Measure how your audience sees, interacts and falls in love with your brand

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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Our Insights Director, Eva Vandenberge recently attended the annual ESOMAR Congress and in this interview, she shares her insights.

This annual event by the global association for market research and insights offers researchers and consultants from all over the world the chance to connect and discover the latest trends and innovations in their industry. More than 1,000 people from 78 countries attended this year’s congress in Athens and boobook’s Insights Director, Eva Vandenberge, was one of them. With over 135 speakers, choosing which sessions to attend was a daunting task, but judging by the insights and reflections she shares with you today, she mastered it gracefully.

It was not the first time you attended the congress. What keeps you coming back?

“Nowhere else do you get such a comprehensive image of the current state of affairs in market research and a glimpse into its future. The presentations from the industry's top minds are inspiring, and the informal conversations with researchers from all over the world are fascinating. While listening to their insights, success stories and challenges I get so many new ideas to help our clients even better with their business questions. The industry has been rapidly evolving since the arrival of AI. It is important to keep track of the developments and discover whether new technologies and tools can help us gain better and more reliable customer insights.”

Before we dive deeper into the AI topic: were there any activities you particularly enjoyed?

“I appreciated that ESOMAR organised the YES Awards: a global competition that allows young research professionals to share their - often refreshing - ideas with the public. From all the pitches submitted, a jury selected ten who got to share their 60-second pitch at the congress. Via live voting, the audience selected three finalists who could give their full presentation. The winning presentations were on cultural bias, regression analysis using AI and data collection via WhatsApp in emerging countries. As a young researcher, it is quite an honour to get the chance to share your findings with such an audience.”

“There were no Belgian candidates this year, unfortunately, but I am also a board member of the Belgian Research Federation CUBE, and at the CUBExEsomar event that will take place on 24 October, several young researchers will share their take on the future of research and insights. Hopefully, this will lead to some interesting pitches by young Belgian research talent next year.”  

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Did you discover ideas that you would like to implement yourself?

“Absolutely. One of the YES award winners talked about cultural response bias, something we also struggle with. Certain cultures have difficulty sharing negative feedback and judge everything so positively that we barely detect differences between groups and brands in our analyses, while in reality, of course, there are. In one of our studies, ran in India, we corrected for this during the data analyses, but we are now inspired to tackle this issue by asking different questions. Questions about behaviour instead of attitudes, for example, or questions with neutral rather than numerical scales, so there is no longer a better or worse option. I had a couple of interesting discussions about the topic and will certainly put the new insights to the test.”

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AI was undoubtedly an important topic in Athens. Is it already reshaping the industry?

“AI was a hot topic indeed. Many AI developments and solutions were showcased. And several speakers discussed the potential, but also possible pitfalls of synthetic data, meaning data that has been artificially created as opposed to collected from humans. This could be used in analysis in the same way human data is used. Filling in missing data is nothing new; statisticians have been maximising samples via imputations for a long time. It might be as simple as filling in the occasional empty response with ‘don't know’ or mean scores based on the rest of the sample. Other times more complex models are used to predict those missing values, based on respondents' other answers, and what the rest of the sample says. Using AI, we can now do much more than fill in the occasional missing value.”

What is already possible with synthetic data today?

“As described, synthetic data might be used to complete missing answers, but you can also generate additional cases. If, for example, your study lacks young men from a specific region, you can generate more of them to boost your sample. You could even generate synthetic respondents; virtual participants that provide answers to a survey just like human participants would, and whose answers you can analyse as you normally would. It sounds futuristic, but companies are already experimenting with it, although there is still a lack of trust. Based on what we have seen and tested ourselves, we believe this distrust is justified and we would not recommend making business decisions based on synthetic data at this point.”

It doesn’t look like human respondents will be obsolete any time soon?

“The future will tell. The presented cases show that synthetic respondents can generate reliable results if closed behavioural questions are used. But virtual respondents can’t tell you how they feel, and they can’t answer open-ended questions well. We are curious to see if and how this will evolve.”

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What are some of your own experiences with using AI?

