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Good morning, everybody. Thank you for coming, and it's good to see you. This is my second time having the last presentation. I think this really will be the last one. So it has been a good year. We've strengthened pretty much on all fronts. We've got a 2% increase in our data products, which a while back, people were thinking, well, that's all going to be taken over by AI. That will be a theme for me today to say actually, AI is a big benefit to us and a big opportunity. It isn't something that's been holding us back. And you can see that in a very difficult market, and it's difficult because apart from all the other anxieties, there are particular anxieties around our industry because of AI and will we all be replaced. In that market, we're growing and we're growing where we most want to be growing.

We've been strong on controlling costs. You'll hear a lot more about the VDP from James. And there are some other interesting things that he will talk about. I will just focus on my role, which has been -- when I came in 2 years ago or 1.5 years ago, I said I was going to do 3 things in that interim period that I had specific objectives. One is to stabilize the business. I think you can see our numbers have done that, and I'll give you some more details about that in a moment. The second is to return to innovation. And the third, of course, is to prepare and deliver the leadership transition.

And on the first of those, we've seen accelerating growth in our key areas with 3% up versus last year. In Data Products as well, we've done particularly well. We've got that cost discipline that I referred to. And also, we have made that switch back to focusing on Data Products from custom. It's not that custom isn't important. Custom has also become very much more a data product than it used to be. It's less about the customization and more about the richness and fullness of the data. So even our custom trackers are really very similar to data products. And we've focused -- we had a unit that focuses on developing that data set or something that can be partnered with and a bit more about that later as well.

Innovation, we have accelerated. We've enhanced the product. There is now an AI Agent that can tell you about what's in the data and help you find more, and that will be growing. And I mean, everybody has that from an agent that looks at your data. We have a heck of a lot more data than anybody else by an order of magnitude. And to get the most out of that is what we're doing. We're adding MCPs, of course, all of that. But above all, as you'll see later on, we're making it a platform that an agent can use directly. We have -- those are our AI-powered solutions and the platform we've long talked about, and now it is becoming really a full-on data platform. Again, I'll show you later on at the end where that's going.

And the third priority was that leadership transition. And I said that we wouldn't make the same errors twice. So we were looking for somebody who is -- has proven leadership in an adjacent area where we can see what they've delivered in Wayne Levings. We found that somebody who has led teams, grown P&Ls, knows the industry inside out and also understands the challenges of AI. We have somebody that we know is going to do this job well. We -- I also said that instead of just handing over, simply we would have a proper transition period in which the new person learns the ropes a bit and not just expected to immediately fit right in because there's a lot. We're a complicated company. We're different to many other -- most -- all other companies in our industry, and it does need a proper transition.

So I'm looking forward to that period. I have a very strong relationship with Wayne, and I'm very confident that we're going to be working well together, especially since I have a new role in the post January period when I go back to being a nonexecutive director, but overseeing running, chairing the Innovations Committee, which will be very active in helping to direct and guide the things happening that the executives are doing with the system. So handing over for the financial review to James.

Hello, everyone, and thank you, Stephan. This is now my second results presentation since I've been at YouGov. You may recall my first one was at the half year after just 5 weeks of joining. And the last 6 months have definitely been fulfilling. We've made deep progress on the planning and execution of the value delivery plan. We've invested in areas that matter. We are growing up now, yet we are keeping the entrepreneurial discipline alongside all of the decisions we make. And alongside all of that as well, we are keeping innovative ideas flowing. And they're not just flowing, they're flowing and they're converting as well. And Stephan will talk about that in more detail later.

So before we get into numbers, I wanted to highlight the key priorities of H2 from my perspective and the wider Board's perspective as well. Priority and there are 7 of these priorities. Priority one, from a market and investor relations perspective, we want to do what we say we're going to do. I've been in the public markets for a long time, and I know how important that is for employees, for external analysts and investors. So we commit that, that is what we're going to do. And this obviously included being within the guidance range that we announced in March, plus delivery on various other areas as well. Priority 2, rebuild trust and improve transparency and clarity where possible. Throughout this presentation, I will refer to things that we committed to at the H1 results and either confirm the action is completed or update on how we're getting on in solving that problem.

And going forward, there may be times where we decide an adjustment to previously communicated commitments is required. It's important that our trajectory may change, and it's important that we're agile and we chip in a changing environment. If our direction does change, we commit to explain clearly what that change is and why that change has happened. Today, there are no such changes to commitments to disclose. We are bang on track for everything that we have committed to in recent times. Priority 3, balance sheet stability. In March, we had a fast approaching maturity date for the debt facility. An extension was therefore critical. This has happened. Priority 4, obtain approval for a share buyback program and start the program. I'm delighted to confirm that the buyback starts today. I believe actually already has started. And over the next 12 months, up to a total amount of GBP 15 million will be returned by this route.

Priority 5, evidence continued solid execution of the Value Delivery Plan. Wave 1 was executed at pace before the H1 results. Wave 2 and 3 are well underway. Planning is advanced and execution is happening. Priority 6, initiate the wide-ranging strategic review of our Shopper division. Again, that is ongoing. And priority 7 provide clear guidance going forward, no vagueness or confusion. And in the release, you'll see very clear guidance in pounds millions numbers with no confusion, vagueness or clandestine approach to what we think will happen. And I'd like to see that our results presentation released a few hours ago and our presentation we've given today provides high conviction that we have achieved 7 Tier 1 priorities. And please do challenge me or Stephan either today or later if you feel we've not achieved those objectives.

And I've circled four of these points, 1, 2, 5 and 7. These priorities aren't just the last 6 months. These priorities will last indefinitely. That's understandable. We're a young, tech-focused listed business, so we don't have an option, and we make sure that we will embrace that in a very growing up and clear way. Before I move on to the segmental operational results and with priorities 1 and 2 in mind, i.e., do what we say, we do and then transparency and clarity, I wanted to cover off how we reconcile between adjusted and statutory group numbers. We have rightly been criticized for lack of clarity in this area before, and I committed in our H1 results in March that the thresholds for items to be classified as separately identified will be higher. And also, we will be much more transparent on the makeup, too. I feel that understanding the makeup of the separately identifiable items is as important as the total quantum itself.

Hence, our commitment to priority 2, in particular, around transparency and clarity. Due to our history of M&A and restructuring initiatives, we have required the use of separately identifiable items. So how are these reported items being built up in FY '26? I split them into 3 buckets. Bucket one, I want to ensure that our balance sheet holding values for assets was thoroughly reviewed as part of my first year-end at YouGov. A large chunk of this impairment balance is the write-down in the balance sheet value of our DACH business. As a reminder, DACH includes our non-shopper German business, but it also includes the Swiss business, including CAI Research. This is a business that runs our computer-assisted telephone interviews.

The balance sheet valuation needed to be adjusted downwards, which we did through the numbers at the year-end. The remainder of this category includes lease impairments as we assess the simplicity and efficiency of our global office footprint as part of value delivery plan, along with some impairment of our software assets, which have been capitalized in recent years. Going forward, a continual firm view will always be applied on balance sheet holding values as our business evolves.

Bucket 2, amortization. An accounting rules-led amount, which is following on from our Shopper acquisition. This relates to 2 items: order backlog and customer list. The order backlog was fully amortized prior to the start of this financial year. And the amount of GBP 5 million from last year -- from the prior year will not be -- will not continue. That leaves GBP 10 million this year, which covers the customer list amortizations. In FY '27, we expect this aspect to continue with another GBP 10 million amount for further amortization of the customer list. This customer list amortization is expected to continue for the foreseeable future at broadly the same amount as this year. However, the order backlog will not come back. So circa GBP 10 million is going to be the number that fills bucket 2 going forward in this category.

Moving on to bucket 3. This is the only cash item within this grouping and arguably the more relevant line item when it comes to assessing underlying value. Buckets 1 and 2 are noncash. In high-level terms, bucket 3 covers restructuring costs. These fell from GBP 13 million last year to GBP 5 million this year as the threshold for inclusion increased and the vast majority of transitional services agreements for the Shopper acquisition have now been completed. Therefore, the cash amount of SRIs has more than halved year-on-year with a reduction of GBP 7.7 million. Going forward in FY '27 and beyond, bucket 3 will continue to be material as we deliver the value delivery plan. Bucket 2 will be GBP 10 million, as I discussed, and bucket 1 will be assessed throughout the year to ensure all balance sheet values are appropriate. The GBP 33.1 million total explains the difference between adjusted and statutory measures on the face of the results announcement. The adjusted EPS at 30p is down 11%, which is broadly in line with the reduction in adjusted operating profit.

As a reminder, though, as you'll have seen from the numbers, H2 adjusted operating profit was marginally up year-on-year. So this EPS downfall related purely to performance in H1. Now moving on to the segmental operational results. As Stephan alluded to, each of our 3 divisions delivered headline revenue growth. Yes, growth levels were low single-digit percentages. Although compared to the majority of our peer group, actual year-on-year growth in itself positions us towards the top of the pack. On an underlying basis, Data Products and Research grew 3%, and there was marginal shrinkage in Shopper. FY '26 is our 16th unbroken run of group revenue growth, an achievement that our teams around the world should be very proud of. We expect FY 2027 to be our 17th year of unbroken revenue growth.

We're particularly pleased with the margin uplift in data products, where operating leverage continues to kick in. I mentioned in H1 results that our Data Products margin needs to improve. Everything I was seeing in my first few weeks suggest that this margin should and can be higher. And this is a great step towards achieving the margin this syndicated subscription-based product fully deserves. The margin in Research was slightly down. This will improve and go back to last year's level and beyond as the value delivery plan delivers. Shopper's margin is materially -- I don't know if to use the word different or lower. Either way, it's lower relative to last year. And this is exclusively to do with the game changing investment program we discussed at the H1 results. If you were to add back this specific investment and targeted investment, the margin would be flat year-on-year.

Moving on to our geographic performance. Very strong performance in Americas. Ray and his team in the U.S. should be very -- had a very encouraging H2. All other regions have shown solid resilience in what we all know is a tough market environment. The vertical sectors in which we have been growing in the last 6 to 12 months is particularly pleasing. There have, however, been some tough sectors, as we have discussed before, agencies being one, and they are weaker and they have declined year-on-year. So with sports. Our sports branding remains very strong. However, there have been some operational challenges and personnel changes, which have led to a drop off. We see no reason why this can't continue to grow in the future.

The academic vertical is also down year-on-year. Although this is not a like-for-like decrease since FY '25 included the uptick due to the November '24 U.S. election work, and we're confident this trend will continue in the future. However, as touched on by Stephan already, and I'm sure he'll talk about it in a lot more detail later, we are all particularly pleased with the notable new client wins from the hottest, most advanced tech businesses who come to us because they want the best data points and they appreciate and understand the benefits the best data and insights give you. Revenue from the AI labs themselves have shown nice growth year-on-year.

The other vertical which is gaining deep traction is financial services. We're up nearly 20% year-on-year in this vertical. And we've got some great new blue-chip global financial services clients with a very high and deep tech focus coming to us in the last 6 or so months. At our H1 results, in March, I said that we needed to improve our cash conversion. And I'm delighted to say that we've increased this to 76% compared to 71% in the prior year and a mid-60 percentage output outcome at the half. I'm not yet comfortable giving a forward-looking target externally, but I'll give you a commitment that this will continue to be a key area of focus in FY '27 and beyond.

And CapEx has increased year-on-year for 2 main reasons -- or 3 main reasons, I guess. Panel investment as part of the shopper investment program. A big chunk of their investment is in modernizing and improving the depth and strength of their panels. And we have been increasing investment in technology to enable the next steps in automation and product capabilities. The value delivery plan is not just about efficiencies and savings, it's about investment in the right areas, too. We have a strong balance sheet. Are we well capitalized with ample liquidity? The answer is a clear yes. As at the end of September, cash was GBP 48 million, and we had GBP 8 million of undrawn RCF. At the end of June, these numbers were GBP 42 million and GBP 6 million, respectively. We are generating cash and continue to generate cash. Hence, our confidence of launching the share buyback program today.

During the year, we successfully extended the debt facilities during H2. There is no additional funding, no increase in borrower or guarantor obligations, no change to pricing, covenants or security. Total commitments remain at EUR 224 million, comprising EUR 184 million of term and EUR 40 million of RCF. The one change we did get through with the bank -- the banking group was to allow us to do a share buyback program. You may recall at the half year, we couldn't do one because we needed to seek a formal approval. As Stephan has already mentioned, as I say the buyback has started today and will be up to GBP 15 million as already disclosed for the next -- over the next 12 months. As guided at the half year, net debt to EBITDA reduced back to 1.7x with interest cover over 6x. And these covenants will be improving over the following reporting periods.

Value Delivery Plan. We launched this in March and momentum is building nicely. The transformation plan comprises 3 waves. The waves combine automation of key processes, efficiency improvements, simplification, enhanced ways of working, along with significant transformation enabled by AI. Plus Wave 3 also includes significant new revenue streams as well. As you will recall, Wave 1 completed in March '26. We delivered an annualized GBP 2.5 million reduction in our cost base, which targeted predominantly headcount and other cost reductions in non-revenue-generating areas. H2 '26 includes some benefit from this action already. FY '27 will benefit from the full-year-impact.

Wave 2 is deep in the detailed planning and implementation phase. It includes a broad range of initiatives. Automation and simplicity are 2 key themes already disclosed. The combined impact of the first 2 waves is expected to deliver an annualized adjusted operating profit margin uplift of 350 bps over the margin achieved in H1 FY '26, which was 12.3%. We have high levels of conviction this target will be achieved prior to the end of FY '27, another example of delivering what we said we'd deliver a few months ago. Wave 3 will focus on opening up new revenue streams through development of AI-powered products and our data as a product capabilities.

Our product development, data science and technology teams continue to make strides in these initiatives, including introducing imputation of our data set and developing newer products such as YouGov Parallax. I know Stephan will enjoy talking about it and add in more detail on these shortly. Wave 2 and 3 comprise 12 individual work streams, each with an executive owner to optimize the speed and certainty of delivery. I was going to name the 12 names, but there are 12 and it will take some time, but everyone is doing a great job. And I personally, I know the Board is, are very grateful for the efforts the team are doing to really push on with this big transformation initiative.

So final slide for me. Coming back to the priorities I mentioned initially, I want to give more clarity and granularity on trading and outlook. We've had an encouraging start to the year, bang in line with our expectations and our budgeted numbers. Currently, 49% of FY '27 revenue expectations are contractually secured. That's not a weak pipeline that is contractually secured. That is marginally up on the same period last year. Hence, the confidence earlier about FY '27 being our 17th year of revenue growth despite the market conditions. We're making a loan repayment this month. The last day of October, we will pay back EUR 20 million of the loan. We've disclosed our share buyback already. Investment in Shopper will continue in FY '27 at broadly the same levels as FY '26, although the benefits of the investment program are expected to start flowing through from a revenue perspective towards the end of FY '27.

Based on the significant progress achieved in planning and executing the value delivery plan over the last few months, along with continued investment in Shopper, the group expects to deliver adjusted operating profit of between GBP 58 million and GBP 61 million for FY '27. This is in line with current market expectations. And this represents a year-on-year growth of around double-digit percentage points. I'll stop there and just pass back to Stephan, who can talk about the exciting stuff now.

Thanks, James. And it's called Strategy Update, which sounds like it's a sort of distant prospect. Everything I've ever had in the Strategy Update becomes reality very quickly. It's actually a road map of what we're doing. It's not some vague thing of this is what we hope to be doing. This is what we're actually doing. And the story of it is that we're benefiting from AI in 3 ways. It is the opposite of what people expected. AI is very much our future. The first, which is very narrow, is -- I mean, this whole thing is a new vertical essentially for us, AI in terms of companies. Obviously, 2 new companies, Anthropic and OpenAI are the 2 main ones. They've become big clients of ours. And just very narrowly, we've done GBP 4 million just in traditional reputational work that they use to communicate -- to understand audiences, but also to communicate.

