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Artificial intelligence is not just changing how people shop. It is changing the rules under which brands compete.

As AI tools make price comparison and feature benchmarking effortless, competitive landscapes become structurally more transparent. Specifications are extracted, ranked and displayed in seconds. Prices are surfaced without friction, increasing price awareness among consumers.

This is not a technological evolution. It is a strategic shift. If your offer can be reduced to structured, measurable attributes, it will be. And when measurable attributes dominate the comparison, price quickly becomes the most visible point of differentiation. In this environment, brands will need to prove more than ever that they are worth their price tag.

The feature trap: when functional dominates, price follows

AI systems are designed to structure information. They parse product pages, extract specifications and rank comparable attributes. In doing so, they privilege what can be measured: performance scores, technical features, material composition, size, speed, capacity.

When products or services are reduced to structured specifications, the purchase decision risks collapsing into a trade-off between measurable features and price. The richer layers of brand meaning become less visible in the comparison interface.

This is what we call the feature trap: when the architecture of comparison makes price the most salient lever.

Brand equity as a strategic shield

Strong brands show that pricing power does not stem from functionality alone. Customers routinely choose more expensive options despite credible and cheaper alternatives. Not because they failed to compare, but because they value more than the spec sheet reveals.

Patagonia is a clear example. In a category where many brands offer technically comparable outdoor apparel, customers willingly pay a premium. The differentiation does not live solely in fabric weight or water resistance. It lives in purpose, identity, credibility and the total experience of engaging with the brand.

The same dynamic can be seen in brands such as Ben & Jerry’s, Apple, Starbucks and Rituals. Their products can easily be compared on functional attributes, yet customers are willing to pay more because the value extends beyond the product itself.

Besides functionality, there are three other dimensions that determine value:

• Emotional: the feelings, identity and meaning a brand evokes‍
• Trust: the credibility and reliability consumers associate with a brand‍
• Experience: the total customer journey before, during and after purchase

AI comparison tools heavily favour the functional dimension. They are far less capable of capturing emotional resonance, accumulated trust or the lived experience of being a customer.

For brands with shallow equity, this asymmetry is dangerous. Their competitive advantage resides mainly in functional claims, which are easy to benchmark and easy to imitate.

For brands with deep equity, AI becomes less of a threat. Their value proposition extends beyond what can be neatly organised into comparable data points. Customers’ willingness to pay is anchored in something broader than specifications.

Pricing power is about willingness to pay

Pricing power emerges when customers perceive sufficient value to accept a higher price and remain relatively insensitive to changes within a certain range. Strong value perception raises the ceiling of willingness to pay and reduces price resistance. It also creates differentiation across segments: some customers are willing to pay significantly more because they value specific dimensions more strongly.

To avoid competing purely on price, brands must understand which elements of value truly drive willingness to pay, and how these differ across customer segments.

Customer insight as the engine of pricing power

To gain these insights and rebuild pricing power, you need to listen to your customers. Understanding the role of functional, emotional, trust and experience value starts with measuring how important each of these dimensions is for your brand.

The next step is to identify the drivers behind each dimension: which specific elements shape functional value, build trust, create emotional connection or strengthen the customer experience. These drivers may differ across customer segments, as different groups of customers value different aspects of an offer and are willing to pay different prices for them.

A combination of qualitative and quantitative research, complemented by market data and web scraping, helps build a clear understanding of what value means for your brand. Next you can quantify how that value translates into pricing power by measuring willingness to pay and price elasticity.

There are different approaches to measuring this, ranging from simple survey-based methods to more advanced techniques such as conjoint analysis, historical sales analysis or price experiments. Each method comes with its own trade-off between complexity and accuracy.

Competing on value in an AI-driven market

AI will continue to increase transparency. It will continue to make feature comparison faster and price differences more visible. That trajectory is unlikely to reverse.

The strategic response is not to resist transparency. It is to ensure that what becomes transparent includes more than just specifications.

Brands that invest in building and measuring multidimensional value can withstand comparability because their differentiation lives beyond the spreadsheet. Their pricing power is anchored in perception, trust and experience, not just in technical performance.

The real question is: is your brand equity strong enough to ensure that, even in a perfectly comparable environment, customers will still choose you? In other words: are you worth your price tag?

Need help mapping the drivers of your value and identifying how you can increase willingness to pay? Let’s talk.

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!