“At boobook, we have done several test on how AI would handle a segmentation study, for instance. When comparing the segments AI came up with the ones we determined, some overlapped but others didn’t. As long as results remain unreliable, real data, captured from real people, remains a must. But, of course, we keep track of the new developments and will continue running tests.”

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What would you like to put to the test in the short term?

“There is quite some enthusiasm about using synthetic cases to map hard-to-reach groups, such as B2B audiences. Synthetic boosters in under-sampled groups would increase the reliability of the study. We have our doubts because it seems unlikely that this data would indeed be reliable. How can it be, when your research pool is small to begin with? But the proof of the pudding is in the eating, so we plan to run a test and find out for ourselves if it could be useful.”

“What was also discussed at the congress, and I very much agree with, is that you should mainly use AI when a human being can’t add value. If using AI to script and translate questionnaires means resources are freed to generate better insights, we can only encourage it. But in terms of data collection and insight generation, we still need actual respondents and human researchers to bring brands closer to their consumers.”

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Which speaker left a lasting impression?

“It is not easy to choose just one, but I will not soon forget the closing keynote by Vivienne Ming, an incredibly intelligent woman who has travelled an unlikely path and achieved so much. She has founded several start-ups and solving seemingly unsolvable problems is her life's goal. She believes AI can be truly transformational and has developed several AI tools, but she is most passionate about maximising our human potential. To do so we need to create open cultures and safe spaces where people dare to experiment. Most of our initial ideas are wrong she claimed, so it is only through failing that we will stumble upon the great ones that can potentially change the world. It left me inspired and proud to be part of the boobook family, where we are all encouraged to share our ideas and get the chance to pursue them.”

“Especially now that there are so many new developments, companies must give their employees room to try new things. If you want to grow you need to accept that you will also fail sometimes. Sharing experiences and ideas is more important than ever, so I would leave you with a warm invitation to connect with us if you want to explore the possibilities of AI together!”

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How can businesses achieve unparalleled growth and maximize profitability in today's competitive landscape? At boobook, we believe the answer lies in our data-powered strategic consulting, anchored firmly on four interconnected pillars: brand, product, pricing, and customer/consumer. By understanding and leveraging these fundamental elements, we guide businesses toward excellence and sustainable growth.

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In the coming weeks, we're excited to share a series of articles focusing on these pivotal pillars, more specifically on how strategic price setting is driven by the other 3 pillars. This insight is crucial in an era where gaining a competitive edge is not just beneficial but necessary for survival and success. Understanding the intricate relationship between these pillars enables businesses to boost growth and ensure maximum profitability effectively.

Kicking off our "Pricing 360" series, we begin with a topic at the heart of product success or failure in the market: Brand perception. The way consumers perceive your brand has a profound impact on their purchasing decisions, a fact no business can afford to overlook. We will demonstrate how a deep, nuanced understanding of your brand, seen through the eyes of your consumers, can empower you to make pricing decisions that not only resonate with your target audience but also drive growth and success.

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Why having a clear view of your brand perception matters

The equilibrium between brand perception and pricing decisions is crucial for businesses striving for strategic growth and a stable market position. It involves a complex analysis of supply and demand economics combined with brand psychology. Contrary to common belief, a high price doesn't always signify premium value, and a lower price doesn't necessarily mean affordability. To truly enhance pricing strategies, understanding the signals your brand sends out in the market is essential.

Pricing strategy involves setting the optimal price for your products or services to maximise revenue or profitability and achieve business objectives - without pricing your brand/products out of the market or course. Pricing decisions will significantly impact sales volume, but also market positioning, and, indirectly, brand perception. The right pricing strategy can also help you attract (new) customers or maintain competitiveness.

So, what is the connection between these two essential components of your overall marketing strategy, and why should you align them? Brand understanding and aligning your pricing strategy with your desired brand positioning is essential because it helps you to create a consistent and coherent brand experience for your customers. When you achieve an accurate and positive customer brand perception, this, in turn, enhances their trust and loyalty, which is translated into increased sales and profitability.