Anthropic has made a big deal out of very large surveys that we've done for them, which connect them to their users and to the public in one of the ways that is central to their strategy. So their spending there is already the biggest growing part of a specific bit of our business. And that number doesn't include lots of other stuff that is adjacent to it, things that they need to know, things that our consequence of news items and so on, and I'm not talking specifically those 2, I'm talking about the whole sector. That comes to about GBP 11 million now in sales over the last 12 months. So that is really a big deal for us. They're not -- they turn to us. They don't do it themselves. They -- of course, they need an outside agency. They need to talk to humans. They do that mainly through us, other companies as well. But the idea that human research is replaceable by machines is simply wrong. The machines really need the humans to supply that data.

So that's the first bit. The second bit is internal efficiency. Obviously, we're using AI to make our systems more efficient. I've got a slide on that in a second. And the third area, the most important is the AI-enabled products. Really, it's a transformation in products and in how we deploy data. So that second box, automating research at scale. We are looking to make an end-to-end automated research flow. That doesn't mean, by the way, that the humans disappear. I actually don't think they will even necessarily reduce. I think that we will grow in scale and be able to do much more with the people that are there because the handling, the helping of clients around these flows, getting the most out of the system is something that remains very important. So I just say that to ease concerns that people might have at the company that the jobs going, I don't believe I think we'll see growth getting scale out of our systems being the way that we most benefit from the advantages.

The advantages are also higher quality in some respect in terms of speed, consistency, accuracy, all of those things because humans make mistakes. The more of that process that we can automate, the more of it we can streamline before it even gets into the system because at the moment, it's typical that people come with their projects and they do them any way -- they design them in any way that they want and then we make that happen and it's highly customized. But the advantages to them as well as to our own efficiency is to streamline how they think about the work and how that works its way through the system and how that helps to reduce error. So the whole process is one that benefits all sides.

Parallax, our new invention. This time last year, I talked about Voices. Voices has become a successful part of our offer. We now -- we have just launched Parallax, which has also had its first 6-figure client confirmed. Parallax uses synthetic data, is synthetic data together with human data. You all know what the Parallax effect is having 2 eyes means you get 2 views of something that together make a 3-dimensional picture. That's what Parallax means. And that's exactly what we mean here. You get 2 views of the world. One is a narrow one, if I can put it that way, narrow but accurate and deep, which is the human view. We can -- we talk to 1,000, 2,000, 10,000 people that we know well and that we have lots of data for. And the data that comes out of that is the most reliable human survey data in the world. And that is the foundation. That can yield then that can create twins, synthetic twins. We have a partner, OriginalVoices, that helps us with that, that is experienced in that and has operationalized that in various ways to help get the maximum value out of that data.

And that can be whether it's our data, BrandIndex, the Cube, all of that to make the most detailed digital twins you can imagine, or whether it is somebody comes along, does a survey with us, and then we turn the participants of that survey into digital twins that can answer lots and lots more questions in the future. But just having those 2 things separate isn't what makes the real 3D view. It isn't just having either or combining the human view and the synthetic view. It is combining them through verification and validation. So at the same time as you do -- you generate questions, answers from the synthetic twins, Parallax allows you to take some of those questions. Let's say, you're asking 100 or 200 questions of the twins. It doesn't matter how many you ask. It's very, very efficient. It's very instantaneous. So you got no problem. You can ask as many questions as you like. You take 2 or 3 of those questions and run them against the human version of those same twins at the same time, you can then use that to calibrate all of the other questions.

So you're not -- it's not only 2 different data sets. It is the first data set that you generated through synthesis that is calibrated by the human data that comes after. I don't think anybody else offers this or can offer this the way that we can because we can talk to the same people at the same time as getting the twins because we have high response rates, and we have lots of data that we create. So our twins are constantly updated. They're constantly available and you get this thing that answers the central problem of today, which is what -- how good is synthetic, -- do we have to throw it away and just have human data? No, you can actually get the benefit of both in a way that strengthens both.

So that is a fundamental change to an operating system. That is a fundamental offer that we make to other -- to the industry, which I don't believe anybody else can make. And that will be something that goes through -- somebody previously asked us how much do you expect to make from this next year. It's very hard. I'm not going to give an answer to that question, as you already know. But I would say, in this case, precisely because it affects everything else we sell. Every single client can have twins of the work that we're doing for them in custom or in the syndicated part.

So that is a big deal for us that transforms the whole offering. And just to remind you of our ecosystem, it uses all the assets that we already have. I mean, we did this a year ago. What are our assets? We have a high reputation with the public as well as with clients that allows us to have the best panel, which produces the best connected data, which then allows you to do pretty much anything you like because if you start with engaged live panelists, you can do all sorts of things, you can experiment and you can verify and validate, synthesize everything. So that's the basic offer. You've seen this before. The red stuff is what we've had. The 2 new parts there are Voices and Parallax. They are all different entries into our world of data. They are -- you can go from one to the other, you can add them together. It is a connected system.

And that leads to that connected system leads to a seamless client journey throughout the whole world of data. This system is already -- you're able to query it using agents. We have MCPs. We have on our site, you're able to ask questions of the data. That is at an early stage. I will not say to you that this is -- I do say, in development. You can right now, as a client, go on Crunch, ask questions of the Cube and see what there is. And that's -- but that's almost table stakes. I said before, everybody else is doing that as well. The difference with us is the sheer quantity of data.

But where it becomes exciting, is that the agent can not only say here's what the data says, but do you need some other data, we can get it for you within 30 minutes. And you can write your own questions or you can have the LLM, your own LLM or ours or whatever we use in that place to write the surveys for you from all the knowledge that we have to answer your questions. Then we can use synthetic and Parallax at the same time. In other words, the agents can arrange the entire journey to give you the most full, the most connected and the most verifiable data that there is in the industry.

That is our system. And what you see there, all of that exists except for the green circle. The green circle is obviously an advanced AI system. We are not advanced creators of AIs, and we will be looking we have in mind someone like Thomson Reuters, who's made steps in this direction, creating their own domain-specific foundation model. That's the area that we are very realistically looking at very specific. We're not saying we're going to be a gigantic AI company. That's not at all we're saying. We're talking about in our industry focused on how clients of market research use data in their businesses. That's our expertise. That's where we're focused on, and that's where we want to create the most advanced agentic model that there is, which we can because of all the things that we already have here. I do repeat this model of being able to look at the data we have and instantly verify and add to it is from the same people is, I think, unique in the industry, certainly at the scale that we can do that. And we do have partners in this.

So I know some people have said some of our very good investors who communicate with and say, are you doing the Reddit thing? Are you selling lots of -- are you selling your data? That is a very dangerous thing for companies like us to do. If you have vast amounts of unstructured, if I may call it, dirty data like Reddit, then the AIs make value out of that by taking all of it. We can't possibly give out all our data to somebody else to turn into something that people can buy through them. It's our data, and we have to jealously guard it. Of course, we can share it through the systems like MCPs that can be controlled through agents that can be channeled in the right way to use that data. And that is what we're doing.

But we are looking at partnerships here. I mean we already have it's known to you a very strong relationship with Deloitte. Deloitte have upped their level of partnership with us significantly. And for their trust model, this is not a new thing. It's -- I think it's out there somewhere, but that's a developing -- an important and developing relationship. And we have other relationships that we're developing in order to get the value out of that and maybe actually some give and take in there as well that we can gain from their expertise.

So this is the way forward. This is a client journey that I think nobody else can mimic exactly, and that is what already -- most of that already exists. So I end by saying we got a framework for the next phase of YouGov's transformation. First, the New Generation Panel Ecosystem. I haven't said anything about that. That's actually my big final task in the next 3 months to make a bunch of changes that we have been making. We have some new methodologies for panel, and I'll tie that up, and that will be for somebody else to present subsequently. But how we recruit and maintain panel is fundamental, obviously, to everything you've seen here. I think we're the best at it, but we need to be better because people are changing, young people are engaging less. We all know that. It's getting harder to engage people into a continuous relationship, and we're doing new things in that area.

The second AI-Powered Platform Transformation. I've just shown you that in some detail. The third part, I've alluded to before, but it is saying this is a platform company. This has -- this has a certain kind -- is looking for a certain kind of work, and we have a unit that is focused on those partnerships that has had its first successes. We are -- and this goes to all parts of the business. This isn't data versus custom. Custom -- the difference between custom data, custom projects and Data Products doesn't -- isn't the same anymore. They merge together. And I'm thinking of one piece that I want to mention that I should have mentioned before when even the Data Products all survive at this as well because they are data as a service.

They continue to do really well. And I've said this before, but it's continued that the very -- the most sophisticated biggest companies are increasing the size of their subscriptions here and probably the most scientific big name company in the world now has a $1.5 million subscription to BrandIndex. So far from being becoming irrelevant, it's actually -- that data is becoming more valuable, not less valuable.

And then finally, the Operational Efficiency & Simplification we've talked about, making all of this as automated as possible so that our experts can also give the best possible advice and service because different customers need different levels of service. That is what it's all about. That's the end of that bit. We are now going into the Q&A. There is a microphone coming around, and I'll take that from the seat.

Sean Thapar from Canaccord. Three questions for me. I guess to start off with a big picture question is sometimes it's hard to get kind of a client-led view on spending. So across the industry, it's pretty low growth rates. They're pointing out -- industry competitors pointing out AI-led growth, but there's a lot of AI washing that's going on there. Could you just kind of explain kind of how clients are approaching their research budgets, spending areas where you're seeing demand and maybe where there has been a little bit of disruption from AI?

Yes. And there certainly has been disruption. I think I said at the beginning that you lose in some areas, but we're gaining in others. And when I look at the growth, it actually represents that loss on the one side and the growth on the other. It's a much better growth for the bits that we are obviously most focused on. And 2 things really. Clients are expecting to get more for less. I mean the basic assumption with AI is that we are already benefiting from the magic of it. Obviously, we're not. We are not at the point yet where we have the savings that come in. I think -- this is true around the whole world in all industries. It's for most suppliers, they're not yet getting the advantages of the efficiencies which the clients are wanting to bake in.

So that's one pressure. It hasn't actually hit us badly yet because we have the other -- I say yet. We are moving very quickly to get those advantages. That's what the automation program is all about. And we hope actually to get uplift from it because we will be the best suppliers of that automation and can, in fact, respond to those demands. But the other area is that the need for human data is growing. So you're seeing -- that's why you're seeing a lot of companies wanting more of that, wanting to make more out of it, and that's where we're seeing the growth. So where we're seeing loss is, for example, media agencies, they're spending less. You would expect that. Brands, I think, are reasonably stable and the AI companies are -- or anybody doing innovation in that area is becoming -- is growing.

And then maybe coming back on those AI companies is -- some of the AI labs now are some of the biggest companies in the world. From a kind of research spending perspective, how do they differ to the kind of Mag 7 type companies that you have already got a very big contribution from in your client base? And I guess the slightly cheeky question on top of that is, do you expect to be able to capture the same share of wallet as you have done in previous large tech platforms?

I mean one of the funny things about it is that those clients come back to you and tell you how you should be doing a business. And they say, well, actually, we've looked at how you structure your things because they sort of get to sneak a look, I don't know how, and say, why didn't you do it this way? It would be much faster or much better. So there is -- they can be very demanding in a good way. It forces us to innovate faster and to listen and to see how we want to deal with that. They're much less interested in your advice, in our advice.

Our consultancy, I think, is going to be assumed to come free because actually, they don't need it, and they don't often even care how you're packaging up the data. In fact, they tell us we just want it at more. And so yes, this is changing the demand. But for us, this is what we want. I mean, where we make our margin is in quantity of high-quality data. The bit that they can't get from anyone else is the actual relationship with humans that is a trusted personal relationship actually. And you need that, I think, in order to create great data.

And then a final question for me is just kind of tying together the strategy update with the value delivery program. I guess if we were to take research automation and additional AI revenues, do they map to a certain wave in value delivery program? Or to ask this a different way, is the 350 bps you're getting from Stage 2 dependent on wide-scale research automation and/or additional AI revenues?

I think it's the 2 bits together, but...

Typically, the automation is within Wave 2. And if we get the full wave of automation, which is not expected to happen all in this financial year, that will be a benefit above and beyond the 350 bps for the full automation, but there is a part of automation within that number. And the new revenue streams are particularly in Wave 3. There will be no new revenue in Wave 2.

It's Lara Simpson from JPMorgan. My first question was just to come back to products. Obviously, we saw quite strong acceleration in growth in the second half of the year. I know you spoke about the U.S., but could you just talk a bit more around the nature of those client and contract wins? And I suppose the sustainability of that, if they were more one-off in nature or a bit more recurring? And I suppose the follow-up to that is trying to think about the building blocks to growth in FY '27 and if you think that strong exit rate is sustainable on the data product side in particular?

And then my second question was on Shopper. I know you said that the strategic review is ongoing. Just wondering if you could give us any indication on when we should expect to get an update on that? Or otherwise, how far in discussions are you? Or if the Board has at least given an internal time line on when they would expect a decision to be made? And with that, just interested again on the rationale for continuing to invest in that business. I think James said the investments will be similar to 2026. I think the one discussion I have is you're obviously investing in a business that might not be with you on a go-forward basis. So just trying to understand the rationale for those investments and how you're thinking about yielding the return on those.

Well, I think on the latter, I'm really not going to say anything about the timing. It's obvious that I can't and won't. The other bit of that, though, is that Shopper needs to remain strong and yielding growth and that requires investment. So if you wanted to sell something that had gone flat, that wouldn't work either. So that simply fits the plan. It doesn't say anything about what we think is going to happen. It just has to be. The first question, which I think you're saying how -- are there new clients for our data products?

I'm just trying to understand Data Products accelerated. I think you did close to 7% growth in Data Products in the second half of the year. So just trying to understand the drivers of that acceleration.

Look, it is a really efficient way of understanding something that you can only do with human data, and that is change. One of the things I meant to say it earlier that is fundamental to understanding our business and how it's affected by AI is to know that most of what we do is to measure change. We obviously do some static polls and studies and so on. But that isn't the core of what we do. We measure change day in, day out by lots of different products and by big custom trackers. All of those things are about understanding how today is different from yesterday. And the one thing that AI and synthetics cannot ever do is tell you how things are changing, except retrospectively.

If they are to project forward, and I think a lot of synthetic data companies like to tell us they can because they're so brilliant and they know how the weather is going to affect everything and all these other mysterious things. If they could do that, they could only tell anything about the future by continuation of patterns from the past. They might say, if things continue as they look today, it will go this way. That's not why you buy trackers. You buy trackers to see what actually happens. And you look -- you're interested in surprises. So this product that we're launching that tested well and theoretically should go well, how is it actually going when people take it home and the kids play with it and the kids' friends play with it. How does that work through actual usage?

What happens when there's a scandal or a new advertising campaign or some related economic change? If it's as expected, it's priced in. It's not valuable. It's only valuable when you don't know which way it's going or you need to know when there's a surprise, and you can only do that with live human data by definition because of everything else is the continuation of existing patterns. So that stuff is not going to be -- in my view, it's not going to be attacked by AI. And that's why I'm so confident about synthetic from our point of view because it's a way of getting extra value. It is hugely valuable. I wasn't knocking synthetic data, as you understand, just for measuring change. You can't measure change with it, but you can get lots more value out of the data you've produced. So that does not attack our core business. Indeed, it gets more value out of it.

Having said that, I do imagine that some people who are used to having trackers might simply not do them anymore or not do them as much or as frequently because they think actually there's some things that we can get elsewhere. So I don't pretend that there will be no effects. And that's why it's important for us to be the best that we can be so that when you do keep tracking studies going, it's us that do the best job for you. And that's where I think the richness of our data and the smartness of our science is ahead of everybody else. But I can't say to you that I can make confident predictions about how all data products will go. I think we're well defended. Our moat is our panel. Nobody can touch that. Nobody can equal it. The entire desire of the synthetic companies is to squeeze out humans. They go and they buy mass polling and hope that they get this. They make their twins and they don't have ask to talk to them again for another year and get lots of value out of it. That isn't -- that doesn't work. They have to have live data to make a great product. So I think we're in a strong position.

I wouldn't want everyone to model 7% or 10% growth in data products next year on the top line. But I think the 2 or 3 encouraging things to add to Stephan is even though agencies, the overall income we got from agencies this year is lower than last year, but we are still winning some pretty big high-profile data products revenue from the agencies themselves, which supports Stephan's point that, that product is still very truly alive in a vertical sector that's kind of moving against it in some ways. And also what's encouraging is that there are some new technology clients, one of which we alluded to at the H1 results, which have no benefit for actual H1, that's come in relatively materially in H2 as well.

