Understanding and anticipating the needs of potential customers is more crucial than ever in the modern advertising landscape. Beyond the ever-growing clutter in the online advertising space, this need has recently been further amplified by significant shifts in online data privacy, such as Google's decision to phase out third-party cookies. This move has pushed marketers to find more reliable and privacy-compliant ways to find and target potential customers.
In this regard, predictive audiences have emerged as a key approach to expanding a brand’s audience in a targeted, relevant way.
Predictive audiences refer to groups of potential customers identified and targeted based on predictions made by analyzing a variety of data sources. These sources might include transaction histories, browsing behaviors, and demographic data. By employing advanced analytics and machine learning, marketers can predict future behaviors and tailor their marketing strategies accordingly. This approach not only complies with new privacy standards but also enhances the efficiency and effectiveness of marketing campaigns.
Let’s take a look at ways in which predictive audiences can help marketers expand their audiences while also connecting more deeply with existing customers.
Predictive models can analyze characteristics and behaviors of a brand’s existing customers to identify their patterns of behavior and then identify new prospects who are likely to take similar actions. This method, similar to (but not the same as) look-alike modeling, expands your reach by targeting new individuals who are likely to be interested in your products or services but are not yet engaged with your brand. For example, a fitness apparel brand could use data from its current customer base to find audiences who frequent similar websites, engage in fitness-related activities online, and share similar purchasing patterns, but who haven't purchased from the brand yet. In essence, the models can predict the people who are most likely to take the actions a brand desires.
Predictive analytics can identify signs that a customer might be at risk of leaving your brand. By addressing these concerns proactively, companies can retain more customers and maintain a stable revenue base. For example, a streaming service could analyze viewing patterns and predict when subscribers are likely to cancel their subscriptions. Before these customers churn, the service could offer them a customized package of shows or a special discount to re-engage them.
Predictive audiences can help brands identify which customers are more likely to be interested in additional products or upgrades. This strategy enhances customer experience by providing them with options they are likely to find useful while also increasing the average order value. For example, a telecommunications company could predict which customers are likely to be interested in an upgraded data plan based on their usage patterns and then target these customers with tailored ads that highlight the benefits of upgrading.
Predictive models can help uncover untapped markets by analyzing existing customer data to find unserved or underserved geographical locations or demographic segments. This allows brands to expand into new areas with a higher certainty of success. For example, a beauty brand could use predictive analytics to identify potential customer segments in geographic areas where they have limited penetration. By analyzing the preferences and demographics of regions where they are already successful, the brand can tailor their marketing strategies to appeal specifically to new audiences in new regions.
Predictive audiences can go beyond traditional notions of audience data to also enhance the concept of contextual targeting. In anticipation of the deprecation of third-party cookies, SPARC, powered by Nativo Predictive Audiences, was developed as a key component to future-proof advertising strategies, providing marketers with an effective and privacy-first alternative to traditional cookie-based audience targeting methods.
Unlike data targeting, SPARC, powered by Nativo Predictive Audiences, uses engagement data from our exclusive content offerings to build an audience model based on behavior rather than attributes. Combining proprietary data, contextual signals, and machine learning, Nativo finds and delivers new users most likely to engage with your ad and convert. This allows advertisers to reach their desired audiences even on browsers that block or limit the use of cookies.
SPARC, powered by Nativo Predictive Audiences, technology enables brands to find new users within their target audience and identify those most likely to engage with branded content. The demonstrated performance lift over traditional third-party audience targeting has been impressive, with clients achieving notable results across all categories. On average, SPARC outperforms third-party data targeting with a 43 percent lift in viewable clickthrough rate and 48 percent lift in time spent on content.
As the landscape of digital marketing continues to evolve, the ability to predict and respond to the needs of potential customers becomes increasingly vital. Predictive audiences offer a powerful tool for marketers looking to expand their reach in a targeted, effective, and privacy-compliant manner.
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