Grow your business with consumer-driven insights

We’re excited to welcome Thierry to the boobook team as a senior insights director. With over two decades of experience in the insights industry, spanning both qualitative and quantitative methods, he brings a deep understanding of a wide range of business questions and how to address them. Thierry has worked across a broad range of sectors with one end goal in mind: delivering clear insights so clients can tailor their products and services to the needs of their customers. Helping businesses thrive is what it’s all about.

What sets him apart? A fascination with human behaviour and a passion for turning complex data into clear direction. Whether he’s solving pricing questions, segmentation issues or other complex business challenges, a project isn’t finished until the insights are put into action.

Turning variety into strategic advantage

Even after more than twenty years, Thierry is still hooked. “I love this job, and I’m excited to put my expertise to work in this challenging new environment”, he explains. “One thing I particularly enjoy is the variety. It’s been a privilege to get to know so many sectors and businesses in depth. Every project brings something new. I’ve never wanted to limit myself to just one sector because there’s so much value in cross-industry thinking. What you learn in one context can often spark unexpected insights in another.”

Driving businesses towards meaningful change

Coming from a larger agency, Thierry was ready for a change. “I look forward to being part of this tight-knit team and staying involved in projects from start to finish,” he says. “The boobook team brings together a wide range of expertise. Working closely together, combining our strengths, and aiming for the highest quality in everything we deliver that’s what makes this next step so exciting for me.”

He thoroughly enjoys delving into complex business questions, particularly in an international context. “The most rewarding projects are those where you can have an impact on a strategic level. Contrary to what some people still think, our work doesn’t revolve around delivering reports. We want to bring the research results to life. To do so, you need a solid understanding of your client’s business context and the challenges they’re facing. It’s just as important to consider how those insights will land within the company. That’s where storytelling and workshops come into play. Translating data into a story people can connect with is a part of the process I deeply enjoy.”

Beyond the professional

Thierry has been intrigued by the workings of the human mind for as long as he can remember. “That’s why I decided to study psychology,” he recalls. “While I’ve never worked as a psychologist, it still proves incredibly valuable. Understanding what drives people and how they make decisions is at the heart of meaningful consumer insights.”

With two sons who are growing up fast, he loves spending time with them and his wife. When he’s not working or spending time with his family, you’ll most likely find him on his bike, clearing his head, or on the pitch, playing football.

“It's an exciting time for insights professionals,” Thierry concludes. “The world keeps changing rapidly. It hasn’t stopped since COVID-19, and it isn’t likely to stabilise any time soon. Businesses need consumer insights with a larger shelf life, grounded in a solid understanding of customer needs, expectations and behaviour.”

Feel free to connect with Thierry on LinkedIn if you’d like to discuss today’s erratic consumer landscape, or football.

While it’s true that no two people are identical, we’re also not as unique as we’d like to believe. That’s what makes segmentation so powerful. If you succeed in understanding the distinct needs, preferences and behaviours of different groups within your customer base, you’re well on your way to boosting business results.

Not convinced? Leading consultancy firm McKinsey found that personalisation can lift revenues by 5 to 15%, highlighting the impact of tailored marketing and services in driving engagement and sales. Segmentation is definitely the path to personalisation but, as our colleague Eva Vandenberge highlighted during her recent talks at UBA and MIE’25, many companies struggle to make it work.

Below are three key challenges shared by her audience and some insights that will help you tackle them.

1. How can we ensure other teams actually use our segmentation?

Developing a technically sound segmentation might not even be your biggest challenge when building a segmentation model. This frustration shared by one company might be familiar: they meticulously analysed the data, tested various models, and confidently delivered what they believed was a solid customer segmentation, only to watch it gather dust because the different teams barely used it.

We’ve seen it all too often: even the most robust segmentation can fail if it’s not actionable and adopted across the organisation. Too many companies rush into the process and overlook a couple of crucial steps. Our 5-step approach will ensure your well-crafted model doesn’t suffer this unfortunate fate.

1. Discover & learn

Talk to all relevant stakeholders to ensure you understand their needs and how they currently work with customer data. You can hardly expect them to use a segmentation if it doesn’t fit their needs exactly.

2. Plan for success

This stage is all about gaining stakeholder buy-in and building an ambassador team. You won’t be able to tackle all needs and questions at once. You’ll need to prioritise and make tough choices all stakeholders agree upon.

3. Collect & develop

You’ve finally arrived at the nitty-gritty part of the process. Only now will you start building a model based on internal data and possibly additional qualitative and quantitative research, depending on the needs you’ve agreed upon.

4. Share & adapt

Have your stakeholders validate the segmentation model you’ve built, and refine or adapt it based on their feedback. Consider a test run that clearly demonstrates the added value of the segmentation for all teams.

5. Implement & adopt

Implementing your segmentation should be much easier when you’ve successfully gone through the previous steps. But remember: segmentation is a journey. Keep it alive by regularly sharing success stories from different teams. Follow up on trends and consider a re-run when market conditions shift, your offering or strategy evolves, the segmentation becomes less actionable, or stakeholders indicate it's no longer meeting their needs.

