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Predictive Purchase Data™

Replace third-party cookies with a privacy-first alternative that delivers personalization, while simultaneously protecting consumer privacy. 

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Deliver Personalization in a World That's Becoming More Private 

With new consumer privacy laws, the disappearance of third-party cookies, and web browsers cracking down on tracking, it is becoming harder for brands to deliver the type of personalization consumers expect. 

"Consumers are 2.1x more likely to view personalized offers as important versus unimportant." - Salesforce 

Over the past decade, RevTrax has analyzed how consumers behave to variances in pricing and promotions. Over 1.2 Billion data points and over 47 million consumer transactions across several consumer categories later, RevTrax is able to analyze both known and unknown audiences and place them into groups with similar spending habits. 

Armed with these insights, brands can deploy hyper-personalized offers and promotions with a higher chance of conversion to both existing and new audiences in a privacy by design framework. By using causal analysis, Predictive Purchase Data (PPD) can determine what it will take to motivate an individual consumer to convert - driving more sales, customer acquisition, repeat purchases and brand loyalty. 

Delivering promotions that match with audience's previous behavior is more likely to drive conversions than ones that are generic


  1. Leverage the power of behavioral personalization to increase conversions, enhance loyalty and drive sales without dependence on audience tracking technologies. 

  2. Improve the consumer journey and campaign outcomes by personalizing offers for both known and unknown audiences. 

  3. Increase consumer intelligence with an additional data layer that is non-intrusive and adheres to all consumer data privacy laws.

How It Works

Very similar to how Google's FLoC technology works, brands utilizing Predictive Purchase Data will not understand consumer behavior or interests at an individual level, instead they will understand how groups of consumers behave in regard to variances in pricing and promotions. 

  1. PPD uses an algorithm that expands on the traditional use of machine learning to classify consumers 
  2. PPD then uses casual analysis to determine what it will take to motivate an individual consumer to convert and then puts the individual into a group of thousands of other users with similar spending habits 
  3. These groups can then be used by brands to serve up personalized promotions at the right amount needed for conversion increasing sales, driving customer acquisition, enhancing engagement and expanding retention efforts. 

"63% of consumers will stop buying from brands that use poor personalization tactics." - Smart Insights 

Predictive Purchase Data proposes a new way for businesses to reach consumers with individualized offers, while at the same time allowing the people who are targeted to remain anonymous. 

PPD is an additional data layer which helps customize each individual's experience throughout the customer journey. 70% of consumers say a company's understanding of their personal needs influence their loyalty. (Salesforce)

Irrelevant marketing is a universal turnoff. "When personalization is done well, there's a 6.4x lift in satisfaction, further endearing people to the brand, cementing brand loyalty and encouraging them to shop more." (SailThru)

Engage Both Known and Unknown Audiences with 6 Types of Predictive Models

  • Price Sensitivity: Helps marketers predict how price sensitive a new or existing customer is within a product category.

  • Full Price Buyer: A simplified version of Price Sensitivity that helps marketers predict if a new or existing customer is a full-price buyer within a product category. 

  • Retailer Preference Rank: Helps brands or retailers understand how important each retailer is to a given customer for a specific category of spend. 

  • Retailer Class of Trade Preference: Tells a brand which type of retailer (grocery store, drug store, etc.) a given consumer is likely to purchase a category of products at (i.e. baby care).

  • Retailer Preference: Helps retailers understand where else a given customer is spending money on a certain category of product and create tailored messaging for those customers to win those purchases. 

  • Best Time to Engage: Helps a brand maximize marketing impact by deploying ads or messaging for a given consumer at the optimal time that consumer is likely to engage and convert.

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Personalization drives customer satisfaction, brand loyalty and repeat purchases.

Brands that leverage predictive analytics can increase their profits by up to 15%.

Learn how Predictive Purchase Data can help you reach your business goals. 


Speak to a specialist today to learn more.