How WPP is evolving one of the world's largest studies in the age of AI without disrupting it

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With WPP's Chief Product Officer, Michael Sussman, WPP's Director of Research, Michele Jee and Bounce’s Co-Founder & Co-CEO, Charlie Butler.

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The pressure to do more with less is compounding in the era of AI. Insights teams are grappling with budget cuts, growing expectations and internal restructuring.

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Few are feeling the crunch as publicly as WPP, which has been mandated to save £500m annually by 2028. This is why Michael and Michele are championing the adoption of AI to cut costs and expand capabilities. But their mission to evolve WPP’s Brand Asset Valuator, one of the world’s largest and longest running studies, must not compromise its quality or continuity. 

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They took to the stage at Quirk’s Chicago, alongside our Co-Founder & Co-CEO, Charlie Butler, to share how they’re achieving this. Here’s a recap.

The productivity paradox

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AI is deflationary. As a result, leadership expect to see productivity gains and cost efficiencies by introducing AI into the research process. But it must be done with care. 

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“The pressure to cut costs is growing exponentially. How can researchers do things faster, better and cheaper, while balancing the need for research integrity and robustness?” Charlie asked. 

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“Michele, how are you approaching this problem at WPP and how is it impacting your day-to-day?”

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“We’re seeing a lot of budget cuts around WPP. But we’re not willing to skimp on data or technology, because that’s the engine of everything we do.” said Michele. 

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“We’re looking at ways to use AI to empower us and our data and this includes different parts of the research process, from checking brand responses to analytics to project delivery.”

The brand blur

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While the adoption of AI promises productivity gains, it risks creating a 'sea of sameness'. As more organizations lean into automation, they’re at increasing risk of losing their ‘secret sauce’ and this is on Michael's mind.

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“I’m very focused at the moment on creating the rules that AI needs to follow, rather than falling into the trap of following AI” Michael shared.  

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"Brands are fatigued. Everyone's using AI, automation and simplification and the differentiation that brands have been refining for the past decade is starting to blur. If you follow the herd, you become generic. We’re seeing that more and more."

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“So how do you make your AI adoption more powerful than anyone else's? Your data. That’s why we’re not willing to compromise. Our data is our secret sauce and it directly influences the quality of our AI capabilities.”

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The red thread

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What data is WPP collecting through the Brand Asset Valuator and why is continuity so important?

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“The Brand Asset Valuator launched in 1993 and holds data on between 60,000 and 70,000 brands worldwide. We measure brand equity, strength, meaning, advocacy, perception and more. We then provide clarity on what the data stands for and how to interpret it and take it further.” said Michael. 

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“The study has been running for 30 years, so the key question people ask is how do you stay consistent?” 

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“We run around 22 studies a year globally, so it’s a beast. As Michael said, we want to innovate, but we don’t want to lose the continuity of our previous data sets. So we have to innovate within those confines.” added Michele.

“There are a few things Michael and Michele have done really well when it comes to embracing AI, while protecting continuity. The first is around keeping humans in the loop. For example, building guardrails around automated quality checks and AB testing them with a human that knows the process inside and out.” shared Charlie.

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“The second is focusing on areas that will get genuine value from automation and not just automating for the sake of it. For example, Michael and Michele purchased an automation tool for a specific part of the research process a while back. While it brought cost down, it actually ended up making the process twice as long, because of the change management needed to make it work.”

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“They learnt from this and honed in on the areas that actually need support from AI. Making this distinction is something organizations don’t do enough. Testing AI capabilities and actively deciding the areas they’re not going to automate, because the technology isn’t quite there yet or because it’s not the right fit."

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The ideal partner

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Making this decision on where you should and should not automate is not something you need to do alone. The ideal partner should have the technical know-how and the research expertise to support you in identifying where AI can and can’t add value and where it should and shouldn’t be incorporated into your research process.

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“For our team, we really needed the technical expertise, so we can execute the survey in exactly the right way to keep things consistent. On the other hand, we needed research expertise, because we have a lot of markets and they all present different challenges. We also have custom work, where we need to survey hard-to-reach audiences, so technical expertise and research expertise were two barriers to entry” shared Michele.

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“Thirdly, we needed someone that was collaborative, because we’re operating within the confines of continuity. We need to keep things stable, but still achieve a good respondent experience and obtain high quality data. For example, we’ve built something with Bounce that has allowed us to innovate how we check the data, so we can optimise on the fly without needing to go back into field to replace 3,000 poor responses.”