Our work at boobook is not limited to collecting and developing segmentation models. We can support you through the entire process from stakeholder interviews to organising workshops to support your teams when they start implementing the model.

“By combining strategic guidance with practical support, we ensure your segmentation strategy is embraced across teams and effectively translated into actionable insights that drive results.” - Eva Vandenberge

2. How can we integrate attitudinal and motivational variables into our CRM database for improved targeting?

Internal data serves as a good starting point for your segmentation, but it will only get you so far. You’ll be able to define your segments in terms of demographics and behaviour, but not needs, attitudes and motivations. These are often precisely the variables that will make your segmentation more actionable, as they are an excellent starting point for your positioning, product development and marketing and communication efforts.

A carefully crafted research design will allow you to build a much more granular segmentation, but to target the identified segments you must ensure to integrate these new insights into your CRM database. Our data analysts are experts on the matter. They will either:

1. Combine your internal data and newly gathered consumer insights into one single, yet comprehensive predictive model.
2. Build a nested segmentation model, as we did for Telenet. This model proved invaluable in finetuning their CRM targeting to better serve their customers.

3. And what about AI?

Another topic that inevitably came up during Eva’s sessions was AI. We don’t recommend using it to develop your segmentation model. Currently at least, there are no real benefits, and AI's tendency to hallucinate can undermine the reliability of your model. We do believe AI can add value in other ways.

“That’s why the boobook team has developed an AI chatbot to help you engage with yoursegments. This tool allows you to have conversations with your segments,sparking new insights and fresh ideas on how to approach them.” - Eva Vandenberge

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Discover Telenet’s award-winning approach in action: watch the recording of our webinar on customer segmentation.

Curious about how we can support you in bringing segmentation to life? Reach out to our team!

In 2023, as Bud Light watched 15 billion euros in brand value evaporate in as little as six months, their analytics dashboard showed green across every traditional metric. Their brand tracking was perfect. their market share calculations flawless. There was just one problem: none of it really mattered. The backlash they received due to the partnership with a TikTok influencer, a transgender woman, Dylan Mulvaney, turned into a boycott, causing a decrease in Bud Light sales by more than 23% during and after the NCAA men's basketball tournament. Critics called the sponsorship "political" due to Mulvaney's transgender advocacy, while major media outlets characterised the response as anti-trans.

This wasn't just a marketing mishap. It was a textbook example, of a deeper issue modern brands often have: measurement obsession.

When David Aaker introduced his brand equity framework in 1991, he couldn't have predicted how his attempt to make brand value measurable, in a relatively simple and manageable way, would transform – and sometimes distort, or even abuse and exploit – modern marketing. His five components (brand loyalty, awareness, perceived quality, associations, and other assets) seemed straightforward. In previous decades, this was all still logical to explain and easy to measure. A kind of foothold for brand managers, marketeers or the board, without pretending to be an all-telling oracle. But lately, the obsession of measuring metrics that could define brand equity has completely degenerated...

Modern marketing departments have increasingly come to resemble control rooms. Teams of analysts focused on real-time dashboards tracking hundreds of metrics, from social sentiment to brand awareness. AI algorithms predict consumer behaviour down to the millisecond. We've never had more data about our brands. Yet some brands are failing faster than ever. Bud Light's collapse came despite perfect metrics.  

This isn't a coincidence. It's about a fundamental paradox: the more sophisticated our brand measurement tools become, the more vulnerable our brands become. This raises an uncomfortable question: What if our obsession with measuring everything isn't just ineffective but destructive?

The measurement trap: From the Coca-Cola disaster to Tesla’s downfall

Back in the 80s, the “New Coke” disaster serves as a well-known example of how you can’t measure and predict it all, sometimes psychological and social meta-effects are happening that you couldn’t predict at all. Coca-Cola had the data. Their taste tests were conclusive, their metrics solid. By every measurable standard, the new Coke should have succeeded. In effect it failed because they measured the wrong variables. They forgot to measure the emotional connection consumers had with the original formula, which transcended taste preferences.

Perhaps their biggest mistake, the company's missed opportunity to adopt a holistic view of their brand portfolio and competitive landscape. At boobook, we ensure such opportunities are seized as we believe brand equity analysis should emphasise this broader perspective, examining how changes to one product might affect the entire brand ecosystem and its position relative to competitors. Had Coca-Cola adopted a more intuitive approach, they might have anticipated what was to come and prevented the dramatic consumer backlash that followed New Coke's launch.

The story of Tesla’s downfall in 2024 is yet another example of this disconnect. While Wall Street celebrated the electric vehicle maker's stock price, consumers were quietly falling out of love with the brand. It begins with a dramatic stock surge of 63% following Donald Trump's election victory, bolstered by Elon Musk's hefty $277 million contribution to Republican campaigns. Beneath this financial triumph, the public was forming a completely different narrative.  