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When companies benefit from brand understanding

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Here’s an overview of when and why companies will benefit from understanding how the market perceives their brand.    

• Linking pricing to brand positioning: Understanding how customers/consumers perceive your brand helps you identify and define your brand's unique position in the market. It allows you to understand how customers perceive your brand, and it enables you to identify the key differentiators that set your brand apart so you can create a compelling value proposition. This, in turn, influences pricing decisions. If your brand is perceived as offering unique benefits or solving specific customer problems, customers may be willing to pay a premium price. For example, if your brand is associated with high-quality and premium features, you can set a higher price point to reflect that perception. On the other hand, if your brand is positioned as affordable and value-driven, a lower price point may be more suitable.
• Launching a new product or service: Brand perception analysis is extremely valuable when developing or launching new products, product features, services or even (sub)-brands.
  It helps companies understand customers’ expectations, motivations, or feelings towards your brand. By mapping the target market's perception of your brand as well as your competitive landscape, companies can make strategic decisions to extend their offer purposefully and successfully.
  
• Rebranding/repositioning: When a company undergoes rebranding or decides to reposition its brand in the market, brand perception analysis becomes essential. It allows companies to assess their current brand identity and their brand DNA and identify gaps or opportunities for improvement. Companies can strategically plan and execute their rebranding efforts by understanding how the brand is currently perceived vs. how it should be perceived.

• Competitive analysis: Brand understanding is an excellent approach to conducting competitive analysis. By mapping competitors' perceptions and positioning, companies can identify opportunities for “the blind spots in the market” and build further on their strengths and weaknesses. This analysis provides the foundations for developing effective strategies to gain a competitive advantage and stand out in the market.

• Marketing and communication: Developing effective marketing and communication strategies isn’t the easiest task. However, with brand perception analysis, companies can better understand how their brand characteristics align with customer needs and expectations. By leveraging these insights, companies can create targeted marketing that resonates with their key audience, increasing brand awareness and customer engagement.

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How to measure and understand brand perception

There are different ways to capture the market's view of your brand and your competitors. Note that the view of competitor brands is critical in this exercise, as a brand is always perceived in comparison to other brands.  

One view might be to look at sales. Who sells most and least, especially how the volume changes when a brand is on promotion, as promos tend to be less necessary for more premium / high-quality brands. However, we know that sales (even promo sales) will only give a limited view of how brands are perceived.  

Another possibility is to collect brand perception views from the sales team, as they are often the closest to the market. However, these views might be biased towards specific customers or retailers.  

The best option is to talk directly to the consumers to get their view on how they perceive different brands—brands they either bought or are aware of. I.e. Which brands do they associate with high quality, reliability, trendy, premium, fun, innovative, mainstream, etc.? Alternatively, some of these perceptions can also be captured through social media analysis.  

Typically, the outcome of such an exercise is presented in a two-dimensional map called a "brand image map". It visually shows which characteristics brands are associated with and in which competitive space they sit, i.e., who the main competitors are from a brand image perspective.  

Ideally, a brand sits in white space, meaning competitors do not closely surround it and are away from the middle. The more a brand sits in the middle, the more its image is less distinct.  

Depending on the brand's goals, its desired brand image will differ. Value-driven brands strive to be considered premium and exclusive, while volume-driven brands will be closer to the mainstream.

In conclusion

Brand perception analysis is a vital tool for your branding and pricing strategy. When used effectively and complemented with other types of insights it can support businesses in strategic decision-making and help build strong brands.

Brand image analysis and pricing strategy are clearly intertwined in building a successful brand. By understanding the perception of your brand and its unique value proposition, i.e. your brand identity, you can effectively align your pricing strategy to maximise profitability and achieve your business objectives.  

Combining the insights gained from a brand image map with a landscaping exercise on current competitive pricing will further help you establish the optimal price (as in a price in line with the market) for your products or services, ensuring that you attract the right customers and drive long-term success.  

If you need to optimise your brand positioning and pricing strategy, get in touch! Our team is here to assist you in developing your ideal pricing strategy.

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