Marketers spend a considerable amount of time trying to understand who their customers are. They analyse purchase histories, build audience segments and look for similarities between people who buy particular products. It makes sense. If someone has bought from a category before, there’s a reasonable chance they’ll do so again.
But what about the people who haven’t entered that category yet?
Someone preparing to become a pet owner, for example, may spend weeks researching how their lifestyle will change before buying anything from a pet retailer. Someone moving from a city centre to the suburbs might begin reconsidering their transport needs or how they exercise. Their circumstances are changing, and their purchasing priorities could soon follow.
For brands looking to grow, these moments are worth paying attention to. The challenge is recognising them before consumers become obvious customers, particularly when traditional audience definitions offer little indication of where they might go next.
The problem with looking at existing customers
Most brands have a reasonably good understanding of the people already buying their products. First-party data can reveal purchasing patterns and provide useful information about existing customer relationships.
The difficulty comes when marketers try to use that information to find new audiences.
A retailer selling home fitness equipment might naturally focus on people who have previously shown an interest in exercise products. Yet someone who regularly attends a gym could become a potential customer after moving somewhere without convenient access to one. Their interest in home equipment may only become apparent once their circumstances change.
Looking exclusively at existing category behaviour could mean overlooking that opportunity.
There’s also a risk of making assumptions about what consumers want based on how they describe themselves. Someone might express a strong preference for a particular brand or product type, while their actual purchases suggest that convenience or price has a greater influence on their decisions.
Neither source tells the whole story. Bringing declared attitudes together with observed behaviour can help marketers understand these differences and question whether their established audience profiles still reflect how people shop.
That becomes particularly valuable when the objective is acquisition. A brand’s existing customers can tell it plenty about its current market, but much less about the people it hasn’t reached.
Following the changes that influence purchasing decisions
Of course, researching a subject doesn’t automatically make someone a future customer. Plenty of people explore possibilities without acting on them. That’s why individual signals need to be interpreted carefully.
Behavioural intelligence can help marketers recognise emerging interests before they translate into purchases. Retail data then provides evidence of how those interests are reflected in actual spending, including whether consumers are beginning to move between categories. Looking at both could help brands distinguish early curiosity from a more meaningful change in purchasing behaviour.
This could also challenge conventional ideas about category competition.
Brands typically examine competitors selling similar products, which is understandable when analysing existing market share. But consumers make decisions within the context of their wider lives. Someone moving home, for example, may begin spending in categories they previously had little reason to consider, while cutting back elsewhere. Looking at these wider behavioural changes could reveal connections between categories that traditional competitor analysis would miss.
Building a fuller picture of the potential customer
There’s a practical challenge here. Most businesses don’t have direct visibility into everything their prospective customers do.
First-party data is valuable, but it largely reflects interactions within a brand’s existing reach. Platform audiences can provide additional scale, although their definitions and underlying signals may be difficult to examine.
Third-party behavioural intelligence and data collaboration can help fill some of those gaps.
Connecting information from qualified sources, including retail activity and wider behavioural signals, can give marketers a broader view of how consumers move between interests and purchasing categories. A consistent identity foundation can help make those connections useful across environments, provided the data is handled responsibly.
Quality matters enormously. An audience that appears relevant because of one recent interaction may be very different from a group showing sustained interest through multiple behaviours.
Marketers should therefore examine how audience characteristics compare, where groups overlap and whether apparent changes are supported by other evidence. Recent activity may suggest an emerging opportunity, but historical information can help establish whether that movement is genuinely unusual.
AI could make this analysis more manageable by helping identify patterns across large datasets. Still, interpreting why someone might be changing their behaviour requires an understanding of the circumstances behind those signals. Human judgement and final approvals remain essential.
Finding growth before the category becomes obvious
Recognising an emerging audience is only useful if marketers can do something meaningful with that information.
For acquisition strategies, that could mean reconsidering when a brand introduces itself to potential customers. Waiting until someone is actively purchasing within a category may mean arriving after their preferences have already started forming.
Earlier behavioural signals could give marketers an opportunity to explore different audience segments and adapt their messaging to reflect changing needs. That might also mean reconsidering which audiences receive acquisition investment in the first place. Comparing emerging groups with existing customer profiles could help brands identify overlooked prospects, then test whether reaching them earlier delivers incremental growth.
This also requires a willingness to revisit established audience definitions. A segment that performed well previously may continue to be useful, but it shouldn’t automatically determine where future growth will come from.
As brands look towards 2027, lifestyle-driven category switching could become a more important consideration in audience strategy. Consumer circumstances will keep changing, sometimes in ways that established purchasing data struggles to capture.
The opportunity is to become better at recognising what those changes might mean. After all, tomorrow’s customers may already be showing signs of what they’ll need. They just haven’t started shopping in your category yet.
Growth often starts before the category becomes obvious. Lotame’s Addressable Audiences, available through the Lotame Data Exchange, can help turn behavioural intelligence into early signals of emerging audiences. Get in touch to explore who your brand might be missing.
