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Factual Introduces New Machine Learning-Based Predictive and Loyalty Audiences

As we head into the busiest month of the year for marketers and advertisers, we’re excited to introduce a significant update to our Audience product that further bolsters our roster of targeting solutions: Predictive and Loyalty audiences built using machine-learned predictive insights.

Beginning today, marketers will have access to new Predictive Audiences and Loyalty Audiences, both built on sophisticated visitation pattern analysis, which will further enable marketers to construct highly scalable and accurate audience segments based on real-world consumer behavior and designed for ROI. We’ve also added over 100 ready-to-use audience segments in every vertical, including auto, retail, and QSR.

Our Predictive Audiences are built by developing an understanding of visitors to a place category and mapping their visitation patterns beforehand. Using Factual’s Observation Graph, consumers most likely to visit a category based on these patterns can be segmented into audiences, giving marketers the ability to connect with consumers before they set foot in a brand’s retail location.

Predictive Audiences are designed to address specific use cases to identify and target consumers. For example, Predictive Audiences built for the auto industry are designed to identify and influence consumer decision-making as they consider which vehicles to purchase and dealerships to visit. Predictive Audiences for auto include:

  • 6-Month Predictive Auto Shopper: Consumers who exhibit behavioral patterns that indicate they are likely to visit an auto dealer in 6 months. This could imply that they are at the top of the funnel and will likely start to consider whether to make an auto purchase.
  • 3-Month Predictive Auto Shopper: Consumers who exhibit behavioral patterns that indicate they are likely to visit an auto dealer in 3 months. This could demonstrate that targeted messages from brands could help these consumers decide which vehicles they might consider for an upcoming auto purchase.
  • 1-Month Predictive Auto Shopper: Consumers who exhibit behavioral patterns that indicate they are likely to visit an auto dealer in 1 month. This could demonstrate these consumers will likely be in-market for a vehicle and are about to start visiting dealerships.

In addition to Predictive Audiences, we’re introducing Loyalty Audiences, which help marketers effectively target consumers based on their level of engagement. With Loyalty Audiences, marketers can identify casual customers that may convert into brand advocates, isolate customers who visit their brand frequently, stay top of mind with new visitors, and more. Loyalty Audiences are currently available for more than 700 chains, with more being added regularly.

Segments within Factual’s new Loyalty Audiences include:

  • Brand Loyalists: Consumers who are loyal and exclusive to a brand
  • Cross-Shoppers: Consumers who frequent a brand’s industry but are not loyal to any particular brand
  • New Visitors: Consumers who historically were not visitors to a brand but have recently visited
  • Returning Visitors: Consumers who have consistent visitation to a brand
  • Churned Visitors: Consumers who used to visit a brand but have not been seen recently

Finally, we’re releasing more than 100 new ready-to-use audience segments that span all verticals. These new segments join the more than 1,000 existing audience segments in our standard taxonomy and can be easily activated across many of our top partners, including Centro, Google Display & Video 360, MediaMath, The Trade Desk, and Xandr.

“The Click-Through-Rate on Factual Audiences is far exceeding other data elements we’ve been using. Combined together they’re an ideal balance of high CTR and conversion rates. We’re clearly reaching the right people now, and users are interacting with our ads.” – Steelhouse, Factual Customer

To learn how you can use Factual Audiences to build highly-customized, scalable audiences based on real-world consumer behavior, contact our team here.

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