How Predictive Audiences Drive Performance Across Both Established and Emerging Categories

Overview

Predictive Audiences represent the next evolution of audience targeting.

By learning and adapting throughout the campaign by leveraging machine learning, real-time campaign signals, behavioral data, and SKU-level intelligence, Predictive Audiences identify consumers most likely to convert—whether they are shopping within a mature category like TVs or exploring products in an emerging technology category.

Across both established and emerging categories, Predictive Audiences helps brands expand beyond traditional audience strategies, improving campaign efficiencies and driving stronger business outcomes.

The Challenge

Reaching the right customers.

Whether marketing established products with years of purchase history or newer products with limited customer data, brands face a common challenge: reaching the consumers most likely to convert.

In mature categories, traditional first-party audiences can become constrained by known customer segments, limiting scale and incremental growth. In emerging categories, limited historical purchase data makes it difficult to identify and target qualified prospects efficiently. As a result, relying solely on historical audience signals can restrict performance and leave valuable opportunities untapped.

Marketers need a more adaptive approach—one that could move beyond historical assumptions, uncover new high-intent shoppers and accurately identify future buyers regardless of category maturity.

Outcome

A great complement.

The results demonstrate that Predictive Audiences deliver value regardless of category maturity.

For established categories, Predictive Audiences complemented existing first-party audience strategies by uncovering qualified consumers outside known customer segments, helping advertisers maintain efficiency while increasing scale.

For emerging categories, Predictive Audiences filled the gap created by limited historical purchase data, using real-time signals and SKU-level intelligence to identify high-intent consumers to optimize performance dynamically.

In both use cases, advertisers benefited from:

• Greater audience reach
• Improved conversion efficiency
• Higher revenue generation
• Stronger return on ad spend
• Lower acquisition costs

Conclusion

Greater scale, higher revenue, stronger ROAS.

Predictive Audiences are a powerful audience solution across the full product lifecycle—from mature categories with extensive purchase histories to emerging categories with limited historical data.

While first-party audiences remain an important foundation, Predictive Audiences unlock additional performance by combining machine learning, real-time campaign signals and product-level intelligence to identify consumers most likely to convert.

The results demonstrate that brands can achieve greater scale, higher revenue, stronger ROAS and lower costs by integrating Predictive Audiences into their audience strategy. As marketing continues to evolve toward more signal-driven and privacy-conscious approaches, Predictive Audiences offer a smarter, more adaptive way to reach future customers and maximize campaign performance.