Grow your business with consumer-driven insights

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

Eager to learn from Telenet’s approach?  You’re in luck! On Thursday, April 3rd we’re hosting a webinar on Telenet’s approach. Don’t miss out: register here for a unique roadmap for effective customersegmentation.

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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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We're delighted to welcome dr. Timothy Robeers to the boobook team as a Senior Insights & Marcom Consultant, bringing over a decade’s worth of experience in qualitative methodologies, strategic communications and insights development. His academic journey began in Antwerp, Belgium, by studying languages and linguistics (English and German), before pursuing a master and postgraduate degree in visual culture and creative writing at the University of Antwerp and Edinburgh Napier University.

His journey continued with a PhD in Social Sciences that combined communication, sustainability, and sports—with a particular focus on the rise of electric motorsports. During these years, Timothy not only travelled extensively across Europe and the United States but also discovered his enthusiasm for connecting research with real-world impact. His experience gained by teaching at both the University of Antwerp and the University of Edinburgh in Sport Management and International Development further shaped his understanding of how strategic thinking can bridge gaps and add value.

The transition to corporate and adding strategic value

The COVID-19 pandemic became a catalyst for professional reflection and transformation. "I wanted to move more into the corporate playing field whilst also applying my background in qualitative methods, strategic communication and sustainability”, Timothy explains. This decision marked a natural progression from his previous collaborations with businesses and organisations in the world of automotive and motorsport.

"I’ve always been someone with a keen desire to know more about things. When I hear about something, I want to know more, not just take it at face value", Tim shares, describing the investigative mindset that has also driven his career choices. This curiosity led him to his first corporate insights position, where he spent three years honing his industry expertise before joining boobook in February 2025.

What drew Timothy to boobook goes beyond professional opportunities. While the company's expertise in pricing and segmentation initially caught his interest, it was also very much culture that sealed the deal. "Based on what I heard, the working culture and the way boobook chooses to manage its employees really appeals to me. And that will also reflect positively on their clients", says Tim with a smile. "Here at boobook, it immediately feels like you’re a part of a caring group of people working seamlessly together across different methods and areas of expertise". This collaborative environment is an important aspect of boobook culture, as it allows for easy cross-departmental cooperation and mutual support.

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People, first and foremost

In his role at boobook, Timothy wants to bring his experience from diverse sectors, including construction, telecommunications, food, health, and well-being. "To me, the industry we work in is an industry of people first and foremost", he emphasises. "It's about helping other people to come up with solutions that work for them, and not just for the short term but also the longer term. That all starts with listening and understanding people’s needs carefully".

Regarding his vision for developing strategic insights at boobook, he emphasises the power of curiosity and combined methodologies: "In my opinion, combining qualitative and quantitative methods holds great strength and potential in getting the full picture and delivering accurate advice".

When discussing his view on the future of strategic insights and innovation, Timothy takes a measured approach to new technologies, including AI. "Innovation and progress are key and it’s easy to be overly enthusiastic. But in our line of work it's also our responsibility, both for ourselves and our clients, to be able to distinguish between what truly helps facilitate our cause of finding solutions and what are more ‘trendy’ or ‘surface-level’ applications", he explains. “It needs to bring real added value in my opinion.”

Beyond the professional

As with his work, Tim is also curious when it comes to hobbies and interests outside of the professional environment. Besides cinema, he enjoys hiking with his Scottish wife and exploring remote locations, embracing nature, no matter rain or sunshine. On the other hand, the worlds of automotive and motorsport that captivated him during his earlier years of doing his PhD research still remain a favourite pastime in his life. As does working on classic cars from time to time. “I guess, in a way, restoring a car is not unsimilar to doing research. You’re looking for solutions to challenges that allow you to make it run better and drive more smoothly. Only it’s quite a bit more greasy,” laughs Tim.

Timothy’s unique blend of academic depth and hands-on corporate experience, together with an innovative approach to qualitative methodologies, helps to further extend boobook's capabilities to deliver exceptional strategic insights. His commitment to understanding our client's challenges and creating lasting solutions perfectly fits into the boobook mindset.  

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