the U.S. But the other high-quality problem we have is that we're also doing extremely well in Germany, for example. So we have a lot of these important choices to make. But that's how we think about it. We're ultimately optimizing for the group.

And just to be very specific, there is no reason -- no structural reason why the U.S. shouldn't over time, achieve the same contribution margins as the U.K. It's merely a function of the retention base becoming larger and larger at above 100% retention rates and then you're adding new business on top of that, but the retention revenue obviously drops through at high margins. And with increasing brand presence and awareness, the retention rates improve, the gross margin improves, the CAC improves. And so everything drives higher contribution margin.

It's Mark Hyatt from Morgan Stanley. I've got 2, please. If I could just follow up on the midterm guidance. Hanno, I know you've talked a lot about the long-term ambition around 30%, your confidence in getting there. But could you just talk a little bit about the confidence that you have to pin to a specific date now? What's really changed to put a date on it? And could you talk a little bit about if you have any AI cost savings, productivity savings baked into that guidance, that would be helpful. That's the first one. And then maybe for you, Adrian, you talked about how Answer engine optimization was partly a driver of that 2H bookings acceleration. So can you just give us a little bit more color on that? What types of customers are you seeing traction in? And I presume this is more of a new customer-led thing at the moment. So how do you plan on driving attach and benefit within the existing base in AEO?

Yes. So I mean if you think back to the -- probably the first slide in my presentation, you can really see the margin progression we've already delivered. And so just by extrapolating this, you will obviously naturally get to these higher margins. I think in the past, we've been more cautious in the way we've been giving guidance, but the sort of 2% to 3% margin guidance that we've given for this year, extrapolating gets you to 25% in 2028 and then 30% by '30 Obviously, we do want to have a little bit of room to make investments in each of these years in our budget cycle. And so this may not be a linear path, but we're very confident we can achieve those targets. And we were confident to put exact dates on this now specifically because in addition to the very consistent track record that we've delivered on both top line and margin expansion, we're also starting to see the benefits of AI that gives us a lot of confidence that we're going to be able, for example, to deliver more product more quickly, more efficiently.

We're going to have a more efficient sales organization. We're starting to see it in the finance function, for example, and in other areas of G&A where these tools are incredibly powerful. And so if you think about the natural sort of progression of the top line and then having AI allowing us to not add more cost, we will drive more margin through the business.

And then on AEO as a driver of the business, you wanted a bit more color. A couple of things I'll say. We ran a series of webinars. We are, at the moment, running a series of webinars on this topic of answer engine optimization and, of course, illustrating how Trustpilot plays into that. The first of that series of 3 webinars attracted 10x the number of attendees of any webinar that we've done in the company's history. And you ask what kind of business that is. It's mostly larger businesses, some medium-sized businesses as well because this is a topic in -- I'm sure it's a topic in all of your organizations. It's talking in every organization now how we're showing up in these new surfaces.

So we can see from evidence like that, just huge interest. And the great thing is what we are saying about this, gather more feedback, authentic human voices, recency, frequency of the content will help you to show up. That's not a controversial position to be taking in this market, right? If you go out and read any sort of best practice guides, they all talk about this stuff. So we are very much going with the flow here and saying it's really, really important to engage with this platform. I think the final thing I'd say is it's really throwing into stark relief the contrast between what we do, which is open, transparent public feedback and what large enterprises have often relied on in the past, which is this secret NPS sort of systems where you answer an NPS question as a user and you never hear anything about it again.

It goes into a black box. All of that is totally inaccessible to large language models, right? So the value of what we do and the way we integrate with those more private systems has just shot up as a result of what's happening with LLM.

Yes, Please.

Ross Broadford from RBC, and congrats on the results as well. Three, please. The first, could you give any more color on a run rate contribution from Trustlayer? or what sort of proportion of sales you think that could reach? Number two, what's your view of where the pricing of the product currently sits? $25,000 still feels rather good value to me at the enterprise level. And number three, has there been a material step-up in the safeguarding ecosystem in recent months to prevent the kind of reviews flagged in a recent research report from recurring?

So happy to deal with each of those. So Data Solutions, we don't, as I said, break it out as a separate line, and it's really baked into the underlying strategy of the business. It's often bought as a package with other parts of what we do. And the overall gross margin of the group is 82.7%. So we would expect Data Solutions also to be a very high-margin business. You asked about pricing. I fully agree. I think our products are great value. I spend a lot of my time telling that to people. As I said earlier, we think about the balance between value creation versus value capture. I think you heard very beautifully put by John Roberts earlier at AO, the value capture that really exists for a large business when you fully embrace what our platform can do. And I still think we're on a very healthy end of that value creation versus value capture share.

