AI is quickly becoming standard infrastructure for marketing. But as technology becomes more accessible, having AI itself is unlikely to be the differentiator. The advantage will be the quality of the data.

That distinction matters as marketers try to keep pace with changing consumer behavior. According to the Interactive Advertising Bureau’s 2026 Outlook, 44% of buyers cite adapting to changing consumer habits as a top investment challenge.

This matters because much of audience marketing still starts with a relatively static view of the consumer.

Consider a retail shopper labeled as a Deal-Conscious Tech Upgrader: ages 25 to 44, household income over $75,000, and interested in technology. On day zero, the audience label is assigned. On day 18, they begin watching smartphone comparison content. By day 83, purchase intent is clear.

Static segments can miss changing consumer intent.

The challenge is not that audience segments are inherently wrong, but that they are often too static to reflect how quickly consumer interests, priorities, and purchase intent can change.

Audience Path’s proprietary data illustrates this dynamic in beauty, where content consumption rises around distinct seasonal, holiday, and self-care moments from December through July.

December’s surge points to interest driven by gifting, events, travel, and year-end routines. The spring rebound reflects a different set of needs around seasonal refreshes, weddings, graduations, and other life moments. The underlying audience may not have fundamentally changed, but the motivations shaping its behavior have.

A similar pattern emerges in luxury. Audience Path found that 47% of luxury shoppers plan to spend less this holiday season, even as they continue to watch connected TV (CTV) and engage with relevant content.

A “luxury shopper” segment can help marketers understand who a consumer is, but it offers limited insight into how their priorities are shifting, what they’re considering, or how close they are to a purchase.

Behavioral signals can help fill that gap by giving marketers a more current view of consumer intent. Content consumption, research patterns, repeat engagement, and other signals can reveal how interests are evolving in ways that a static audience definition may not capture.

For marketers, that means looking beyond “Who is my audience?” to also consider what that audience is signaling at a given moment, and how those signals should inform media planning, activation, and optimization.

Why vector-based targeting matters.

The need to interpret multiple signals together is one reason the industry is beginning to explore vector-based targeting.

Rather than building an audience around a single characteristic, keyword, or predefined segment, vector-based approaches can interpret multiple dimensions of information together to create a richer representation of an audience.

The concept is still emerging, but large agency groups are already experimenting with it. WPP Media has been developing vector-based solutions, while Dentsu has been testing the approach across online video, streaming, and digital display.

CTV is particularly interesting because audience signals are fragmented across platforms and environments.

But sophistication cannot come at the expense of transparency.

If AI creates hundreds of highly precise audiences but marketers cannot understand why someone belongs in one of them, one targeting limitation has simply been replaced with another.

The next generation of audience intelligence has to be explainable.

Recognizing the pattern before the outcome.

That principle is central to Dailymotion’s approach to Audience Path.

Audience Path draws from more than 5 billion monthly video streams and more than 13 types of synthesized signals to turn behavioral, contextual, and intent data into explainable targeting logic.

These signals aren’t intended to replace first-party shopper data. They add context, helping marketers understand how an audience evolves between the moments traditional data captures.

The next competitive advantage won’t come from simply having more segments or more AI. It will come from recognizing the pattern before the purchase.