What Are the Best Use Cases for Predictive Audiences?

Take a look at the top use cases for predictive audiences

In a marketing landscape where data has become the most valuable currency, the ability to leverage that data to predict consumer behavior is a game-changer within advertising strategies. That’s why predictive audiences are front and center, particularly in conversations about how our industry can bridge the gap being left by Google’s deprecation of third-party cookies in Chrome. 

Predictive audiences, derived through a combination of data analytics and machine learning, are designed to forecast future consumer actions based on past behaviors. They can be built by analyzing a wide array of data points: browsing habits, purchase history, social media interactions, demographic information, and others. By employing algorithms that detect patterns and predict outcomes, brands can create highly targeted marketing campaigns that cater to the needs and preferences of different consumer segments.

Let’s take a look at the top use cases for predictive audiences, as well as what those use cases look like in action. 

Finding New Customers

One of the most compelling uses of predictive audiences is to identify and acquire new customers. This is particularly effective for expanding a brand's reach beyond its existing customer base, targeting individuals who share similar characteristics with current customers.

Imagine a brand that sells eco-friendly household products. By analyzing the behaviors of their existing customers, such as frequent purchases of green products or engagement with environmental causes online, the brand can use predictive modeling to identify potential new customers who exhibit similar behaviors but have not yet made a purchase. The brand could then target these individuals with tailored advertising campaigns that highlight their eco-friendly values and product benefits

Enhancing Product Recommendations

Tailoring product recommendations using predictive audiences can significantly improve the shopping experience by suggesting items that customers are genuinely interested in. This approach boosts not only sales but also customer satisfaction by making shopping more convenient and personalized.

An online fashion retailer might use predictive analytics to understand the buying habits and style preferences of its various audience segments. It could then offer personalized apparel recommendations through its website or via email marketing, suggesting items that complement previously purchased products. For instance, if a customer frequently buys vintage-style dresses, the retailer might recommend accessories that match that style, thus increasing the likelihood of further purchases. In addition, predictive audiences might uncover less-obvious buying patterns among customers and be able to put those to use in recommendations as well.

Optimizing Marketing Spend

Predictive audiences also enable brands to allocate their marketing budgets more effectively by identifying which segments are most likely to respond to specific marketing activities. This targeted approach helps maximize ROI by focusing resources on the most promising opportunities.

A video game company could use predictive modeling to determine which gamers are most likely to be interested in a new game release based on their playing history and engagement levels with previous titles. The company could then concentrate its marketing efforts on these segments with high engagement ads and exclusive previews, rather than spending indiscriminately across a broader audience.

Re-engaging Inactive Customers

Predictive audiences can be particularly useful in identifying customers who have not engaged with the brand for a while and might be at risk of churning. Understanding the factors that contribute to their inactivity allows brands to re-engage them with relevant and compelling content.

A subscription-based streaming service might notice that a segment of users who once frequently watched horror movies hasn’t logged in for months. The service could send these users personalized emails highlighting a new horror series known to be popular among other viewers with similar content tastes, along with a limited-time free upgrade to a premium plan. This targeted approach can reignite their interest and bring them back to the platform before they decide to unsubscribe.

Deepening Consideration 

Finally, predictive audiences are particularly useful for marketers looking to improve their audience engagement within the mid-funnel, where potential customers are trying to make decisions. In this regard, predictive audience solutions that build on an understanding of content, and audiences’ engagement with content, are especially valuable. 

Recently, a leading luxury Japanese automaker partnered with Nativo to improve the scale of their campaigns and deepen consideration for their vehicle. To do so, the automaker leveraged Native Display with Nativo Predictive Audiences, which uses proprietary engagement data to incorporate non-cookied audiences into a scaled targeting solution. The strategy was designed to find qualified automotive consumers in the consideration phase and drive them to the brand’s site. Ultimately, the automaker achieved a 50 percent lower CPA with Nativo Predictive Audiences compared to third-party data segments, as well as a 2x greater CTR and 2x greater click-through conversions.

Predictive audiences are transforming the way brands find and interact with customers. By leveraging the power of data analytics and machine learning, marketers can not only predict consumer behavior but also shape it. From acquiring new customers to enhancing user engagement, the strategic application of predictive audiences enables brands to create more personalized, effective, and efficient marketing campaigns. 


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