So technology clients are often more custom trackers and more research-based, they're now seeing the benefits of Data Products. So my focus on Data Products personally is very much the margin side and to ensure that we still get these new clients coming in. But the margin side is something that the value delivery plan is hitting pretty hard on data products because it's syndicated, it's got amazing operating leverage as we proved in H2. So that's as important as any top line growth from a value delivery perspective.

Sean Kealy from Panmure Liberum. I've got 2 for you, James, and then hopefully, 3 for Stephan, if that's all right. So apologies for possibly setting a record for a number of questions. First of all, James, you're very clear at the half year that you thought MI and sort of infrastructure inherited was good enough to run the business. I just wanted to get your updated view on what you think now of quality of financial reporting and infrastructure that you're working with and particularly noting you've had a bit of a restatement on tax this year and so on.

Financial reporting, no issues at all. We just had a -- I did my first audit with -- I mean it was a very clean audit, very relatively easy audit. The tax issue is, I'd say, a minor frustration. Happy to talk to you one-on-one about what the detail is. It's basically do with the Shopper fair value versus cash value when we purchased it, there was an accounting change, which wasn't picked up at the time. We paid for the accounting change, but we did not give us the credit for the extra payment. So that was a minor sort of acquisition-based change, which is annoying and it's not ideal, but that does not reflect on anything to do with our systems and our MI. And I'm feeling that each week I'm there, I'm getting more comfortable with the commercial MI that actually helps people make decisions.

And the whole thrust of the value delivery plan, there is 1 of the 12 work streams is on systems more generally. -- and focusing on margin more generally on time sheets more generally. So all this sort of wiring and sort of good discipline is a key theme, and that's all happening. And for example, now, previously, time sheets were filled in by some cohorts of our team, but not everybody. So you could get misplaced margin on some projects where now 100% time sheet completion full stop. Are they all reviewed and everything done proper yet? Not quite, but we're already seeing the benefits. So to answer your question, yes systems from a commercial perspective give me what we need, and I wouldn't read anything into the prior year adjustment. That's an annoyance as opposed to a systems issue.

Perfect. Very clear. Second question, given the ongoing value of group program, are you able to give us a gentle guide at this point for cash restructuring charge for the coming fiscal year?

I'd rather not give an actual number, but we are -- the areas we are investing in is in data scientists and engineers. And we're only going to be investing in that area if they're going to deliver a particular feature or product. And by definition, that means it will justify going on the balance sheet. So there will be a more structured, more tech company-focused approach to our capitalization of that type of spend. But a bit too early for me to give you an indication of a number. But happy to be asked that question at the half year when I'll have more clarity.

Perfect. And then, Stephan, if I can just rattle through 3. First of all, in terms of Parallax, can you just run me through -- I appreciate you've sort of said in the presentation that it's token-based pricing. But where are you seeing pricing for that product sort of -- I appreciate it's really early stage, but maybe it's more a question of where are you targeting pricing for that product relative to other services that you got provides? Secondly, with respect to the MCP connection, I'm just really interested to discover, are you primarily seeing clients pull your data into their own environment? Are they pulling it into an environment created by a third-party company? I'm just -- what I'm hoping to understand is sort of where clients are interacting with your data and so on. And then finally, on the AI labs revenue, both the figures of 4 and 11 that you gave, are you able to just sort of give me a run-through of where exactly that sits in divisions and various halves of the year just gone?

I'll start in reverse just remember the last one. So the 4 and 11 splits this way, the 4 is specifically work for AI companies that we weren't -- that wasn't happening before. The other is for investigating and adding to work that we have with other clients, not with -- some of them maybe labs. I mean, obviously, our -- some of our biggest clients, as you know, are Silicon Valley. So they fall into more than one vertical. But there are related things about how AI affects them as well as how using it for AI itself.

So the 4 is just is just specifically for dedicated AI companies. The others are for other effects being measured, but that we wouldn't have done if there hadn't been that. So you could say that it's a bit blurry whether -- because there's always something happening that you're investigating, so you might have had that. But it is -- it does present itself to us as a new vertical as a new thing that people have to think about. They're not only thinking about the customers' economics or whatever. It's a new thing you have to think about. So that's why it's 4 and then rate rises to 11 if you take into account all those other related things. The second one was...

On the MCP.

Yes. I'm actually not very good on this. I'm not a technical. They don't get to import our data. They pay for data, which they then can use. But I would embarrass myself if I said much more, but I will get somebody to find an answer on that.

Just to add, we're very conscious that we our General Counsel sitting in the back actually, but we're very conscious that if we start giving people access to our data, do we get disintermediated? Are we going to lose some control. So we're spending a lot of time on that at the moment with various advisers and help us because it's clearly a risk if we get that wrong, that's not good, but we are being very diligent to make sure if we let anybody anywhere near our environment, there's very strong legal protection. So it's a well-trodden path in other sectors. So we are getting the best advice to protect ourselves in that area.

First question was just on pricing for Parallax. I appreciate it's early days.

It's actually really hard. We have got a rate card. But the synthetic and real bit are 2 different things, and the real bit is priced the same way as any other research that we do. And the synthetic is something we're sort of working out on the fly with our partner that I mentioned. So right now, everything is individually done and experimented with. It is a completely new area. These are not definitive answers, I'm afraid.

It's Hai from UBS. I've got a couple. In terms of your growth assumptions for next year, could you clarify a little bit of the dynamic that you see versus this year? And is it going to be when you said not 7% to 10% in data products, a bit more normalized than that, but also slight improvements sequentially in Shopper from the investment you made? Is that what you're seeing? The second one is on in terms of the macro impact you're seeing over the next year, obviously, U.S. midterms coming up. How helpful is that to numbers usually? And -- but the interest rate environment on the other hand, and cost inflation coming up. So a general macro view on how that impacts your top line and margins?

And then finally, -- there's been recent news on a potential acquisition from Salesforce to AI-enabled like Listen Labs, for example, alternatives, which are gaining a lot of traction, at least from reporting. Do you see that as a potential threat to some of your business given that their business model is lower cost, fast turnaround? And I know, obviously, the answer to that is Parallax, but do you see a potential displacement in the near to medium term? And how do you address that?

I'll do the last one, but you do the first.

So in terms of our revenue forecast, we deliberately haven't given a formal revenue forecast. Well, obviously, if we were not comfortable with what the consensus was, we would let people know. So there will be growth in the core business next year. There will be marginal growth in Shopper is my prediction based on where we are at the moment. And in H2, we do expect that the benefits of the previous investment will start to show through because with the Shopper investment, as many of you know, you don't invest today and get benefits tomorrow. You invest in the panel over a 12-, 18-month period, and then the benefits come through at a later date. So there is a time lag, but that time lag ends in H2 next year.

So we're not guiding to 7%, 10% overall revenue growth, but we -- I did allude to the fact that we are focusing on growth next year. In terms of the macro impact, the way that I'm personally dealing with and the company is dealing with it is we'll go back to value delivery plan. The value delivery plan is about deep-rooted discipline. So if we see something that's not going in the right direction, we will stop it and stop investing in that area. So it's really acting and behaving like a modern tech company where when you see trends move, don't be -- stop and invest elsewhere.

So I think that discipline, if it continues to work as it has done in H2, we should overcome lots of other sort of external challenges that face us because we're being much more agile and adapting in where we invest, where we make headcount request where we invest in systems and technology as well. So nothing major to report on that. There's obviously some -- and I alluded to it briefly, timings of elections, they do impact us somewhat. And we are -- we've had a great 6 to 12 months in the new sector, the AI world, investing in us where maybe the agencies have been a bit softer. So we're continually making sure we're relevant to whoever needs our research going forward.

You're right to raise those companies. They're very interesting, and they are slightly different to us. So Listen Labs, as I understand it, does interesting things with companies' own data. And that's why it's valuable. I mean, I think there's lots of crossovers in some people as well. But principally, it is that they are valuable to Salesforce because of their interface with people rather than -- I mean, as far -- they either have not yet started or they've hardly done anything with synthetic twins, and I'm not sure if they're particularly interested in that area. But it's not the same offer. It's not well, I've described that. It's similar to -- if you look at Simile, Simile have a strong partnership with CVS and it's about how they work with CVS data.

In other words, how they fit into that relationship. So it reminds me more of what Qualtrics does as something engineered into a company's own customer systems. And then there's Aaru, which is different again. But in all 3 cases, they don't use -- they don't develop their own human resource, their own panels. Now that's critical for us. Our special sauce is that. We're not doing the other stuff, right? We are focusing on what we can do best. I don't think they can ever achieve what we're doing there, partly because of the way that even if they did try to build panels, their relationship to people would be probably a bit spooky. We have very strong open relationship, transparent relationship with our panelists. They trust us in a way that they might not trust a pure AI company that does other stuff with their responses.

We are very clear about how people's data is used. We give them lots of assurances that we can then follow up with very ethical behavior. I'm not saying the others are ethical. I'm saying that they're not able to develop those relationships. Our special sauce is -- our moat is that relationship with large numbers of humans sharing a lot, not just doing a few surveys, but actually sharing data, sharing even downloaded behavioral data.

I think we've got time for about 2 or 3 questions that have come in online. The first one is for you, James. What was the 49% metric of contractually secured revenues in the past 3 years? Is that with or without Shopper?

It includes Shopper.

That was a quick answer.

It was.

And then the second one also for you, why do we not have any new revenue targets given product stroke automation rollout. How much of FY '26 revenue is under pressure or disappearing as the market shifts to AI stroke synthetic products?

One, we didn't give revenue guidance because we felt giving bottom line guidance is more helpful. That's the honest answer to that question. But I have alluded to, obviously, growth very publicly a little while ago. So there's going to be growth, but we're not giving a clear revenue number at this stage. In terms of revenue under pressure, Stephan and I have alluded to quite a few of the shifts in the vertical sectors that are more in -- that are growing and they're shrinking. But I don't think there's going to be a material change in anything. And I think the one thing as I did touch on just now is even though agency revenue as a whole is down, agency revenue in Data Products is up.

So you could argue that, that trend is potentially beneficial to the value of the business as opposed to not beneficial. So there are lots of trends that are happening, but I don't see any step change that's going to think -- actually, this is -- the step change I do think we're going to continue to see is financial services improving further and the AI world investing further and the tech vertical getting stronger and stronger. And some of the less -- I would say, less modern verticals are willing to maybe accept slightly less good quality data may somewhat decline, but I think that the growth in the positives will more than outweigh any shrinkage in the negatives. And we're focusing much more on margin and revenue quality now as well than maybe previously. So in terms of value creation, that trend should definitely work in our favor.

Yes. And it's worth mentioning, I think I'm allowed to mention that the order book has never been bigger at this time of the year in our history.

Yes, I'm not saying it's 20%, 30% higher than last year, but it's a good few percentage points higher. And as I said, that's contracted. It's not just a floppy pipeline.

Thank you. I think we have time for one last one. This is one for you, Stephan. Can you give more color on refocus on Data Products? Is the separate sales team structure now in place?

So that is a reference to something we said 2 or 3 years ago when we said that where our strategy had got lost was in merging, you can sell everything. And we said you need to sell Data Products separately from custom in order to get the most sales because it's a different approach and it requires different skills. And that's why we set up this unit that I mentioned, which is adding this expertise to everybody else's continuing sales, but has its own program of growth and that -- which is dedicated to its core data partnerships. Some of those -- and that itself divides into 2. One is basically MCP clients and the other is Deloitte-type clients. And that is pushing out solutions, as they say, across all kinds of clients.

And the change, which I did allude to earlier today, is that the division between data and data products and custom is changing very much as a consequence of what AI allows you to do. So the contrast, you can now customize anything that we do as a data product, whether that's adding YouGov Voices to it, which are then customized to the needs of the client and asking the questions that are programmed for the client or whether it's synthesizing twins that you then validate as add-on research. There is no longer the clear distinction. And we are -- but we have changed a little bit of our organizational structure for our platform team and the research team, but they're working together much more than they would have done had I looked at this 3 or 4 years ago. So yes, we are pushing different kinds of sales through different kinds of channels, but also there is much closer cooperation between custom and product.

Thank you. I think that brings us to the end of the questions online. And I'm going to hand back to you, Stephan, for any closing remarks.

So just thank you very much, all of you for coming and really interesting questions, really interesting discussion. And I think YouGov is in a very strong position right now. I'm very happy to have 3 more months as I hand over to an extremely able successor who will be here next time. Bye-bye.

Hello, and welcome. Thank you very much for coming. Now I realize you're very much focused on the numbers this morning, but I want to start with this key message. Next one, there we go, that I realize, as I say, you'll focus on the numbers, but because I think so many people in the market believe AI is a problem for us, I want to emphasize this. We certainly believe there is great disruption in our industry, but this will favor YouGov, favor us, not harm us as we are investing in AI to maximize that opportunity.

So it's against that background of expected disruption that we have shown incredible resilience. We've delivered reported revenue up by 2% to GBP 194.8 million when most companies are flat or down. We're showing a statutory operating profit up by 14% and statutory profit before tax up by -- sorry, [ 14% ] for the revenue and profit up by 4%. Data products up 2% like-for-like, that is excluding a discontinued product and adjusted profit is down for the half year to GBP 24 million, reflecting essential investment in Shoppers data gathering methods and also investment in AI. Earnings per share is 11.4p and the balance sheet is solid at nearly GBP 33 million positive with a net leverage of 2.1.

Profitability, of course, is affected by investment partly to -- in shopper to keep it competitive and partly in AI to transform the company. Given what we consider to be pretty extreme undervaluation of this company, we do intend to have a share buyback after refinancing. And while the macroeconomic environment remains uncertain, clients are continuing to prioritize the high-quality human data and strategic research projects that are where YouGov continues to be strongly positioned.

This is a quick reminder that we had 15 years of growth driven by innovation. That is the DNA of this company. And as I will show you a little later, AI is driving some amazing innovation at YouGov. We've launched an add-on to brand index. Voices, renewals have been steady. And in the new year, as in the old year, we've hit record highs for a single subscription in this case, to a major tech company. We've -- in research, we've had strong performances, especially in banking and retail and in all regions, especially in America. A particular favorite of mine was a major study of perceptions of AI across the U.S. for Anthropic, which they released as a major public report. In Shopper, we've expanded and upgraded our panels, and we've added a new method for data collection, which was essential to be competitive in a changing landscape. And given the severe undervaluation of this company, we are conducting a strategic review of how Shopper best fits.

I now hand over to my colleague, our CFO, James Davies, but let me first say what a pleasure it is to be supported in our transformation by such an experienced, talented and determined individual. James?

Thank you, Stephan, and hello, everyone. I've enjoyed my first months as CFO of YouGov. This is a great business with passionate people around the whole globe. It has an exceptional platform and brand, although we are not optimizing its potential, and there is a lot of work to do. We are not as efficient and streamlined as I would like us to be. 5 weeks in, and I am seeing many areas of productivity and efficiency improvements that must be executed. These are all within our gift. We are not yet embracing modern ways of optimizing the value we can drive from our market-leading panel and trusted brand. This doesn't just relate to cost cuts, but also more efficient ways of working, collaborating across teams and countries and with a much sharper focus on how we actually optimize margin across our diverse product range. We're in the process of improving the discipline across the business in all of these areas as tools, market dynamics and competition have evolved.

My road map is filling up nicely, although my main priority is to focus on growth in the right markets, margin enhancement on a group-wide basis and to bring back agile ways of working. Yes, we are a listed business, although this doesn't mean we can't be slick and entrepreneurial, focusing heavily on maximizing returns on every penny of investment. I don't plan to spend too long looking backwards today. I want us to look forward, and I will be announcing shortly our value delivery plan. The value delivery plan is a full reset plan. However, I will spend a few minutes going back through the segmental P&L performance, along with some data points around our balance sheet and cash flow before moving on to H2 '26 and beyond.

As Stephan alluded to, the market reset and ag sector is in a tough patch at the moment and growth is being compromised for many reasons, including budget constraints. However, our product and reputation means we remain highly relevant, and we continue to grow top line by 2% year-on-year. There are not many businesses in our sector or adjacent sectors that can say that. Many are declining in high single digits, let alone growing in single digits. I particularly like the graph Stephan ran through earlier, showing 15 years of growth. That is very impressive stat for a business of our age in the sector in which we operate. Research has been a star performer with Data Products and Shopper stable from a top line perspective.