And then you asked about safeguarding the platform and stepping up our efforts there. So as you can see, we significantly stepped that up through 2025 in terms of the volume of activity, and that means both volume of fake reviews being removed, also the amount of business enforcement activity we do, which is a whole other piece, putting warnings on profile pages when we think businesses are trying to manipulate the platform or have a regulatory warning against them or something else, a significant increase in the volume of that sort of activity in 2025 as well. But I think the important thing to recognize is that's baked into the numbers that we are reporting today that we've just delivered. And part of that is because it really is extraordinary with AI that so much you can do better, faster and cheaper at the same time.

So we've ramped up the amount of spend in this area, but we've also done disproportionately more with the effort that we're making and a lot of that is thanks to technology.

Sean Kealy from Panmure Liberum. Two, if I can. Firstly, just on investment levels. Should we be thinking -- so would you give us any guidance for going forward this year? I'm just conscious that I think, Adrian, you mentioned you've got 2 sets of releases this year. And Hanno, you sort of mentioned that the balance between sales and marketing, tech and content may change a little bit. Just how are you thinking about current investment levels and whether or not either we should be looking at either cap dev or the part that goes to the P&L changing at any point in the next couple of years? And then secondly, would you have a gross sort of retention target you'd be willing to sort of communicate for maybe medium to long term at this point?

So on the cap dev, I mean, I think it's been about 3% in the last couple of years, and we would expect that to not go down. We're definitely looking to invest into product. And at the same time, we're making the team more efficient. So I think it's going to be in a similar level in the next few years and not specifically, no. I mean I think the gross retention rate has been a meaningful focus for us in 2025. We talked about this. We've made it part of the company-wide bonus plan. We've made it part of the commission plans for the customer success teams. And we've seen the results. And obviously, we're going to keep focusing on it, but we're not putting a specific target out there.

Joe George from JPMorgan. Just 2 for me, guys. Firstly, I just wanted to ask on any shift you've seen in the competitive backdrop and the barriers to entry that you see for your product and maybe not like-for-like alternatives, but alternatives that enterprise customers may look at in the era of AI search versus traditional search. And then secondly, maybe just on the long-term margin guidance. Can you just talk a little bit about the extent to which this builds in further monetization opportunities and further product launches? I guess I'm asking the achievability of this based on the current product suite versus to what extent it's baking in further launches as well.

Yes. So just to help with this. So the competitive situation I would say if anything has strengthened in the last year. I mentioned already the sort of disproportionate value of public feedback today versus -- because of LLMs versus where it was a couple of years ago versus private feedback because it's readable by AI. But I think what Trustpilot does being an open cross-platform, cross vertical global system for customer feedback, there isn't really anything else quite like it. And so internally, I've been in other businesses where every day, people are talking about the competition. Internally, we're really focused on customers and doing more for them and relying on the kind of natural competitive moat of growing the volume of feedback, growing the volume of customers with all of the dynamics that you're familiar with.

And then you asked about product development and to what extent is that baked into the margins. I mean I think you can see with the product development spend that we've got, we're rolling out improvements with, if anything, increasing velocity. So I'm sure we will roll out. We have -- as I mentioned in my remarks, we have a Chief Product Officer with a multiyear product road map. We've got all kinds of ideas about what we'd like to do going forward. And I'm sure you'll see a lot of those developments in the coming years. So it's not assuming that our product set remains static by any means.

I think it doesn't require that, though, I mean, in terms of -- we have a great product that we're selling. We're continuing to build on it. That's going to drive continued gross retention and net retention. But if you just think about the white space ahead of us in each of these markets, there is massive markets like Germany, Italy, France that are still significantly smaller than our U.K. business, which there's no structural reason why there shouldn't be a similar size. And then obviously, you have the whole U.S. opportunity, which should be orders of magnitude larger. And so we can just continue to do what we're doing and compound this business at sort of mid-teens, and it's very clearly achievable.

All right. I think we're done. Thank you very much, Everyone. Apologies. There's a question online. Let's just take that.

It's from Patrick at Goodbody. So in terms of AI, can you define how much you're spending on external software packages? And do you expect to internalize a lot of this with AI tools?

No. Certainly not off the cuff.

All right. Thanks, everybody.