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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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