So moving on to adjusted operating profit. There are 3 themes. Firstly, when we compare statutory measures, not adjusted measures, operating profit is up a healthy 14% year-on-year. As a principle, like many of you, I do not like adjusted measures. It can mean a reader can't see the wood for the trees, although I am reassured that our statutory measures have performed well. Also separately identified items are much lower this year relative to last year. There will, however, be separately identifiable items as we progress through the value delivery plan, which I will talk through shortly. However, I commit that the threshold around adjusted measures will be higher and communication clearer going forward.

So theme 2, the Data Product division continues to perform very well with an even higher margin than an already attractive margin of 30% that we achieved last year. In this half, we were 35%. This is very encouraging. Theme 3, material incremental investment in Shopper of over GBP 3 million in H1. This is the main reason for the adjusted operating profit reduction versus last year. This investment does continue into '27 and '28. However, we do see revenue benefits hitting us in the '27 financial year. This investment is substantial, but it's imperative we introduce this now to enable semi passive and passive data collection as well as to expand and upgrade panels across Europe. This investment is intended to support its growth trajectory and competitiveness, and we're already seeing early success in improved client delivery and new opportunities. I'll come back to operating profit in a bit more detail shortly.

Moving on to revenue by region. 3 of our 4 regions drove revenue growth. Americas sector focus is proving fruitful. And despite the political calendar comp going against us this year, 2% growth was still achieved. U.K. and Asia Pacific also grew nicely. Mainland Europe is our largest revenue and it was flat year-on-year. The larger most strategic European countries performed well, although there are some of [indiscernible] for example, in Nordics. And as we go through the later we focusing on areas that we need to improve performance and some of the smaller European countries definitely fall into that category.

There is a slight timing difference for Shopper, but I'm confident that H2 growth will be faster and if not better than expected for the Shopper business. And we will return to year-on-year growth for the European region by the end of the year.

Moving on to customer base. Our customer base remains resilient. And from a sector perspective, nicely diversified. Technology remains our largest segment and continues to deliver growth. On some of the detail on this page is the [ FMCG ] aspect of Shopper. This page just shows YouGov core and exclude Shopper. So when you add on Shopper, this is an even more attractive picture.

Cash generation. Cash from operations is GBP 3 million times lower than last year. That is less than the reduction in adjusted operating profit. Cash conversion efficiencies are supporting this cash conversion measure to. However, I've been here 4 or 5 weeks, whenever I need to focus on more is to improve the cash conversion and focus more heavily on working capital improvements and smoothing as we go through the next few quarters. Our business is very seasonal from a cash flow perspective, as many of you know.

Moving on to the financial sheets. Since H1 '25, we paid down EUR 20 million of our term debt, which occurred in October of last year. The net payment is for the same amounts due in October of this year. However, I'm delighted to say that we have already commenced our refinancing exercise for both the term loan and our fully drawn RCF. Our debt ratios we net debt to EBITDA and interest cover are both well within the terms of the current loan docs. I'm not concerned about liquidity for this business, although I am looking forward to doing a refinancing which will better reflect the structure and needs of the balance sheet as to where we are at the moment.

Moving back to operating profit. Yes, I was keen to view 2 bridges. The first one is the H1 bridge. This is basically earning story H1 this year versus H1 last year. The [indiscernible] shows a GBP 6 million reduction year-on-year. Half of this is due to the additional Shopper investments already communicated. The GBP 3 million represents the incremental level of Shopper investments in H1 '26 versus H1 '25. In H2, a further delta is expected to be of getting up circa GBP 3 million to GBP 3.5 million with further investment in '27 and '28. But as explained, we do start to see returns coming in, in 2027.

I could divert a narrative, which says of a further year-on-year value also of GBP 3 million is due to a clear and specific investment program in YouGov core which would be delivered very clearly defining returns in [ 8 ] years. However, I won't do this, I see this earnings variance has been broadly in the normal course of business. Yes, there is investment in our platform and tax footprint, but there is also GBP 1 million additional cost to the significant progress we've made in the Shopper work stream. However, I class is actually GBP 3 million as ordinary course activities. So to conclude, [indiscernible] we are GBP 3 million down due to extra costs that have been incurred.

Moving on to H2. So I'm now starting to look at the future following the escalation of the past. Last year, H2 was flat with H1. This year, we will see growth in adjusted operating profits in H2. We are guiding to an adjusted operating profit outcome for the full financial year between GBP 52 million and GBP 56 million, which gets us into the bottom end of the consensus range. Let me talk you through the chart below on the seen, and I can explain why I've communicated the range.

Shopper investments have approximately GBP 3 million to GBP 3.5 million will be incurred in H2. We also have GBP 2.2 million of additional costs in the second half. This is a consistent theme in H1 that the year-on-year delta is due to shoppers TSAs and platform investment in the core business. Again, not specific enough for me to take out as an investment variance. Plus, we have a benefit of no less than GBP 1 million, which I'm calling margin improvements or value creation in H2.

What do I mean by this? I mean, as of now, I can identify this uplift is relative to last year. I have very high conviction of achieving this number. This number gets us to the bottom end of our range. My work on a more specific year-end outcome -- outturn is ongoing. And there are initiatives in flight, which could mean value growth margin enhancements could be up to GBP 4 million higher. This will get us to the GBP 56 million number. We will be working hard over the coming months to optimize where we end up. However, I'd like to be very clear that we are not solving for an optimum FY '26 operating profit. We have sold them for a value optimized in 2027 and beyond. They'll be very clear at the year-end along how and why we ended up with the prime position within this stated range. There at the year-end alone how and why we ended up with [indiscernible] imposition within this stated range.

I also wanted to highlight that by simply taking out the Shopper investments, the guidance for the year would be between GBP 58 million and GBP 63 million. I want to reiterate that I could take out GBP 3 million YouGov core to add to this number, but I won't. This is normal to business under my definitions.

Moving on to the value delivery plan. We have covered sort of numbers and the outlook is actually here, but I want to talk about 2027 and beyond because this is where it gets really interesting. I'd like to follow some of the details as Stephan will go through shortly. There are 3 ways to our value delivery plan. This is a plan that we've kicked off in the last few weeks. Second session below is going to mainly cover Wave 3 and part of Wave 2. But let me go through my version of each wave now.

Wave 1, as I mentioned, I've been at YouGov over 5 weeks, and I was very keen to ensure that we could land some key messages with conviction stay. I want us to be able to provide confidence that we are taking all appropriate actions to optimize value and earnings I do not want to sit here today and say, "I was going to do this. I will do that." I wanted to say that I have done something already with the full support of the board. So we've already actioned annual run rate margin optimization of GBP 2.5 million where we have eliminated terms of costs and several inefficiencies predominantly in nonrevenue-generating areas. This is real, and I'm delighted to say that Wave 1 of the [ VDP ] has been completed. We are just starting this new focus of executing your face, and this will continue.

Wave 2. This kicks off next week, which is needed to get the business back to where it needs to be. [indiscernible] today is where the solution to every problem is to increase headcount and further inefficiency and so that focus on accountability. This is stopping and will stop in its entirety very soon. I can [indiscernible] provide you an update on Wave 2 at t he Capital Markets Day in the summer. It won't be complete by then, although it will be reasonably advanced. The earnings uplift from Wave 2 will be a multiple of the value of the Wave 1 uplift and the shrink of this benefit will hit the financial year 2027. The combined impact of the first 2 waves is expected to deliver an annualized adjusted operating profit margin uplift in excess of 350 basis points relative to the margin achieved in H1 FY '26, 50 basis points relative to the margin achieved in H1 FY '26. This is once it is fully executed.

For example, if we manage to fully execute this before the year-end, the full benefits of this uplift will happen in FY '27. If it doesn't all happen for the end of this financial year, there will be elements of this investment within FY '27. Again, looking forward to giving more updates on this in the Capital Markets Day and at the full year results presentation in October.

In Wave 2, as you see from the graph, that's where we start to implement the AI transformation program. Wave 1 with nothing was over to do of AI, Wave 2 is partly to AI and Wave 3 is fully to do with AI. So moving on to Wave 3. This really is a exciting piece, and Stephan will go through this in detail shortly. This wave represents a material evolution in how we win, operate and serve clients. Wave 3 is expected to deliver a margin profile aligned with an AI-led data business, making a step change from our historical margin structure. I can't yet unify the specific financial shape this will result in. However, I'm starting to get an incline of what this could look like, and it's very exciting. We will be sharing further detail on this in due course.

I now like to pass back over to Stephan to go through Wave 3 and many other topics in more detail.

Thank you very much, James. Yes. So look, the AI disruption is not only about a big operational savings. The bigger picture is that the age of AI demand is exactly the kind of data that YouGov deliver so efficiently. And I'd like to remind everybody that it was YouGov that first build an engine for automated research data at scale. And by the way, we're still the only ones that do this. Since 2007, for 19 years, brand index has been selecting some running daily surveys, processing the resulting data and delivering into an analytics platform without any issue intervention. It's all automated that has never broken. That data goes into our data lake, which structured and corporate the cube and it generates a variety of products, including brand index profiles and rates.

So the single source of human data is our mode. Nobody else has it, and nobody else can do it quickly. And that's why I mean data disruption in AI will disadvantage most companies, but it will massively benefit YouGov because the advantage for us is inherent in our data generation engine. A couple more specifics about that disruption, which sit is very welcome. On the left, individual samples will be commoditized, people safe on those are panels just becoming a commodity. But we have to differentiate between supplying near samples, but having an engaged pan. These are different things. And engaged panel builds constant stream of connected data is not just like the [indiscernible]. [indiscernible] panel is a [ concall ] and behavioral single-source data system. The human operations that are required for our conventional research with YouGov, this will be 100% end-to-end automated. We already have [indiscernible] right now, and LLM has direct access to our system and can create its own machine research have any human intervention on either side.

The only human been develop panels -- has talking directly with [indiscernible]. And then there's a consultancy part, which is, of course, a huge part of the market research industry that has always been scalable and like most forms of consultancy will be directly replaced for the most part by AI. I guess some teams in using the system. Yes, expect deploying the data, yes, for consultancy whole. This Is a dynamic engine for premium connected structured universal human data. It's a utility for everyone to use.

I'll be quick on this slide. You already saw it 6 months ago, and it's operationalized now. So it's tens of thousands of people every day, not only cooking on brand index questions but then talking about the markets and adding the quality why data to the quality of what data. So you get the moving lines and the transcript of the [indiscernible] from the same Qs. And the first [indiscernible] for this was a [indiscernible] that we launched a few weeks ago and it has aroused initial interest, but it's only scratching the surface of what's coming.

This is the new operating model for generating the most relevant human data made dynamic by AI. It's a learning. The left-hand side of the diagram is the flywheel of the data generation, which has been going through '19. A global panel automatically serves surveys the ocular global panel automatically service surveys that going to the structured data lake. Number two, is the dynamic, the inflection. Number three the data lake cube, which is structured and contains all of that data in a form that generates trusted data products. Is number four, and that's served up into dashboards and an analytics platform. Now there are recuts as well and to AI systems. That big middle of the left, the 5 pillars is the YouGov system for generating our connected data.

But now we have the dynamic part on the right-hand side, a learning rule. So the right hand side -- data seek agent, which is -- which looks at the data and did what takes needed and an AI analysis system that works out what the uncertainties are in the data and what then is needed in order to fulfill that. And that puts us back into the flywheel as it well. It changes the dynamics, daily collection and means that an updated too.

So let us put it this way. Imagine you have a number of new straight trend line. And then new data starts to look at it both as a move away from that trend line to load assets is away from that trend line. That will be the detected, and it would mean we will increase data collection to make sure that our imputation model and in the consistency of the trends is understood and then corrected or update should be in the model. That will generate automatic alerts to users and will be a trigger for new research about. This is a way of a new way of seeing research, not as a series of surveys not as a series of a disconnected [indiscernible] of insight but a model of your markets that is continuously updated in an intelligent way, without you having to do anything and getting the alerts for what's happening at what's changing.

So that's the revolution that's coming, and it will be pioneered by YouGov, the Internet revolution in research was pioneered by YouGov. And this is what excites me and what is coming down the [indiscernible] faster than you might expect. But it all exists, and we're going to the next stage with it.

So finally, I'd like to introduce our new Chairman, and man with so much experience in our industry that I wanted him to succeed me as CEO when the time comes, but sadly he turned me down. Our new Chairman Ian Griffiths.

Thank you, Stephan, James. Good morning, everyone. I just want to close the few comments, recognizing where the business is also putting together some of the key themes that we've announced in our RS this morning.

But I would just like to say that when I joined the Board back in September, I was genuinely excited about the opportunity be part the team, the guest's is business back to delivering sustainable growth. Stephan has traded something rather unique at YouGov. It's a panel of fast and breath driven by data from real people. And as, as you said, it's almost impossible to replicate and is very different than anything else in the industry.

Now I recognize that this business and the shareholders have had a challenging couple of years. However, I hope you agree and almost [indiscernible] today's share price reaction. But what you've heard today does starts to set a different tone and sensor direction. Under James, there's increased grip and focus on our financials. We have a new value delivery plan, which we're executing at pace. We're getting much clear about how the business can be positioned as a leader or using Stephan's phrase as a pioneer in the age of AI. And we've kicked up a strategic review of our shopper business. As the next phase, the Board has asked the team to put all of this together into a new business plan that will be reviewed in May and it will form the basis for a Capital Markets Day later in the year.

And I believe probably the most important role for this Board is how we allocate capital to create value for our shareholders. And it was very clear even before today that there is a material disconnect between the underlying value of the business and the share price. As a result, the board has agree that annual return to shareholders this year will be by way of a share buyback rather than the usual dividend. I also want the board with more of a private equity mindset, one that really aligns the team and to shareholders. We've already reduced the number of net [indiscernible] and by the time of our AGM late this year, we'll be down to 4, which feels about the right size.

And then finally, for me, let me deal with the CEO succession because they were announcing the start of the process to find a permanent successor to step up. That process will take as long as is needed for us to find the right person. As you've heard today, there is no rush. When Stephan will step back into the CEO role, he sends off a series of objectives. And as you've heard today, we believe there's real progress delivering against that. And the pathway is an exciting value-creating future is becoming clearer. There's clearly a lot to do. There's new initiatives underway, a view on the direction that we've set. And I hope you agree, a real positive sense of momentum Pace is important but so is executing brilliantly. The Board is genuinely excited about what's possible for this business, and we look forward to updating you as our delivery progresses.

That's what I want to say at this point. I'm now going to hand you back to Stephan and James to lead you through the Q&A. Thank you.

And we do have quite a few questions coming in. Could I just remind anyone wanting to post questions to please put it in the Q&A. So the first question coming out is from Sean Thapar. And we have a few questions from Sean, thank you very much.

The first one is you are pursuing investment in FY '26, but are expecting the value delivery plan to deliver enhancements to profitability in FY '27. What gives you confidence that this level of investment is sufficient rather than requiring a sustained investment period over multiple years. Relating to that, what details can you share that give you confidence that there will be an immediate improvement in profitability from next year?

[indiscernible] First of all, thank you for your question, Sean. So is the investment in that -- it's like only been here a month or so, I think the investments in the Shopper business has been structured well. It's been -- it didn't start in the last few weeks or months, it started last year. So it's very well structured at has come through the required due diligence and asset from the central stage. I'm confident that the work that the investment is done is in the right way. And as we explained, that investment is predominantly a Shopper with the secondary identifiable pieces, the investment within the rest of the business at the moment on I'm keeping that in the sort traditional operating profit category.

I now move nicely answer your -- the second part of your question on margin. So yes, I've got high conviction that we will get to that 350 bps improvement during [indiscernible] but during FY '27. And that will be after to any further investment we may choose. So for example, Wave 2 to is not going to be about pure cost cutting. There will be some cost cuts, but we'll also be investing in different areas as well. So the net benefit will be the 350 increase in on margin, but we'll be thinking about how the business reshapes as we enter Wave 3.

