Intelligence, at its core, involves acquiring knowledge, understanding complexity, and adapting to evolving information. Within the world of advertising, intelligence is the vehicle that helps brands navigate the sprawling number of consumer touchpoints and focus on effective ad spend that drives stronger outcomes. Building intelligence as multi-dimensional as people is what empowers brands to understand the nuances of consumer behaviour and deliver growth on an otherwise limited horizon.
Aligning brand interest with consumer interest is par for the course, but what more can we unlock when customer-centricity is fostered over time by holistically understanding what matters to consumers? Brands move from generic groupings and limited contexts into relatability and relevance. Consumers feel seen, allowing deeper connections that establish brand loyalty and equity much more sustainably than simply getting in front of them as often as possible.
From identity to intention
People aren't captured by demographic generalisations, generic targeting segments, or static IDs. Two consumers placed into the same bracket may be treated the same by a marketing strategy, yet dig a little deeper, and you'll find they have completely different tastes, interests, and behaviours. Grouping individuals into blanket audiences prevents brands from properly connecting with individual consumers and differentiating their targeting from competitors.
I love talking about paradigm shifts, so here we go: there are significant benefits to transitioning from identifying one-dimensional audiences to analysing multi-dimensional signals. Every attribute learned about a customer or seed audience becomes a valuable input for scaling out, offering brands the chance to gain fresh, pertinent, and creative engagement with existing and prospective consumers. Looking at dynamic signals also delivers a critical advantage that pre-defined segments and IDs alone can't: differentiation. When your targeting is built on the same off-the-shelf audiences your competitors are buying, your strategy can only ever be as distinctive as theirs.
By drawing on relevant signals, brands gain insight into the micro-decisions consumers make across the spectrum of their daily lives. Communities, intent, motivation, and context surface rather than sociodemographic summaries. The result is intelligence that reflects people, not just buyers.
Endemic data collaborations are the first step on this journey, and they’ve already proven the power of first-party signals working in concert. The opportunity now is to strengthen that foundation with an even fuller picture.
Redefine endemic in relation to the consumer, not the category
Retail media has led the way with consumer packaged goods (CPG) brands leveraging shopper data for insights, activation, and measurement to drive and prove significant sales uplift. Travel, financial services, and gaming networks are following suit. These category-aligned collaborations have successfully demonstrated their value and become essential.
The next frontier lies one step beyond. As data collaboration continues to mature, the focus is less on staying endemic to the brand and more on becoming endemic to the person being addressed. A consumer is defined by far more than just one category. How a person travels, spends their leisure time, shops, consumes media, forms habits, thinks about family planning, navigates property or car ownership, and acts on their values amalgamates to a holistic view of consumers, present and future, that is pertinent for any brand in any category.
If endemic sources provide the launchpad to stronger outcomes, non-endemic signals provide untapped insights and new growth opportunities. Both together bring brands closer to understanding the full spectrum of consumer behaviour.
Building intelligence in practice
The challenge for most brands isn't recognising this opportunity; it's knowing where to begin. Today, the average brand can access few data points about its audience, namely on-site and possibly decoupled retailer purchase behaviour, though often not even across the whole group portfolio. The reality begets a limited view of who their customers really are out in the wild and what drives their decisions. Building intelligence is a process of layering, of connecting owned data with second- and third-party signals unique to that brand.
Layer 1: Endemic foundation
Start with category-aligned partners. For example, a CPG collaborates with a retailer, or an airline partners with a hotel group. These foundations reveal purchase patterns, category preferences, and the timing and frequency of decisions within an advertiser's space.
Layer 2: Adjacent categories
Expand into complementary, non-competing sectors that share the same customer in other contexts. An automotive brand partnering with an EV charging network is a nice example: same driver, different moment in their journey. The intelligence gained extends beyond point-of-purchase: post-purchase behaviour, usage patterns, lifestyle indicators (commuting versus leisure), brand-loyalty signals, and infrastructure preferences.
Layer 3: Non-endemic diversification
Then come the partnerships that, on the surface, look unrelated: travel companies, sports rights holders, telcos, entertainment platforms, and financial services. This is where the real differentiation flourishes. Connectivity patterns, family composition, lifestyle preferences, and aspirational behaviours round out a picture no single category could ever produce on its own. This holistic view of consumers means brands can relate and connect with them on a personal level, rather than working from overarching assumptions.
The result is a meaningful expansion from two or three nodes of knowledge to six or more dimensions. Moreover, the intelligence produced is proprietary, differentiated, and exclusive to the brand, instead of relying on centrally commoditised datasets to which competitors also have access. Each partner contributes what they know best, and, together, they generate collaborative intelligence that's greater than the sum of its parts.
The intelligence engine
Decentralised data collaboration is the infrastructure that underpins intelligence. Companies only feel confident bringing the full fidelity of their signals to the table when robust safeguards and controls protect their data. A decentralised approach delivers exactly that: data never moves or commingles, and each partner always retains complete control over their contributions. This approach allows brands to scale private data networks with peace of mind, turning a series of one-off collaborations into a continuously evolving data ecosystem of faster, smarter intelligence.
The virtuous cycle of intelligence, performance, and trust
Peering around the corner, this intelligence gives brands an enduring data advantage and the strongest possible competitive foundations from which to apply AI models and agentic workflows to their marketing tactics - not to replace the human connection they set out to create, but to make that connection scalable.
Once the flywheel is spinning, brands gain an unprecedented understanding of their customers, which translates directly into better performance. Consumers, in turn, receive more emotionally connected and relevant ad experiences with the reassurance that their data privacy remains fiercely protected at all times.