If you are buying from Tesla, the persona of Elon Musk is highly likely to impact your view on whether or not you want to buy one of his company’s cars. So, such an emotional connection, even though not the only factor, is again (as in the Coca-Cola case above) the most influential one. (Oh, and let’s not even start with the failure of Twitter/X re-brand).

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Can AI algorithms truly understand your customers?

The new digital era promised better brand measurement through real-time metrics, instant feedback, and predictive analytics. Artificial Intelligence seems like an ideal solution for brand measurement. However, these technological advancements have created the danger of what behavioural scientists tend to call "automation bias"— or the tendency to trust automated systems over human judgment — even when humans are right.

In early 2022, Netflix executives faced a harsh reality that came to shatter their beliefs about what their subscribers wanted. For years, the streaming giant had relied on sophisticated AI algorithms that suggested viewers craved an ever-expanding library. "More choice equals more satisfaction" had become an unquestioned mantra within the company's Silicon Valley headquarters.

But as viewers found themselves endlessly scrolling through thousands of options, unable to decide what to watch, a different truth emerged and the phenomenon known as "choice paralysis" began taking its toll. Subscribers, overwhelmed by the sheer volume of content, started doing something unprecedented: they began canceling their subscriptions.

The market's reaction was swift and merciless. In a single day, Netflix's market value plummeted by 54 billion euros—a reminder that sometimes, algorithms can lead even the biggest companies astray.  
The incident became a cautionary tale in the tech industry: more isn't always better, and AI predictions don't always align with human psychology. Basically: a little less blind belief in what technology presents to us, a little more use of common sense and thorough knowledge about the human psyche.

This shift in thinking has led many companies to reassess their relationship with data and artificial intelligence and began to question the "more is better" approach. Perhaps no company better exemplifies this alternative philosophy than Apple.

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How Apple does it

In today's data-saturated business environment, companies often fall into the trap of measuring everything they can instead of everything they should. Yet Apple seems to be one exception that proves our rule. In 2024, as their brand value soared to 516.6 billion euros – larger than the combined value of Starbucks, Mercedes-Benz, Tesla, and Porsche – they achieved this through a laser-focused approach to data that prioritises quality over quantity.  

Apple's approach to data analytics exemplifies a crucial principle: it's not about measuring everything possible but measuring what matters most. While competitors drown in endless dashboards tracking every conceivable metric, Apple focuses on a carefully curated set of indicators that truly drive value. They complement quantitative data with qualitative insights about how people interact with technology, what frustrates them, and what brings them joy—metrics that many companies overlook in their pursuit of more data.

Consider their controversial decision to remove the headphone jack from the iPhone in 2016.  Every metric suggested it would fail. Consumer surveys showed overwhelming opposition. Social media sentiment was deeply negative. Market research predicted a significant sales impact.

Apple did it anyway.

And the risk paid off. AirPods alone now generate more revenue than Spotify, Netflix, and Twitter combined. What the traditional data missed – but Apple's leadership understood – was that sometimes the most predictive metrics aren't found in spreadsheets but in deeper patterns of human behaviour.

This pattern repeats throughout Apple's history. When they launched the Apple Store in 2001, retail experts called it commercial suicide. Traditional retail metrics showed computer stores consistently failing. Yet Apple measured something others didn't: the value of experiential shopping. They recognised that people needed physical space to experience technology—a metric that wouldn't show up in conventional retail analytics. Today, Apple Stores generate more revenue per square foot than any other retailer in the world.

This goes beyond a story about Apple's success—it's a masterclass in strategic data analytics. While many companies measure what's easy to measure, Apple measures what's important to measure. They demonstrate that sophisticated data analysis is less about having the most data points than it is about having the right ones. This isn't a rejection of data, but rather an understanding that true data intelligence means knowing which metrics actually predict success, even if they're harder to measure.‍‍

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The need for a new brand measurement framework

What the brand measurement paradox teaches us is a crucial lesson: if we approach brands purely in a scientific way, we risk making them less human. The most successful brands of the next decade won't be those with the most sophisticated automation and measurement tools, but those who master the delicate balance between data and human intuition.  

This balance requires some fundamental shifts in how we approach brand measurement:

• From quantity to quality: Rather than tracking hundreds of metrics, like Apple, focus on a carefully selected set of indicators that truly predict brand health.  

• From real-time to right-time: Sometimes, slower, more thoughtful measurement yields better insights. As the Bud Light case demonstrates, instant and superficial metrics can miss deeper cultural currents.

• From algorithmic to human: Whilst AI and analytics have their place, they should augment, rather than replace, human judgement. Netflix's content paradox illustrates how even the most sophisticated algorithms can completely miss the intricacies of human psychology.  

At boobook, we've developed a framework that fully embraces these principles. Our approach combines rigorous quantitative analysis with deep qualitative insights, recognising that brand equity exists not (only) in spreadsheets but, most importantly, in the hearts and minds of consumers.

The future of brand measurement isn't about choosing between data and intuition—it's about using and applying each where they work best.

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