Thank you very much. We have a few more questions from Sean. On the balance of investment, you have earmarked 6 million for Shopper and 3 million for YouGov platform. Given how ambitious you are being on the platform side, AI research, delivery, et cetera. Why is the largest portion of investment going into shopping now rather than -- why is the largest portion of investment going into Shopper now rather than allocating more to the YouGov core?

Well, I think that the investment in -- to the core part is the large amount. I -- our teams know what they're doing. It's actually very efficient where we generate efficiencies as we move forward on that. So about that. I think the investment in Shopper is about competing in a new environment, and it does mean some important new pieces are being built. That does feed back into our core data pieces, which is not entirely separate. The new method of methodology for gagging Shopping data that generates not only purchases in the shopping basket, but in fact, all kinds of [indiscernible] across a variety of different outlets, everything from gas stations to [ fit ] tickets, to visits to department stores. It isn't limited towards hoping supermarkets. Therefore, it provides a constant stream of verified purchases that can be that can lead to instantaneous interviews or interview people who just made those purchases, and that can be not to by [ Quanta ] but [indiscernible] as well, in other words, by conversation.

So it does actually open up new closely related products to the main zSpace product. I wouldn't see it as a sample altogether, I would say as part of the total.

We have a few more parts to Sean's question. Your strategic review of Shopper seems to suggest 2 potential outcomes for the business. either disposal or deeper integration. Your current investment appears to be a bet on expanding the business. Can you indicate which outcome you favor for the business? And how the investment you're making today aligns with that?

To be clear, we see some of that as important to the strategic direction of zSpace in terms of data that generates more and more updated generation. And one way that needs some certain is part of our strategy, but there are ways that we can do that with our Shopper like if that's as necessary. It wouldn't be something we'd be doing where we now so undervalued or nothing the shareholders must want us to consider.

Do you want to add something to that?

No, I just really want to reinforce the last point Stephan made that if it wasn't our share price position, we would not be doing a strategic view Shopper. The rationale when we bought the deal is exactly as strong now as it was then. And it would be wrong of us not to assess the potential returns of sale of shopper could do to the shareholder base.

So the question you raised, Sean, was about further integration or sale. That's spot on. We've integrated it to a certain level. We've cleared out pretty much all of the transition services agreements from the previous owner. And it is now in a nice clean self-contained space, and we will be making a decision over the coming quarters whether we do go for the big full integration or the value creation is better with another owner. But I would like to reinforce that we're not in active discussions at the moment. So this is not going to happen over the coming weeks or months. It will be a slightly longer burn. And the investment we're doing, we've got half -- it will enhance the sale should that be the route we go down. If we do keep it, it will obviously enhance the ownership of our ownership going forward as well.

And final part of Sean's question, could you share what programs are underway to deliver the value creation component of the FY '26 profitability plan? And what drives the variance behind the GBP 1 million to GBP 5 million contribution range?

Very good question. Yes, if we're doing this results presentation in 2, 3 months' time, I could probably be much more clear on not having a range, but there's quite a few initiatives we have actioned in the previous weeks, which will dictate where we end up in that range. But as I alluded to, and I think this is an important point, and I will -- please do hold me to this in the summer in October. We're not solving for a 2026 number. The waves are very exciting, and we want to make the right decision with investment as well as efficiencies over the coming quarters.

So yes, there are a few decisions we'll be making as a Board going forward. But as [ Ian ] said, we are going to be thinking more private equity like and returns based than maybe we have in the past. But yes, looking forward to updating you in more detail over the coming announcements.

Thank you. And now we have a question from [ Leo Mansor ]. He has asked in reference to underperforming products, what are these? And are there other products like that still?

This specifically refers to the residence or free wall piece that we were using for recruitment. We now have a much better way of doing that. We are recruiting. I think I neglected to look at my last slide where I talk about -- apologies for that. So I just fit in this very important part of it. But we are now using AI as well in the recruitment process and the recruitment process generates data, whether someone joins a panel or not. This is one of the really big advantages of our methodology that I don't know how I missed that, but it's alluded to in that last slide.

So we have something better, and we don't need a free wall part. It was not generating the value that we wanted it to. Things have changed, the way people interact have changed. and we are obviously modernizing our systems with that. That's what happened there. There are a few often strain -- what's the word stray little data products that have grown up over the years. Nothing consequential. But here and there, there are some things that we discontinued as they come out of contract, but they're trivial. It's nothing significant.

One thing I would just add, and I alluded to this in my section is we are very global. We have 52 offices around the world. And there are some regions that I think we can be more efficient in operating in such regions. And I think that's part of the reason for our European performance in H1. I'm optimistic -- very optimistic that growth will be there for Europe as a whole for the year, but we are doing a deep dive into how we operate on a regional basis for accountability and efficiency purposes.

And now we have a question from Jessica Pok. Do you have the skills within the business for Wave 3 of the value creation plan? Can we expect reskilling or replacing the staff?

Well, I definitely think that automating research operations must lead to, I wouldn't say significant, I would say, huge efficiencies. I think this is fundamental when other companies are talking about technical companies, tech companies are talking about this, and they're talking about 40% reductions in workforce. I'm not going to put that out there, but we are a data generation company. That is the most important thing we do. And that has involved quite a lot of heavy lifting inside those processes, which will be done by AI. So we are expecting really significant change in that.

Now obviously, we hope to grow in other aspects of our business such that it doesn't necessarily mean that all of those people are lost, but I do think it will be a skinnier business. I think it needs to be. I think that is the effect of AI and [indiscernible] to talk about it, but we must be realistic. We see great efficiencies in this.

And the second question from Jessica. Can you provide more color on the progress of YouGov voices and how it has been received by clients? What percentage of your panel is interacting via chat for YouGov voices?

So I'm not going to give a blow-by-blow account of that a couple of weeks after launch, but it was a very generative launch, lots and lots of conversations and better than that, but I'm not going to give that right now. But I would say that something like 1/4 of our active panelists that are actually doing a survey are going on to do conversations. That is within the countries where we're doing this, which is U.K. and U.S. I don't have the exact number because it differs from day-to-day.

And now a question from Michaels [indiscernible] . Michael, apologies if I haven't pronounced your name correctly. Regarding the buyback, consensus is expecting you to generate just under GBP 30 million of free cash flow. Is it reasonable to expect a buyback of this size? Or will it just be the equivalent of what the dividend payment was expected to be, i.e., closer to GBP 10 million?

First of all, Michael, that's a good question. The answer is there's an element of flexibility. One thing that I would like to reiterate though is we are doing a refinance. We are not going to be doing a refinance to lever up to do a share buyback. But as you correctly pointed out, we do have generate free cash flow. So there is an element of flexibility, but I would use as your base case as a starting point, an amount broadly in line with the annual dividend payment. But I wouldn't say if not, it couldn't be potentially margin higher should we feel comfortable with the cash flow at that point in time. And in terms of the analyst modeling, I would assume that this happens in H1 2027 in terms of timing.

On August onwards.

Yes, I'm still getting used to our year-end, yes, so August onwards.

And now we have a question from Steve Liechti. It's in relation to the VDP. And the question is, why no revenue growth targets? And one related to you specifically, James, are reporting budgeting systems good enough to deliver cost savings? And then the final part of this question is related to the AI learning loop. When is it in place and revenue generating? Is this a product thing or changing or changing all existing operations?

Great. First of all, thanks, Steve. And as always, great questions from yourself. And so first of all, revenue, I'm deliberately focusing on margin on disclosure of the plan at this stage in the summer and in October, we'll talk more about the revenue side because I feel at the moment with the AI piece in Wave 3, that's still bit still getting sort of finalized from our thinking. So at the moment, we're focusing very confidently on the margin improvements, but the revenue picture will become more clear as we shape out Wave 3.

In terms of your second question about systems, I work in many companies now and systems are never perfect. The systems that you got are not perfect, but do I have enough MI and real-time information to enable to deliver these benefits? The answer is most definitely yes. I don't believe we need to invest heavily in our back-office systems yet, but there will be some investments going through in the coming quarters to make sure we do have what we need to go through Wave 2 and 3.

And Steve, on the third question, a really interesting question, and you've clearly got what's happening there. So that divides into 2 bits. There's the learning loop that the AI that the LLM can do and that is now. So in other words, if somebody is -- if an AI or indeed a researcher looks at the data coming in and says or their own data that they have and says we need more, that system defines what is the data that is to be collected. They generate that through their systems into our system and get that data back. So that learning loop exists with the client AI, they can learn in real time, they can update their models in real time using our engine and that is by them. And that is today. That is not -- I'm not saying I talked about a prototype. It's a working prototype that's functioning. But obviously, we're learning from that functioning.

Now the second part is when do we do it for ourselves because you have that LLM bit in the other pillar, the other part of the pillar is when we do it for ourselves. And that really goes hand in hand, I think, with our research partners. They are our client partners. I can't really say more than that at the moment.

And here we have a question from James. It's in reference to Wave 3. And the question is Wave 3 references a margin profile aligned with an AI-led data business. Can you give us a target range for what that means? Are we talking 20%, 25% or higher?

That's a question I was hoping I wouldn't ask from being honest at this stage. I don't think I'm ready to give any number there, but it will be more than we're getting at the moment. So I know that's not overly helpful. But please do bear with us. And as soon as we feel comfortable giving a range in that level, we will do so.

A question from William Larwood. Are you seeing pricing pressure in the market? And if so, which areas are you seeing it?

Let me start off and then Stephan can add to. So I think the one thing which I was pleased that when I first joined was I double checked what our order book look like and our pipeline look like from a client perspective. And I was particularly pleased that -- and I think we got this in one of the releases that we are 80% covered for the full year already. And when I say covered, I don't mean order book, I don't mean pipeline, I'd be contractually covered. And that is 100 basis points higher than where we were at this position before. So that gives hopefully an element of comfort that from a revenue perspective, we're feeling pretty comfortable.

We're not being complacent, obviously, but we're feeling comfortable. And obviously, within that number is both price and volume. So I think hopefully, that gives you comfort that pricing pressure is not something that's hitting us from all sides at the moment.

I'm just going back to a few additional questions from [ Leo Mansor ]. So we have a question related to Shopper and the investments we made. What are these investments?

So these investments and Stephan can probably answer it's basically changing the way that our panelists collect data because one thing I've learned in my very limited amount of time in the market research sector is you've got to make it easy and clean for certain cohorts to provide the information you want when you want it. And we all know from previous sectors I've worked in user experience is absolutely critical and consumers now compare [ UX ] across all sectors regardless of what that sector may be.

So really, this is about introducing semi passive and passive data collection, upgrading panels across Europe and the investment is to make panel more effective, more efficient, more timely and deeper. And that obviously can create many benefits to the big customers that we have in having them in Shopper.

Yes. I think we went over the key part of this, which is the methodology for data collection moving from scanning to receipts and downloaded data uploads and downloads. This is a new methodology, new technology that is in the market and that replaces or adds to the other methodologies that we have. And that requires not only the tech of that, but also the interpretation, the translation, if you like, of the data from something that is incomplete to something that is more complete. I was mentioning, I think, at the SKU level stuff, there's an investment there. And of course, a significant part of it is also in the cost of the new dashboards, I should say, platforms on which the data is delivered.

And 2 more questions from Leo. The first one in relation to our business in Asia Pacific. And the question is, can you explain why your business in Asia Pacific is not profitable? And what is your strategy there? Is it the same offer as in the other geographies?

Well, it's always been a difficult market for us. I can't say anything particularly smart about that. There are parts of it which are, in fact, very profitable and other parts -- other countries where we haven't done well and some difficult countries, as you can imagine, some large companies where we are struggling to not to be so involved. So it's -- I haven't got to say there other than the strong parts are what we will focus on. And as James has already mentioned, there will be geographies that we may not want to be in physically anymore.

And one last question from Leo related to panel development. What is your strategy there? When will you have enough panelists? Didn't you reach the right size with 34 million panelists?

Well, it becomes a very different calculation when the process of recruitment itself gathers data. So this is actually pretty important because a panel must always have fresh minds in there since quite a lot of what we do has to do with awareness. And you can't test people for awareness of things that you've already talked to them about several times. And so you are constantly refreshing your panels and growing them as you have more to do and as you're selling more, of course.

So the process of the recruitment is now part of the value generation of our data. So it can't be viewed in quite the same way. We want to keep having new people involved. We also are changing the experience of being a panelist. It won't all be about paid panelists. It will be a lot about unpaid panelists doing it for the experience of being part of a panel, and we found that in some areas, that's better in other areas, panels than necessary. This is actually a core strength of YouGov and a core challenge all the time is to change with the times. So it isn't now exactly as it was 5 years ago.

Thank you. And we have a question from Johnathan Barrett. It's in 3 parts. The first part, can you tell us what price uplift you achieved in data products?

Well, we have not been increasing the price of data products significantly over the last few years. And we feel we need to be doing that, and that's part of James' review, and that certainly is an important part of that. The new data that we're adding, however, will, of course, bring extra revenue to those products as well.

Second part of the question, how many sales of [indiscernible] have been made? And what is the pricing?

Well, as I did say, we just have been there for a couple of weeks. We'll wait for full year.

And the final part, when will the end-to-end product be released? And I think you may have touched on that.

The end-to-end, meaning the end-to-end automated research.

I'm assuming [indiscernible] product.

Yes. So that is what I mean. Well, that is developing all the time. I mean we are running end-to-end [indiscernible] on a case-by-case piece and the will take -- it will keep developing. It's an ever developed product.

Thank you. I think that brings us to the end of our questions for today. So thanks very much. Thank you to everyone who submitted a question.

Thank you very much. Bye everybody.

Good morning, everybody. Thank you for coming and thank you for coming at first -- my first one back. And I hope that it won't be too long before we see the rate of growth we've had before. We're at 388 -- GBP 389 million at the moment with a 16% margin, with 8% increase in reported -- in adjusted EPS, and -- sorry, in reported EPS.

And the key thing here is that we're showing stable growth. Now stable growth sounds a bit of a contradiction, but actually, it represents the 2 things we're trying to do this year. One is to return to stability. And that means fixing things. And the other is to invest in growth. So we're not seeing yet the kind of leaps that we have had in the past, but we are investing for that very, very thing.

And just to remind you of that story of YouGov's growth, there's a huge graph before this, which shows something like 10 years of growth, and we've just come off that. And we need to be reminded that this is fundamentally a growth company. And it's a growth company because we have always been led by innovation. And when we stop innovating, we go flat. But we are back on the road to innovation, something that I will show you in the second half of this presentation when I talk about the new methodology that we have produced.

So in the last year, we've had some good successes, which is the stabilizing part that I've been talking about. We have continued rollout of ID verification on panelists. We spent quite a bit of effort focused on -- we're moving forward from panel. This is something that has been bedeviling the industry as a whole. I think we're a long way ahead of everybody else, partly because of our historical asset of a well-embedded panel and partly because we have been using the latest techniques, and I think we pretty much lead the pack on the reliability of our data.

We've invested in our Cube powered products, especially on the data science side. A couple of days we'll be announcing a new addition to the team, a very important addition, someone who has led an important team for 10 years at Nielsen, really representing the seriousness with which we're taking the data science side and growing that to create the richness and the reliability of the entire Cube data.

We've also established on the client services side a team that specializes in selling and educating clients about the value of our connected data, our data products. And we've continued our program of updating our dashboards, including putting AI into those to help create -- to help with discovery.

And finally, for this section, we have done a pretty good job, I think, of integrating Shopper, it's a major job that was and it has been successful and Shopper is actually doing a little better than our expectations were. So that's a pleasant change.

So with that, I hand over to Alex.

Thank you, Stephan. I just want to do a quick overview of our lines of business. I just want to point you to the stack charts on the top of the screen. We've gone from GBP 335 million to GBP 388 million -- sorry, GBP 389 million revenue for the year. You'll see the biggest contributor to that. We've got a full year impact of Shopper coming through.

On an underlying basis, you'll see the 2 divisions, core YouGov growing at 1%. I think I want to make a -- specifically point out data products. We've turned that from a decline in the last period to growth. It's a lot of investment and a lot of focus that's going into getting us back on track. It was a key driver of our performance for the previous reporting periods that Stephan referenced in terms of those double-digit growth years. And I think you'll see the beginnings of an evolution of things that we're doing in that space.

