Brands need unique data to truly understand their customers’ motivations

The difference between category leaders and everyone else is the ability to successfully predict consumer behavior. That requires a holistic understanding of your customers—not just what they buy, but what they watch, listen to, who they follow, where they dine, and what sparks their interests: what makes them them.

For years, brands have relied on transactional data and traditional third-party insights to anticipate consumer needs. Think of every “you bought this, so buy this” recommendation you’ve ever received. But what you needed yesterday isn’t the sole driver of today’s needs. A brand’s own first-party data may help predict the next purchase, but it assumes consumers can be summed up by their previous expenditures. That’s not reality. Of course, second- and third-party data can help fill gaps, but concerns around data quality, privacy, and costs make this approach difficult. Finally, while platforms like Google, Meta, and Amazon hold valuable insights, the walls around these gardens have only gotten taller.

Consumers are more than their purchases. They are a totality of their tastes, preferences, habits, biases, and experiences. EMARKETER reports that nearly two-thirds of consumers consult multiple channels when shopping, but this is too late for real insight. What happens before they enter the purchase funnel? Did a podcast host suggest something? Did an artist drop a new album spotlighting a trend or brand (looking at you Beyoncé with your “LEVII’S JEANS” track)? What brands do their favorite influencers love? These insights are missed by optimizing for surface-level metrics rather than understanding the deeper forces driving consumer behavior.

An evolved grasp of consumer intelligence requires integrating unconventional data sources that capture human behavior. For example, Qloo combines diverse data sources—restaurant reviews, product reviews, social chatter, entertainment preferences, detailed metadata, and more—to provide a richer view of audience intent. Brands incorporating unique data points are making smarter decisions about campaign strategy, new product development, partnership selection, loyalty optimization, and more. And it works—Michelin’s Tablet Hotels, an online booking engine, saw a 273% increase in bookings using Qloo’s multi-faceted taste data to serve highly personalized property recommendations.

Now is the time to move beyond traditional data silos and embrace insights to create more dynamic, human-centric strategies. AI-driven analytics allow marketers to bridge these gaps, using machine learning to uncover unexpected correlations and gain a deeper understanding of audience resonance. Forward-thinking brands predict shifting preferences, optimize spend, and personalize engagement at every stage of the consumer journey.

The takeaway is clear: if you’re a growth leader needing to conquer the new landscape, your consumer intelligence must extend beyond a single platform for a comprehensive view of audience behavior. The winners will tap into unconventional insight sources—capturing the full spectrum of what influences consumer decisions. By leveraging richer, diverse data, companies can develop a holistic and forward-thinking approach to understanding their customers.

To explore how AI and machine learning are shaping advertising and engagement metrics, watch “AI and Tech Trends for Marketers to Watch in 2025,” a Meet the Analyst Webinar made possible by Qloo.

—Jim Jansen, Chief Revenue Officer, Qloo