Navigating Unpredictable Consumer Behaviour with Data: A Q&A with Kristen Whitmore 

If marketers had a dollar for every time consumers said one thing and did another…well, they wouldn’t need better data. The growing disconnect between intention and action has made audience behaviour harder to pin down—and easier to misread. Kristen Whitmore, VP of Consumer Intelligence & Analytics at Lotame, breaks down how brands can cut through the noise, rethink their assumptions, and keep pace with consumer habits that rarely sit still. 

Q: How has the gap between consumer intentions and actual buys made behaviour harder to predict? 

A. We all have areas where we wish we were better than we are, or wish our actions better aligned with our values. You see this expressed in aggregate with consumer surveys, where responses often reflect aspirations more than real world behaviours. This is known as social desirability bias, and even in anonymous surveys it skews results towards what people think the most socially acceptable answer is. 
 
It’s a well-known phenomenon that can be corrected against with some smart data wrangling. When it comes to predicting consumer behaviour, declared data such as surveys needs to be combined with a portfolio of observed sources to keep audience profiles up to date, with data gathered from the past 30 days being particularly valuable. This also allows us to observe the gap between aspirational versus actual behaviour, which itself is a valuable signal. Knowing that an audience might feel like they are falling short of their ideals can be leveraged in campaign creative. 

Q. Why is looking beyond first-party data important for marketers? 

A. First-party data should be seen as the foundation from which more robust audience intelligence can be built. In isolation, it can tell marketers about their current customers and programs such as loyalty cards can result in incredibly rich first-party profiles, but without other data sources all marketers can achieve is retention. Without a prospecting strategy, audience, mindshare, and sales will shrink. 
 
Connect these first-party audience profiles to the wider data ecosystem and suddenly you can see cross-retailer purchasing, offline spend, wider category exploration, the media they engage with; the list goes on. This all begins with having a strong identity foundation, such as an interoperable ID spine that can connect first-party identifiers (website visitors and app users, typically) to other data sources, from publisher audiences and retailer shopper data to third-party marketplaces. 
 
This detailed map of consumer movements allows marketers to identify high-growth audience segments by seeing which are underrepresented within their first-party profiles. By targeting these prospective customers, marketers can ensure they achieve incremental growth rather than simply selling to the same audience again. 

Q. How can marketers adjust to unpredictable habits and still drive results? 

A. The more you rely on assumptions, the more you will struggle with unpredictability. Consumer habits change fast, and marketers need continuous audience analysis to keep up. I see so many situations where marketers build creative decisions around one assumed persona, but engagement data tells a different story. A brand might feature teenagers prominently in a campaign, only to discover that households with toddlers are driving the bulk of interaction. Or, when a product that was historically perceived as the value option migrates into the premium space. 
 
Marketers can adjust by testing new targeting attributes, comparing personas against each other rather than analysing them in isolation, and then tracking how audiences change over time. Audience data isn’t etched into stone, it’s part of a dynamic feedback loop that must be continually validated and challenged.  

Q. What other recent emerging and unexpected trends have you seen? 

A. I’m encouraged by the number of marketers who are moving from an activation-first to an analytics-first strategy. Historically, data platforms were primarily used to build an audience and push it into a DSP for activation. Now marketers are pausing to ask themselves: what can we learn about this audience before we activate it? 
 
Using data this way paints a richer picture of current and prospective audiences. For example, if you’re targeting affluent leisure travellers, you don’t just think about which channels can reach them, but what differentiates them from other segments. They may attend live events, over-index for health and fitness activities, or be engaged with fine arts. Such insights shape media planning and creative strategy before a single impression is bought. 
 
We’re also seeing broad demographic labels being challenged by more nuanced behavioural attributes. Targeting ‘tech enthusiasts’ isn’t enough anymore. Marketers want to understand things like purchase intent, category migration, brand affinities and spending patterns. Demographics are still layered in, but they’re a start rather than an end point. 
 
Access to data and analytics capabilities has improved dramatically. The challenge now is differentiation. Everyone has tools and scale, now the advantage comes from combining multiple validated data sources, understanding where that data originates, and using it to tell a more authentic story to your audience than your competitors can. 

What This Means for Marketers

As consumer behaviour grows more unpredictable, the margin for error keeps shrinking. Data alone isn’t enough—it’s how you interpret it, challenge it, and apply it that makes the difference. At Lotame, that’s where 20 years of experience shows up—not as a nice-to-have, but as the thing that turns signals into smarter decisions, and pressure into performance. 

In a world of uncertainty, expertise is the only true advantage. 

Why risk it? Get yours.