I think we're pleased to see we've had renewal rates normalizing, back up to 82%. And the couple of wins in the media agency space, that may be a little bit of surprise, people saying we are seeing a little bit of weakness as well, but it does go to show when we have high-quality data, there's still a demand for that, and we had a significant win in the retail space as well.

In our Research division, a bit of a mixed bag in performance. We've seen some headwinds coming from our government sector and our gaming sector. Gaming has been a long-term decline for us, but we saw some real strength and demand in our academic technology and financial services sectors. Shopper just referencing what Stephan said. On an underlying basis, I mean we don't have this in our numbers. But if we were to look at it on a trailing 12-month basis, it's growing by about 4%. So we're pleased with the way that has performed.

I want to make the point is a period of coming off the TSAs under the ownership -- under the sale from NIQ. We're now off the majority of those, a lot of heavy lifting, getting control of the finance systems, et cetera. And so it's been a period of lots of, in a way, disruption moving systems, et cetera, but we're really pleased with the way the teams have continued to perform. You'll see our profit on the bottom of the chart has increased from GBP 49.6 million to GBP 60.7 million. A big driver of that is contribution of Shopper, obviously, but also the amount of cost that we took out at the beginning of the year -- referenced that at the beginning of the financial year, we announced that we made -- we had pressed the button on GBP 20 million of annualized savings. Because of timing, we realized about 70% of that in the year.

Just moving to a geographic analysis, a bit of a mixed bag in terms of performance. Europe, you'll see year-on-year growth is 0%. Part of that has been some headwinds that we've had within Switzerland and Germany. We're starting to see some improvement coming into the second half on that. In the U.K., which has historically been a strong driver for us, a lot of disruption going through the redundancy programs. We started that on the 1st of August 2024, lasted about 3 months. And so it was inevitable that we would see a slowdown in performance there as we went through the consultation process. But we've ended the financial year really strong, good trajectory going into the financial year.

Areas of growth for us has been Americas. It's always been our big focus. We'd like to see that growing at a much faster pace, but 3% on an underlying basis is broadly in line with how fast the market has grown. And just a small point, Asia Pac continues to grow by 2% -- this chart, which is looking at our sector. We take out Shopper in this because it's so skewed to FMCG and retail, but we continue to be very well diversified. Technology remains our largest segment, and that's a combination of technology clients using our data, but also using more traditional market research type services.

Good contribution from banking and insurance. Travel and tourism has picked up again. Retail, I mentioned academic coming through in research. I want to make that point again about shopper. Just moving on to higher -- our cash conversion and our cash capital expenditure. For the year, we remain about the same cash conversion ratio as the previous period. We've had a bit of working capital outflow to do with -- we've had a bit of accrued income increasing. We've also seen panelists redeeming more points this year, and that's in part, we're running a lot of surveys, particularly in America off the back of the U.S. election.

CapEx is down slightly. You'll see we spent a little bit less on panel development. That's not necessarily we haven't been getting more panelists. We've been a bit more efficient in how we -- in our conversion, and we'd like to see that improving over time. And we've kept our investment in technology expenditure roughly flat. That's not to say we haven't increased the amount of people in our technology teams. We have been spending a little bit more time on maintenance. And I think when we get to the latter parts of this presentation, you'll see some of the things that they have been working on, which will drive some more performance into FY '26 and beyond.

We end the year in a robust balance sheet position. We started the year with a EUR 240 million loan facility. We paid EUR 36 million of that down in the year. We have a EUR 40 million RCF, which of currently EUR 24 million is drawn. We made an adjustment to our amortization schedule in terms of payments. And we had a -- we negotiated particularly aggressive for us. We wanted to delever as fast as possible when we took the loan. We're not trading at the same levels we were before. So we've reduced our payments to EUR 20 million for the next 2 payments, EUR 20 million in FY '26, EUR 20 million in FY '27, just that we have the headroom to continue investing in the group. And again, we really want to get ourselves back into this growth trajectory. But I want to make the point, we remain well within our loan covenants throughout the year.

Just moving to current trading and outlook. You'll hear us talk about investing. It's particularly important for the group that we are taking on this market. We used to be the challenger brand, and we certainly see we have a right to win in a number of spaces. So we've got a clear set of execution priorities that we have around panel and product innovation. We've got some investments that we'll be really focused on data science and product development people, which are moving us toward our SP3 strategy of being more of a platform business in the way that we go to market, the way that panelists and our clients consume data. And we're starting to invest in Shopper. And the idea around Shopper is to get their capability expanded in the markets they're currently in, filling out more of the European map. And over time, we'll be looking at how do we invest into getting Shopper into the U.S.

For our trading currently has started in line with expectations. I think for FY '26, we expect to see modest improvement in revenue and margin, and that's after making some key investments, particularly in data scientists and technologists. But I think we start the year, I think there's a lot of compelling opportunities for us. I know it's a slightly challenging macro environment, but we're certainly seeing some good opportunities from clients coming into the financial year.

And with that, I'd like to hand back to Stephan.

So yes, I mean, I said the YouGov story is growth through innovation. That was the promise that we made at Capital Markets Day in May 23. And the strategy that we put out there is the one that we are following. We are back on that road to growth, I believe, certainly on that strategic road.

And that involves these 5 things: the renewed commitment to increasing visibility and quantity of public data. As you will see in a moment when I demonstrate the importance of public data to us, that is something that drives our reputation, our trustworthiness, our panels and a lot of other stuff. And it's something that we are highly committed to and we have increased our spending on.

Innovation around panel recruitment and management of panel. We are changing the panelist experience. Again, you will find always this emphasis that we have on public data on panel, creating data that creates products that are good for clients. This is all part of a flow. And the way that we treat our panelists and the way that we get data from them is being enhanced.

We're accelerating the execution of becoming a data platform. More and more of our dashboards are now containing AI and better ways of utilizing our data. Custom research is a huge part of what we do. The degree to which custom research is aligned with our platform is the degree to which our success strategy is working. Those two things being aligned is absolutely critical to us, and we've been putting energy into that. There are aspects of the custom research offer that are not so aligned. We need to bring everything in line. And AI is helping us to do that. And of course, something that we'll be focusing on in a moment, in fact, in the next slide is the innovation in AI that is massive for us leveraging the value of the assets that we've built.

So that is the broad strategic view. You've heard it all before. There's nothing new in there other than that we are updating all of that with AI and we're coming at it with renewed enthusiasm.

Now YouGov in the Age of AI is the big question that anybody would ask. And we, I believe, and I hope I'm going to show our company that is ideally suited to use this moment, this historic revolutionary moment for our growth because we are all about talking to real people. And the essence of the use of AI is real people. It is building extra value out of the real people in order to get even better data products, even better value to clients.

And that is something I'm going to come to several times because we think that our industry has maybe gone a bit wrong in some areas. There are so many wonderful things that AI can do. Replacing humans isn't really the job of a market research company. There's lots of great things that synthetic data can do to get more value to make it easier to do things like ad testing, and there are a lot of places where that really works well.

But remember, the vast majority of spend from our clients is in measuring change. That's what people are interested in. And change cannot be extrapolated. Change -- extrapolation is the assumption that things will be the same. Every time you use synthetic data, the underlying assumption must be that things are the same way you're extending into. That is the definition of synthetic data, it's extrapolation. And extrapolation tells you what you already knew. It extends it, but it doesn't tell you where the surprise is coming. And that's why people buy tracking data because they want to know what's changing, that they don't already know. If everything is going the same next month as last month, then it's all nice, but that data isn't very interesting. It's when the change is not what you expected, and that's not going to come from synthetic data. So we are actually very interested in synthetic data.

As you know, MRP has been a very big part of our success in accuracy and getting more value out of our data. So we are actually pioneers of synthetic data. And it has fantastic value. There's nothing against that. But the vast majority of the market research spend is in tracking change. And change, you need real people. And real people are the basis of everything we do.

So a data company focuses on the flow of data across 4 things: people, data, process and output. And for this to work, we have developed over the course of 25 years 3 major assets. First of all, YouGov has the best panel. We don't have the best panel in every single country, I wouldn't want to pretend that. But in our major markets where we have our strong panels, we have the best panels. Everybody knows we have the highest contact rates. We have the highest levels of representativity. We have the engagement that keeps them there for a long time so that you can build layers and layers of data. It's by building layers of data from engaged panels that you get the second big asset, which is that we have the best data. And it's the best data because it's a single source, it is connected, and it is always recent. It's always being updated. Every single day, it's updated. Recency, representativity and genuineness, which should be a given, but isn't these days, are the things that make great data. To have that, you need good panel that gives you the best data.

And I'm putting there that it's also across different areas of demographic data, things about the people, what they're thinking, attitudinal, behavioral, passive data, that's part of that. Qualitative is the bit that's going to be a big new piece, which I'm talking about in a moment, added to quantitative.

The third area is our incredibly strong brand. And it's always good to have a strong brand. But for us, it is key. It is a key function of what we do. Because strong brand yields better panels and builds trust amongst clients. And I have 2 examples here that I want to just run.

[Presentation]

The importance of that for us is Trump says that assuming you know what YouGov is. And if you don't know what YouGov, the name still implies it's an authority. That phrase "according to YouGov" is incredibly prevalent in the media. It is what we want -- I used to say Google is to Google. According to YouGov, is our talisman as it were for this. And Meltwater tells us that we are the most quoted company in the world's press. There are over 1,000 mentions about us -- of us, of our brand every single day. The total number of -- is in the small 385,000 mentions in the last year for the YouGov brand. And you can imagine, this is a value in itself.

We're also ranked #2, and this comes from the next -- this one and the last one are coming from independent research. We're ranked #2 for aided brand awareness globally among research buyers. And amongst those, switching to the last one, we are the most trusted market research provider. So even when we're not the most famous, we're just #2, we are the most trusted. And anybody I think in the industry would say, who is the most likely to get a result, like it's YouGov.

And these are really fantastically important assets. A strong brand is strong reach. What isn't in here is -- sorry, I skipped it, is we have 4,000 active clients. Now all of those active clients, obviously, people that we can talk to and people we can show our new product to. And you could say this slide is a massive strength. And it's also, in some ways, an indicator of we've got a hell of a lot more assets than we've managed to convert to value. And so we know what to do.

This is a massive asset. This is stuff that you can't -- you can't create this quickly. This trust, this reach, this visibility. And all of it will feed into the new products or the new methodology that I'm going to show to you. So what changes about all these assets in the age of AI? And it is a revolution that's happening. But for us, it's very much an evolution because everything that AI allows us to do is an enhancement of assets we've already built.

Our mantra from Andrew Ng, who was the founder -- co-founder of Google Brain, really fantastic quote for us, "It's not who has the best algorithm that wins, it's who has the most data." And the other people say, oh, most data is, what's the best data or the best insight, all of that. All of that stuff just emerges from the quantity of data. Genuine human data at very high scale creates good data, it creates good insights, all flows from that. There's no shortcut to the value of really large-scale data.

And that is what the whole world is turning on. While other people are trying to cut out the real sources of this, trying to say, hey, we can make more money by not bothering all these humans. We are saying no, it's all about the humans. It's all about the number of people talking to you, how much do they talk to you, how much do they give you? And that's what AI lets us do.

And AI enables us to do data collection and discovery at scale. Data collection actually, you didn't need AI for until now, but we are talking today about qual data. And qual data is a different type of data. And it's a type of data that we haven't done much with. It's a type of data that is the Cinderella, if you like, of the industry. People do this as a good way of getting insights, of brainstorming and so on, but you can't base big decisions on qual data because it's touchy-feely stuff, right? It's not stuff that you can create a measure out of. Well, that changes. That changes when you have AI to, first of all, use the background data to choose the right people to talk to and to know what to say to them.

And then to take all of this unstructured data that's produced by interviews held by AI and turn those into data that the clients can actually use. It's not enough to be interesting, it's not enough to be good for a brainstorm. It has to be things that you can use and base decisions on. And we are now doing thousands of interviews driven by AI on a daily basis.

I'm showing you one example of this. Now I'd love to really -- not doing any demos here because you can't really demo this stuff. So I'm going to show you this one thing, which is a snatch of a conversation. And I'll just -- you probably can't read it, so I'll read it out to you.

It says, I've noticed you've given top ratings to quite a few music artists recently, everyone from Rick Astley and Hall & Oates, to 50 Cent and Pussycat Dolls. They each got 5 stars out of 5 from you. What shaped your views on these artists given they span such different musical styles?

Now this is a question that the bot came up with that was not based on a prompt of us asking them about anything in particular. They have the background data, the panelist, they were definitely told to talk about music, but they got the bot. The bot got the -- found something interesting in the data to turn into a question.

And the answer is, I grew up listening to and appreciating music from different genres and eras. Bot comes back and says, what first got you into such a wide range of music? My mom, school friends, going to gigs and music channels. Which one of those -- which one do you think have the biggest impact on shaping your taste in music? Music videos in the '90s and the '00s. It goes on and it can go on as long as you like. But you've taken previous data, turned that into a relevant question that's targeted at this person. They know that you're listening to them. They know that you know something about them. And that's why they're here, by the way. It's not creepy when we do it. It's creepy when Google or Facebook or whatever does it because you didn't ask them for you, you didn't come there for that. You come to YouGov to be listened to. So this is listening to. And it's responding to and it's getting you in a conversation and it's coming up with an insight. And that can be used, built on in lots of different ways.

Now we're not doing one of those. We're doing literally thousands of them, thousands that would cost you -- you could never imagine the cost of just this 20,000 conversation study that we're doing previously. And it is a very low cost. I'm not going to tell you what the costs are now because we'll have a Capital Markets Day before too long and we'll go through all the things and our expectations and things. I'm just showing you a new methodology.

This is huge scale at low cost. It's automated, customizable, configurable, continuous data collection. Only YouGov can do this. Nobody else has the combination of things that this requires. This requires a large connected data. Imagine that bot going into the 2,000 or 3,000 things we know about a typical panelist and being able to use that and find the interesting things there to maybe open up a discussion, or to look for the particular thing that the client wants. Maybe the client is only interested in their supermarket habits. So the bot goes into there and finds anything you can find about supermarkets and takes that at a starting point. Only YouGov can do that, because nobody else has the range of connected data with live panelists now. Nobody else.

And then only YouGov can do the scale of continuous questioning, so not asking 100 or 1,000. We can do 20,000, we can do 100,000 interviews a day. And we can do them at this scale because we have highly engaged panelists and they come back. They're not -- of course, there's a lot of churn, but our stable panel is with us over time, and we can build up a relationship and we can build up all of that data. So this is our right to win. It is the assets built over 25 years, the best panel connected at scale, the strongest brand and now adding the AI. All of that comes into something that is unique to us.

This is a slide that attempts to encapsulate just in one picture what we're talking about and really what we're adding. So over here, we have, on the left-hand side, we have the world of things, the entities. YouGov, as you know, covers over 20,000 brands and products in our tracking. When you combine brand index and ratings, it's more than 20,000. But we say that because it's a changing number and ever growing. But it's musicians, it's TV shows, it's media products. It's supermarkets, it's brands, it's consumer goods, everything that you can think of that is in that commercial world that you might want to track is in our database, is in our Cube and it's all being processed through all of these people's heads.

That's what's happening here. They're living their lives. And they come into YouGov and they ask questions and they become obviously noughts and zeroes, and that creates a line. And that's brand index. And brand index was the first, is still the only reliable daily measure of brand strength. And it goes up and down. And you really need to know that. You need to know a lot of companies put this stuff into their risk -- I mean, for example, Bank of America, it's embedded in their risk modeling. It's part of their understanding how news flow affects their accounts and new accounts opened and accounts money taken out and so on, is predicted by reaction to news flow measured by YouGov.

But what this doesn't do is it doesn't tell you why something has gone down here. You might know why there might have been some incident, and you know why already, in which case, you might want to know, okay, how does it bother people? Who does it bother? And why? Or maybe you have no idea. There's a trend line, you say, I don't know why it's going down. What this new data does, of course, as you've guessed already, is it gives you the why, not the what. I can't remember if I mixed that what and why. But this is the what's happening. This is the why it's happening. It's in here. The way people are talking about your brand, you can be very specific and say, have you heard anything about Tesco lately, to try and pump that, or you can just say, what do you think about Tesco, or which is your favorite supermarket? You can decide how you want the prompt to go and generate these conversations, and then you can find out how are people talking about you.