Michael added: “When I came into WPP, I said to the team: ‘We’re the Blockbuster of the industry. The beast. What does that mean? That means fatigue, it means we’re stunted, it means we can’t move easily.’ I pulled up a chart showing where Netflix sat at that time. Underdeveloped brand potential vs. a behemoth. We needed to be more like Netflix, which means we needed to evolve.”

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“It’s been a real balancing act of driving change without disruption and that’s why I love the team at Bounce. They’re helping us do it in the right way. It’s not easy, because there’s a lot of data and we have to be careful of every little nuance. We needed a partner that could help us find the right path without ruining what we’ve built so far.”

One vs. many

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“Unfortunately, this combination of tech capability, research expertise and a willingness to co-create isn’t easy to come by. Client-side researchers are left with three imperfect options right now.” shared Charlie.

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“Full-service agencies have amazing people. Deep research experts. But if you’ve been using them for 10, 20, 30 years, their core competency has remained the same and they’re not strong with technology. What this means is that you’re effectively outsourcing your research to an agency that’s outsourcing to a tech company that may even be offshoring their software engineers. That’s a crazy process and a very disconnected value chain.”

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“The second is DIY software tools. They entered the space over the last 10 or 15 years and became the best thing since sliced bread. But it’s tricky because they only operate well in certain domains. There are some amazing software companies out there that can standardize research and roll out tooling that’s easy to use. But where most struggle is custom research. There’s no ‘one template’ that can be applied to a custom brief. You need humans to understand the context and apply the best methodology.”

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“The third is AI tools. When you have an AI-native company that doesn’t have researchers to support it, how is it supposed to understand the context needed to solve a research problem?”

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“How can you overcome this challenge? First, consider core competencies. What skillset do you really need to solve the problem at hand? Focus on vendors who have core competencies in this area. The second is to avoid staying with one vendor for too long. Two things tend to happen. You miss out on cost efficiencies and there’s less pressure for them to innovate. You need to challenge them on both of these things to make sure you’re getting the best out of them.” 

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“Something I don’t see enough and something I’d love to see more of our clients engage in is co-creation. When it came to WPP, they were very sceptical of us at the beginning. We showed them that half of our team are software engineers with a core competency in tech and that we have a team of leading researchers in-house, bringing the research expertise they needed. We worked hand-in-hand with them to show them what the technology could do, while protecting the need for research integrity, building a custom solution specifically for their needs.”

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“I think this is a common issue when working with vendors. Not asking enough of them. Look at how competitive the industry is right now. Sit down with your vendors, ask them what their core competencies are, ask them if they’re willing to co-create. This avoids the need for a fragmentation of tools and suppliers, because you have an in-house solution co-created with a partner who has core competencies in the areas you need.”

What is the insights operating system?

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The insights operating system exists for insights leaders like Michael and Michele, who need a partner with tech capabilities, research expertise and a willingness to co-create. 

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It’s for insights leaders who are under pressure to do more with less and are looking for a way to deliver robust, nuanced insights at speed and at a low cost.

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In short, the insights operating system has two parts. First is the Retrieval Engine. Studies show over a third of all the research done at an enterprise level is duplicative - a huge waste of budget and effort. The Retrieval Engine unifies all of your past research and enables you to ask questions of it in plain text to see if the answer you need already exists. No need to dig through slides, scan datasheets or rely on your memory.

“Knowledge management, like many DIY tools, failed, because it was like a Google Search for your information. It would simply tell you which of your reports mentioned ‘drinkers in Mexico’. But it didn’t give you the actual answer.” said Charlie. 

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“Only recently has it been possible to ingest structured and unstructured data and auto-tag it. We can integrate into your central data infrastructure and understand the context [behind your question], so instead of telling you where the answer might be, we can give you the answer.” 

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For the questions your past research can’t answer, the insights operating system runs a gap analysis and builds a custom study using the Research Engine, which is run and delivered at speed by your dedicated Bounce consultants.

“One of the critical components for an operating system is that it can be independent. For a lot of our clients, we’ll tell them the gaps in their knowledge and they’ll go off and run the research with a different partner.”

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“We’re not saying we can do absolutely everything. When speed is important and the need is quant, we will design a survey ready to go live for you in a matter of hours. But if you need deep qual, you can take the brief and take it straight to your qual partner. You may not need to run any research at all, you may need to buy Nielsen data. The point here is independence. The clarity of what you know and what you don’t know.”