Then you can do several things. You can compare and contrast things within the data and -- within this data, but you will also take all the previous data that you've had because, if you're a Brand Index customer, we'll have a bank of sort of background hum data as to know what the normal conversation is like, and you can compare the normal conversation about you to the conversation happening today and find out what is it, what's driving this change?

There's going to be a huge amount of value in this that we have yet to discover. This is like a whole new treasure chest. But just it's not abstract. After this, I can't demonstrate it to you here, but after this presentation, this goes up online, at the end, you'll find 2 links. One is to about 20 transcripts and the other is to the functional output. Now it is an output for one study. So all the buttons don't work the way -- I mean, it's showing you how it would work, but it's specifically around one study. But you can play with it and see.

Because if you have all of this vast data coming along and you don't have a way that it turns into something usable, then that's interesting but no good. So obviously, there's a lot going on there. And really, it's very -- it's delivering real value to clients. It gives the why and the what else to the what that we've already done. It's automated, customizable, targetable and actionable. That is to say, it is really a custom thing as well as a product thing because you can turn it onto anything. You can have a single study from it or you can have it on all the time.

Its scale reveals the long tail of new information. It isn't just the thought things that you -- and this is kind of under the next thing, it isn't just the thing that you thought you wanted to know, that's the known unknowns. That's what a survey is. You know what you were trying to find out and you write the questions for that. This is the unknown unknowns. The long tail. The stuff is -- what are people talking about? How are they talking about these things? Does somebody, maybe one person in that conversation come up with something anomalous? The AI will surface that and you can find out things you didn't know.

And the last bit, all of those first 4 things, of course, happening already. This isn't just a plan. This is actual delivery. But the last part, the alerts, we have not got to and that's something we're productizing. So the idea is as this flows along and you're getting actually just open-ended questions to sort of pick up the continuous hum of chatter -- by the way, not the same as social listening on social media because the whole point about our panels is they're highly representative. They represent all the subgroups of a population and they represent them fairly. So when you get this hum, you find out what people are actually talking about, not what's on Twitter or whatever it is. That's not -- those are not the same things.

So you get -- you look for the -- across this entire horizon and you will get alerts to say, "Hey, here's something you might look at, something that was unexpected."

This significantly enhances the values to our data products, I should have actually said, to all of our outputs because you can do this to a single survey, if you want. This is not simply a new product, although it will exist as a product, you'll be able to do just a study of this based on conversations. But it enhances every single data output we have. Everything that we do that was a what becomes a what and why. And that's why this for us is a major revolution. It is going to, I think, have as much importance to us, the qual side as the quant side. And that's what's new.

So we've got these 3 dimensions: the sheer quantity of data, the recency, the daily collection, and we've got this massive range. Nobody else has the range. Nobody else has the quantity. Nobody else does the daily collection. And you can say, well, you add up all these assets and you add up the stuff that I talked about before about our reach and our trustworthiness and so on, and then you may ask yourself, why the hell do you only make GBP 388 million? And I do think there is a massive gap, and that's something that we really have to address, how we do better at teaching people the value of our data. So that remains something that we are -- that we have ideas about but that we are working on.

The last thing about this slide, this data is an ideal for processing and analysis by AI. I've said that AI, I think, is great for some things and not so great for others. This is right in its area of strength, taking large amounts of unstructured data and turning it into something meaningful is what it does like magic. It's like when we first saw ChatGPT talking. You can't really work out how it is. It isn't really the algorithm. It is the sheer quantity of connected data.

Right. Well, we already have our first paying client. I have to say it's a tiny, tiny alpha version of this, but very good. So we already have engineered into the system into Brand Index, that if you want a daily collection or an occasional collection of open-ended data, you can trigger that, and you get that every day. And the version we have now is simply one question, why did you say that? So you've given Tesco or whoever it is a good rating or a bad rating, and it comes up and says, "Why did you give us that rating?" And that adds up to a really useful continuous a little bit of insight that we add to the Brand Index subscription. And that's just had its first subscriber. It's only been out -- we've only been talking for a couple of weeks. We have a lot of clients lined up for further discussions there. But what we're obviously really talking to them about is that question becomes a conversation. And that will be engineered in. It will be ready by Christmas.

Just a last couple of points. We've talked about one very important use of AI, but we're using it across everything we're doing. It's helping us with fraud detection. And I think that we will be the -- we are the leaders in genuineness of data. It allows us to do new types of data collection at scale. As we've seen, it does data analysis for us. It does discovery and interactivity on our dashboards. And finally, it also is being used, we're working with a couple of LLMs to turn our data into usable things in search and so on so that the public side of our data is inserted in its best possible form inside the infrastructure of search, because we are a trusted source of data. We want to maximize the value of that data.

Everything that we do, everything that we do for ourselves, our proprietary data, is available for free in top line form. That doesn't, in any way, hurt our products, I believe, because you always want the detail. No marketer just wants the top line. But the public is interested in the top line, like the President. It's valuable, it's used almost always in its top line form. And the more that's available, the more it teaches about the data that we have.

So all of these things add up to YouGov becoming an AI-driven data company built on real people for all society. As I said, at the end, when you look at the end of this presentation later on, you'll find 2 links, one is transcripts, one is to the interface. It is obviously in a curtailed form. There's also a video to watch that's being added. And we are ready for your questions.

Will Larwood from Berenberg. Firstly, just if you could provide some color on sort of the visibility for the top line in FY '26. Obviously, we've got the key renewal period for data products in November and December. That would be great if you could share a little bit more detail on that.

And then secondly, in regards to sort of pricing more generally, how you're thinking about that in FY '26 and potentially beyond?

And then finally, just -- is -- do you feel there's anything further that you need to do in terms of, from a commercial point of view, there's obviously been some change over the last, say, 18 months or so, particularly on sort of both the CPS side and the data product side?

I'll take first 2. I'll start on visibility. So I want to point to a couple of things. I'll pick on the U.K. We've got Will, who's the U.K. CEO here. We ended the year quite strong, in particular, building momentum into the second half in some of the markets that, in the first half, it underperformed. And so we go into the year and we have talked in the past about our backlog, the committed revenue that we have coming into the year. We came in 3% higher than we were last year. We're just a shade under 45% this year. We were just -- we were up 41% last year. So we're seeing that backlog increasing. So we've got fairly confidence on a sort of good performance in the first half.

We're not talking -- I'll make this point again, expect modest growth. We're really looking at how do we continue doing a lot of work that's happening under the hood. But I think we're moderately pleased with that. Obviously, it shows some strength coming into the year, particularly with the macro environment. And so I think looking forward to the renewal season. For us, that's typically clients take a data product renewal from the 1st of January, and so November and December for us are key months to make sure that we're getting on top of that. We've amended the way the team structure works. We have a dedicated data product team back to the old model of a team that's really incentivized and focused on those renewals, getting those renewal discussions early. I think having some interesting things to talk about, new developments, in particular around this capability that Stephan's pointed to, shows we should be garnering more interest in that. So we're quietly confident on that.

A little bit on pricing. I mean FY '25, we didn't touch it, there was a lot of change going on in the teams, and in particular, getting ourselves set up for changing some of the incentive structures in the 1st of August, which it's very hard to change the incentive structures midyear. We are now putting through. It's a relatively small thing, but we are pushing through inflationary price increases. That's something that we hadn't pushed in the last sort of 18 months. That's when we just started. And again, coming back to our peak renewal season, we should see some of the benefit coming from that.

I'll pass to you on for the...

Just one thing I wanted to say about pricing, because you may have some more to say. I just wanted to say that there's something in the -- in one of those slides that I could have expanded on, and I thought we've already spent a fair amount of time on it, that one of the outputs of a data company is a Data Lake with APIs or an API. And we haven't done a lot of that. We have, in fact, got a number of clients who just take a feed of the entire Cube. But if we're a platform company, we won't be always just thinking of selling this product and this project and you have to come in at this high level or you don't get anything. In fact, it should be the opposite. You should be able to buy exactly the bit you want in any slice or any form that you want. If I just want one question and one -- that's always been impossible on Omnibus, but it should be possible for all of our data.

And I think this is quite a big project. This is not something we can deliver. The API and the Data Lake bit is available now, and it's -- but it always involves some extra work on something. But a real front end to that, that says, I want just this particular data should be the way that we allow clients in. And part of maybe how -- when I said there's a big gap between all of our assets and what they're buying is make it easy for people to buy any bit that they like. There's no reason for us to say you have to have a very big subscription to Brand Index. You can ease your way into that.

And when we've sometimes tried to do little data slices, it's been very successful. It's just we haven't wanted to do that. And that is a bigger project. That isn't an overnight thing. But that is definitely what a platform company would do to sell data at lots of different levels.

Further changes to the commercial team?

Yes. I mean we have put a very large -- not bounty. We've put incentives in place to make data products get more prominence, and hurdles that you have to hit before you make money on selling custom to sell products. And we have a dedicated team that does nothing but products. It's a small team, but it will grow. And this is really the change in our commercial -- in our sales approach. And there haven't been -- it hasn't been a massive -- it hasn't been as some -- somebody said we're having an overhaul or whatever. It's not an overhaul. It is an evolutionary change to our system. It's -- we've done well. We wanted to be better and we've made, as I say, some significant changes, including putting product as the #1 thing we're trying to sell and making it impossible not to sell a product if you want to get -- sell custom as well.

The 2 things are so aligned that it is a matter of how you incentivize, when you're selling one, you can sell the other. But it has to be that you have to sell subscriptions first. Otherwise, you're not going to be a data company.

It's Lara Simpson from JPMorgan. My first question was just to come back to the P&L. So you did GBP 61 million operating profit, which was really in line with expectations, but you clearly have benefited from sort of lower central costs and then some delayed spending in Shopper. Can you just talk a bit around the margin pressure you saw in Data Products and Research? Clearly, profitability was a bit weaker there. So where are you investing? Or is it sort of slow realization on the cost optimization side?

And then you've obviously outlined increased investments into technology and data science. Can you just outline, sort of quantify those investments? And then maybe just give us some line of sight on exactly where they'll be going?

Yes. On DP, it's a very simple answer. We acquired Yabble at the beginning of the year. It was a loss-making entity when we bought it. This is about just a shade under GBP 3 million. That's been completely allocated to the Data Products division. So yes, the margin pressure is purely as we're ramping up the activity, wrapping up the integration, ramping up the Data Products, the capability behind this is in part driven by Yabble's applications. So we expect to obviously see some of the revenue growth coming from that to help absorb some of that cost that's going into the business.

In terms of the investments, we're budgeting around GBP 4 million. There's a question mark on how fast we can bring that, about having head count. And so Stephan pointed to we have a new hire coming in as our head data scientist. And so it will be primarily focused on platform technology, which will support product, depending on the types of activities because Stephan is correct, this could be applicable to customers as well. So once we make a bit of progress, I'm just going to repeat what Stephan said, we'll come up with the Capital Markets Day to really flesh out what that looks like in terms of where do we see the growth rates coming and where do we see that landing. But for now, it will probably be even spread between the 2 because we're going to see some applications that are applicable to both of the lines of business.

And then just another question for me was around the balance sheet. So you've obviously closed 1.7 net debt EBITDA, maybe slightly higher than what I think some were expecting. You've obviously pushed back some of the payment terms. It feels like there is more sense of urgency to deleverage post CPS. Obviously, now you're investing a bit. Can you just talk about sort of balance sheet expectations over the next 12 to 24 months and how we should think about that new deleveraging as a priority going forward?

Sorry, Lara, to kick off. It's still a priority. I mean for us, it's really -- we have to be -- not careful. Careful is the wrong word. We do need to make sure that we have capacity to invest. We do see some clear opportunities for us. As we start to go out of the market talking about some of this capability, if we can see some revenue potential there, then, of course, you'll see us being much more aggressive in terms of being able to go for a market.

In terms of deleveraging, we're taking that down to EUR 20 million for the next 2 years. So we expect that to come down, albeit at a slower pace, but we do expect to have delevering happening. And of course the other side of that, we're trying to significantly increase our profits. So we're trying to achieve both, where we'd like to see some significant movement over the next 2 years in that deleveraging and, at the same time, making sure we're getting into that growth trajectory.

Jessica Pok from Peel Hunt. I've got 3, please. The first is, can you comment a little bit on the custom -- the sentiment for custom research amongst your client base? I mean Data Products slowed down, but also custom research.

And the second is on Shopper -- the Shopper segment and the investment going in. What is the key focus for Shopper over the next 12 months? I mean you've talked about broadening geographies and you've talked about product, but which is the main focus?

And the final one is just on the new innovations that you've showcased. Does that change the way that panelists are monetized -- are paid by going into this form of interaction?

I'll start with the last one because I remember it, the -- and the second one, I remember too. It fundamentally changes our relationship with panelists and we're changing the structure of panel. We've already talked in the past about having a core panel that can do a lot more, called YouGov Plus. And we have -- we know that, by the way, people who are doing a lot more are not giving us worse data, they're giving us better data. It isn't like there's a professional survey taker that somehow gives you worse data. They give you better data that's more aligned with the reality, in fact. And so there's a core panel that we would talk to more and that we'll -- we can rely on more.

And we are also now recruiting people not on the basis of any cash reward whatsoever, only on the basis of participation. This is a good way of actually making sure they're not frauds in the first place and as they come through the system. But more importantly, lots of people want to take part just for the sake of participation.

If you give them large, boring surveys, that's not going to help you very much. But if you give them these conversations, they will -- we know that they enjoy them. And I mean not everybody wants to talk forever, but lots of people do. And so we have not only interesting surveys that are contributing to public data, but we can have these conversations.

And actually, they will also do market research surveys and they would do -- and in any case, a lot of the things they notice and talk about is a form of unprompted market research. So these are sort of 2 ends and there's things in the middle, which is like our regular panel, which we don't interfere with because it's worked so well. So we're doing lots of things in panel and changing the relationship at different ends of that range.

The second bit was Shopper. And there are, yes, 2 things: more countries and changing the product. So we've invested in the receipt stuff, which is a form of -- well, it's not entirely automated, but it's less onerous than scanning your shopping.

Always remember that the old style here of actually scanning or shopping gives you a level of detail that no other methodology does. So that's why that -- even that old-style methodology of Shopper is incredibly valuable and retains its clients, and grows its clients actually because it goes down to the SKU level.

But also we're doing passive data collecting and we are looking at other forms of doing that. And that is also something that will drive our entry into America with this behavioral data. I think that's the aim, as we add more types of behavioral data in there. So there's a mix of ways we're looking forward.

I don't remember the first question.

Yes, just a change in appetite. Yes, we're seeing a little bit of a mixed bag. I think in some of our clients, we're seeing and having seen some good wins in Data Products in the financial year coming into this year, we are seeing some pressure from media agencies. And so we should anticipate that's going to be a bit of a struggle for us. There is an element of doing a fair amount of custom research for that sector. But on the flip side of that, we're starting to see more opportunity to pitch for larger things as well in the U.S.

So I think it's, to one degree, it depends on what country you're in. It also depends on what sector you're in, it's a pretty obvious statement. But I think we should still see some progress within the custom team coming into FY '26 besides macro. And to come back to some of the points that Stephan is making, it is around the measurement. It's people looking for more tracking opportunities. We like that. There's lot of visibility in it. And I think the U.S. team has been working pretty hard to uncover some significant opportunities here. There's a couple that we're working on in the U.K. as well. It's difficult to -- when you're in the summer months, not very much happens from a client decision making. So I think when we come into our Q2, we'll start to see some of that, potentially unlocking, we'll see some decisions made from clients.

And I think that there's a change happening as well in expectations of clients. So they're expecting something new from AI. And they've been holding back, I think, because they're saying, well, what's this amazing stuff going to deliver? And so far, it's delivered essentially toys. The things that you get, people are not paying for those things. They think they should be there because it talks back at you and stuff like that. But it doesn't give you data that you're going to make decisions on, not for the majority of the market.

So I think, obviously, I would say this, that our use of AI goes to the heart of what they are looking for. And so I think this is what they've been waiting for. I think they've been waiting for something that is new and yet that they can rely on them, that tells them something they really need to run their businesses. And that hasn't happened from AI yet. And I believe this is the start of that.

It's Hai from UBS. I have a couple on Data Products and then one bigger picture, please. So on Data Products, you haven't mentioned category view this time. I know you mentioned that you want that to be the way going forward. But is there a bit more of a tangible time line on when you're expecting it to add into the 95% customers you haven't monetized from?

My second question on Data Products is just a bit deeper on the margin perspective. So Yabble brought the margins down. But without Yabble from the numbers, it will be 35% margins, right? So what were the drivers in there? Was that the cost savings? And is that going to be continued? Where do you see the margins going forward essentially with Yabble, and deep into that, when do you expect Yabble to break even?

And then the third question, a bigger picture, is you mentioned the LLM potential monetization. How big of an opportunity do you see that is? And how aggressive are you pursuing it given the data quality that you have. Do you think monetization opportunities are there?

I'll do the 2 outside ones. I think the monetization of our data is potentially high but it may be 0. I mean I can't give you a better answer than that because should they want it, of course, they should, because the thing that those models need is recency and trustworthy sources, and that's what we do. But are we of sufficient scale for that? I don't know. So we are going to scale it up, but I couldn't possibly make a -- say something about that.

On the first question, category view. It didn't go well. We launched it, and the feedback was, well, we like what you have, but you're missing things that we need. And I'm afraid it was dropped at that point. There was no going back to fixing those things, which are highly fixable, and we are doing that. That is now with Joe Razza, our Head of Product, who is working on that as one of the things he's doing.

So it very much ought to work. And it's actually, we have a good way of bringing it back, which is -- well, really, I'm not supposed to talk about it, but I mean, we want to apply it to a new category that doesn't have very good tracking. And that category is AI. And so we'll be seeing for long a variation of category view for that sector. Yes.

And on the side, I would say that AI companies are definitely buying from us now. It's -- I have Investor Relations that I shouldn't mention that it's our fastest-growing sector because it goes from so small to something quite large, but we have made our first 7-figure sale to one of the LLMs. And we think that there is a need for our data by them.

I'll pick up on the Data Products margin. So a couple of things moving the margin around. Yabble is one of those and another is the cost reduction program. We've also had changes in the level of capitalization that we have and a lot of our developers are focused towards the Data Products.

I think coming back to when do we expect Yabble to break even, a lot of that depends on the pace that we can get these particular products out. I do want to make the point -- repeat the point that Stephan has made, you've got a difference in the way that clients are approaching AI products. And some people are finding they don't want to pay for them, they've seen this as a hygiene factor of having summarization, et cetera, built into your tools. We're really looking for ways that we can monetize that. And again, we'll update more concretely when we come up with the Capital Markets Day. But we do think we should be able to get that being a positive contributor fairly quickly.

Where do we see margins growing? I think there's a couple of things we'd like to see evolving. So one of those is just referencing back to what Stephan said, around data slices and having clients be able to come and self-service their own delivery of data. Obviously, that would be delivered at a high margin. You're just taking -- it's a repackaging of existing data.

But we're also focused on data partnerships. It's evolving the way that we -- the market that we point to, the set of users that we point to. Primarily, we are still talking to market research buyers. I think, clearly, you can see there are opportunities for us to go beyond market research buyers, particularly the LLMs. A lot of people are doing data deals with LLMs. And we already have a relatively small, we call it data activation, but it's data that goes into marketing campaigns, as part of Shopper's investment area, we've also been -- we make a few million pounds in the core YouGov business around that. We can see that also accelerating. The more clients are using AI tools for their own campaigns, it's a clear space for us to be putting data into that.

So as the use of that data evolves and the sophistication of clients using data for their own AI models, yes, we see that certainly moving up into their [ forte ] and beyond. But the pace of that is still to be determined.

It's Johnathan Barrett from Panmure's. I guess I've got 3 questions. Just first of all, thanks for the interesting presentation on the AI interviews. I wondered if you could just walk us through the model for that, the commercial side of that. So what sort of volume of interviews do you need for this to be useful? What's the cost of that? And then how do you commercialize it? Is it a case of bundling with other products that you -- where you're already selling? Is that an uplift? Is it just a question of clients expecting more value for money and you end up with the same pricing? Obviously, at the moment, you're saying you're getting inflationary pricing increases through, but that sort of implies that volumes are flat. So is that -- what's the driver there? Does this drive growth in actual customer numbers? Or does it simply enhance your -- the value of the sale to existing? If you could just walk us through that.

And then second question, and you've said a few things around this, so I'm just going to sort of try to round it up a bit, I guess, about data activation being used with clients. And just a more general issue of predictive work that you can do. Obviously, you've talked a lot about historic data, the what, the why, flagging what's going on. Can you move in that direction? Can you build your own personas for those purposes? Are you getting any commercial interest from clients? Just if you could wander into that side of the equation as well.

And then thirdly, a very simple question, I think. Yes, obviously, we're wandering into this AI period. You're back in the hot seat, Stephan. Are you really just the CEO for this AI period? In other words, the company needs someone experienced like you who's been in the business for a long time to see through this and you don't want to take risks? Sort of a difficult question. I think it's just an open question. So I think we're all keen to understand that.

Well, on the first one, the business model is, in some ways, it's early -- maybe too early to have been talking about it because there is so much work to do as to how far does this go, what kind of other interviews can you do, how can you use the prompt. We are at an early stage of that. The reason it's legitimate to talk about it today is because we've sold and we will sell a bunch of subscription add-ons from the alpha version. And the methodology is one that is really to be used tomorrow. I mean we have lined up in the U.K., I'm looking at Will, 5 or 10, I don't know how many clients -- 4 clients. Always inflate. We have lined up 4 clients that are -- will be very excited to want to use this. And so it is active and it will be able to answer their questions now.

And if you came along afterwards and wanted to -- representing brand or whatever, wanted something, we would do it. We could run it today. It is not a methodology that requires engineering other than what we've already got. It builds on every asset we have. It's putting together things we've already been doing. We've run 20,000 interviews. They are very low cost.

It depends on whether you're paying the respondent or not paying the respondent. But if you're not paying the respondent, you can imagine that one of these sections is going to be less than $0.20, right, for -- just I'm talking about that bit of the cost.

And this engineered part isn't a high cost thing. Brand Index isn't a high cost to collect. So I can only give you very broad indicators of the numbers involved. I don't know if it's 20,000 interviews per day or 5,000 will do, or it depends how many countries we're in and so on. So it's legitimate for us to talk about it because we are selling it now in some version and more versions over the next weeks. But it isn't in a place where I can give you a business model for.

That's what we intend to do and that's what we've just done. And we're looking to see what does that yield. Does that -- is that something we could have got just as well with 5,000 or 3,000? Or did we need 80,000 or whatever? It's -- and it depends, I say, how many countries you do it in. And so we've done it in a way that allows us to come up with conclusions, initial conclusions, about all of those things. I mean the beauty of our system is experimentation is incredibly simple and incredibly interesting from the first -- from the get-go.

The second part was predictive, yes. Well, so prediction is hard because prediction is extrapolation really, unless you know -- you can't know what's going to change something. So I think prediction is about extrapolation and tracking. And we've done with MRP. I mean the best thing -- the best measure of prediction is something real, and that takes -- that is totally visible and we are the best predictors of elections. There's no question. We do it in many, many countries. We have just had 3 MRPs in Australia, Germany and Canada, I believe -- may not be Canada. I think it might have been Spain, actually, which were bang on -- huh? Spain, sorry. And as have been -- many previous elections, bang on in market research terms would be within 5% or 10%. For elections, it's like 1% or 2%. And that is our average.

So that's a prediction of sorts, but it's assuming that people -- what they say going to do soon. Long-term prediction. I don't see how you do it from what we do. So if there's a predictive model that somebody has, I think they would use us as opposed to us doing that stuff.

As an aside, you may remember with the Trump election, one of a hedge known as the Trump Whale, made large amounts of money on betting on Trump, and praised the quality of the data that we had. That data was ours. That was a client of ours. He went on to eventually say. We would not have used it the way he used it, our data. So it's a good example -- we will supply the real data. And if somebody else is better at doing that, that's their job. It's not our job.

Our job is to provide the best, most accurate, real description of things now. And that's -- we know how to do that.

And my personal stay, I'm here to make sure we're back on track as the growth company we were in the sort of 9 or 10 years that we were growing at double digit year after year. Obviously, I'm not staying for that period, but I'm staying to the point where we feel, hey, we're back on track. Now that could be just the end of this year. I think our expectation was 1.5 years, something like that. I've just gone the half year. If it was 2.5 years, it's too long because I'm -- I should have made bigger success by then.

So I can't say. I mean it could be 3 years, but it's more likely to be a year or so. But there's really no point in deciding that by the second. We've got at least 6 months to see what happens before we have to make -- start making a planning decision. I don't know if that's...

It's really just, are you there for handling the AI thing right now?

Yes.

Everyone's got this headache on the horizon, or it's right there right now hitting you. It's depending on who you are and what industry you're in, but.

Yes.

Steve Liechti from DB Numis. Just a few. On Data Products, I guess, in the second half, in my head, we had 3 things that you needed to do, which is category view, the AI -- sorry, the user experience tools and AI tools, which I thought was Yabble. Just talk us through -- you kind of alluded to category view didn't happen. Can you just talk us through on the other 2? And then going into fiscal '26 now, we still got -- have we still got those 3 to get the benefit from plus the qual stuff that you're putting into and launching into Brand Index. Is that the way to think about it? Can we do that, first of all?

Yes. So on category view, it's exactly what we should have been doing. We should have changed the product once we realized what it was they wanted. And that was -- they wanted more questions. They wanted 2 things. They wanted more questions around specific use in that sector, so more sector questions. And they wanted more -- they wanted historic data that we had.

I've tried to avoid going back over what didn't happen and should have happened in my periods. We had a head of product that came to us and did not -- decided not to make category view, not to go back to it, not to fix it, just to move on. And I think it was wrong, and we've picked that up. But it's -- of the 3 things, it's not our -- wasn't our #1 because it already had been dropped. So it's that we're definitely doing category views, as I just mentioned, and reviving it in a new area.

The other 2, the -- Yabble, obviously, is a major contributor to the product that we're just looking at. The summarization, they're working out what the data means and so on. And the other part was, I think, yes, the interfaces. And those have been improved and there will be a continuous improvement.

So I think that those 3 things, the category view is the one that didn't happen, but is going to happen.

And you think that the 4 things that I said, i.e. repeating those 3 original plus the qual stuff are the key drivers for DP into fiscal '26?

Yes. And I think you were asking as well, Johnathan, I think you were asking, was this a separate product or an add-on or just making it more attractive? I think it's a major new type of data, which means it will be deployed as an enhancement to existing things, which you can -- which you have to pay for, or it can be used on its own. So I can't go further than that because I don't know how you will engineer some of these things, on what order we would do it.

Number one is it's an enhancement to Brand Index that you pay for, which I think would make Brand Index a more exciting product to sell and therefore -- and to buy. And it would mean that existing users who are interested in this data are likely to be early buyers of it. That would be the beginning. And yes, the AI tool.

And I suppose we should put in, a fifth one actually, which is a more focused sales team as well on the product side. So that you've got the 5 things.

Yes.

So if you took those 5 things and I think about fiscal '26 between the first half and second half, last year, you did about 1% growth in DP in the first half and 1% in the second half, give or take-ish. How do you think that should flow through first half, second half? Is it kind of more of the same in the first half and then acceleration in the second half?

Well, there are so many moving parts here and, obviously, I'm so optimistic that you shouldn't probably listen to me anyway. But I think that we will have a Capital -- I mean I know that we will have a Capital Markets Day. I know you're going to ask for these updates. And we will have that as soon as we have some -- the next stage of concreteness around this. I would like it to be very soon, but I can't promise it until I have a little bit more customer feedback.

And then last question, just in terms of the overall profitability of the business, given you're putting in the investment that you talked about, this year, roughly, give or take, 30-30, won't be between the halves, if consensus is now low to mid-60s, how should we think about the profit flow through first half to second half?

I think these investments will take us a bit of time to come through. So I think we'll start to see those coming in really Q2, Q3 of our year. So as you project before both going into the latter half of the year with a higher cost base. Of course, the thing that we're still sort of working on, and not to rehash this too many times, we could see an acceleration of client adoption around these things. But for now, we're being fairly conservative in terms of we just can't predict what the uptick will be, it will be very client-dependent.

So yes, the main factor will be how fast can we find people and employ them within these data science teams. But we've already made some progress in terms of getting a leader.

Jessica Pok from Peel Hunt. I just have one follow-up, please, about the new products. Obviously, you're doing a lot of testing for clients in the U.K. who are interested. Do you -- I mean is the process now to deploy it to these initial customers' feedback, reiterate? I mean when do we get to the point, I guess -- or is it more second half of this year? Or are you really envisioning FY '27 of when you could possibly do a full launch of products? I mean, I'm assuming that whilst you're doing this testing, you're holding back a little bit on kind of showing it to all your customers and deploying it to all your customers.

So I mean, if you -- which you do know the business, you'll know how easy it is for us to do this, because we do at least 20,000 surveys anyway every day. By surveys, I mean, interviews. And we can add this to the end of every single survey and say, would you like to talk about anything that's on your mind now? And actually, we will. We will add it to every single survey as just a final sign-off question. And they can tell us what a s***** survey it was or they can tell us they'd like to do some -- they'd like to chat a bit.

And some of these people talk about their divorce. They talk about their football team. In the first few thousand that we had, the variation in those are incredible. People want to talk.

Now what proportion? Every single survey that we run, we will ask them if they want to talk some more. So it's really easy for us to set this up. And we have a prompt that allows them to talk about anything they choose or to choose one of the things that we put in there. So they actually can use it the way that they want. And it's sort of pre -- sorts itself as it's going through. It's unbelievably simple and rich in its product and enhances user experience. It isn't a cost for this because we're not going to pay people except when we wanted to talk about something really boring. That's the bit about being a panelist, sometimes you have to talk about your use of OXO cubes or something, and that's not what most people want to talk about. Nobody ever wants to talk about an insurance plan, right? You have to pay them for that. So you do have to do that sometimes. But you don't have to talk to them about music -- pay them to talk about music or about their lives or whatever. And that gives you a lot of background information that you can then divert when you need to and offer extra incentives.

So what I'm saying is that this will be very quickly engineered to the entire running of surveys that we do. And we'll be able to do -- create products of it, I believe, all over the place. But it will not run out for a long time of ideas. There's so many things to try. But it just sits there as something people want to do anyway.

I mean I should say, just -- please, just last. We have had a box at the end of surveys for years, which nobody reads. And we realized we had 10,000 comments coming in the day every day that we ignored that nobody ever looked at. We still don't know what they said. I mean it's a very bad thing. And that kind of triggered this.

We have a question that's come in online. It's from Jonathan Cohen from Zipper Line Capital. And Jonathan's question is, SP3 called for 500 million of revenue excluding M&A and CPS, and 25% operating profit. Is that still what you're aiming for? And is that what you're guiding to in the medium term?

Yes. And you know what I'm going to say to that, I'm going to say that we will have a Capital Markets Day and we will remodel everything for that. It's impossible for me to say that now. But I will say that we are a data company that is incredibly ambitious to be the world's #1 supplier of opinion data everywhere. That's what we can do. It's incredible that we haven't done more in that sense because you see what's there, it's all engineered, it's all there, and we just haven't done enough.

So our ambition remains huge. Our execution has not been good enough, and we are doing quite a lot. We haven't talked about this, but we're doing quite a lot. The Board has been very active in helping improve execution at YouGov. We have board members that are actively involved. We have new Board members. Nobody has asked about the Board members, but you'll have noticed, we've had some very high-quality board members. A couple from Silicon Valley, a couple from very good experience in U.K. PLCs, one of them from Kantar. We have high involvement now from the Board in pushing for better execution.

And so my answer to that, Mr. Cohen is -- and he's been an interesting contributor in comments, I totally agree that we are pushing -- that we are a data company that should have very high ambitions. And we will give you a more realistic steer on that at the Capital Markets Day that we'll have. We'll have it as soon as we can -- as soon as it's ethical for us to do it. In other words, that we have enough actual information, complete information to base it on.

Great. Thank you. Thank you, Mr. Cohen, for the question online. We don't have any more online. So unless we have any more from the room...

Thank you very much.

Thank you very much, everybody.