Cutting-edge data science predicts purchase behavior while protecting consumer privacy

RevTrax, the industry pioneer and market leader in offer management solutions, and the exclusive Offer Management Platform (OMP) provider to hundreds of multinational consumer brands, announced the launch of data services to help brands better understand consumer sensitivity to pricing and promotion.

Using data science, RevTrax now offers its client partners the ability to leverage insights from billions of consumer engagement data points to determine ideal pricing and promotion in a privacy-by-design framework using state-of-the-art modeling techniques.

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“RevTrax’s technology empowers consumer brands to better understand ideal pricing and promotion for each consumer, drilling all the way down from general categories and classes of trade to specific products and retail banners,” said Jonathan Treiber, Chief Executive Officer of RevTrax.

RevTrax clients have been using these data services via RevTrax AI to optimize consumer offers with great success.  The Clorox Company – a leading multinational manufacturer and marketer of consumer and professional products – recently partnered with RevTrax and saw a 291% increase in units moved by leveraging this technology.

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“What’s most important are the data science breakthroughs that have enabled us to build this predictive engine without using any sensitive personal information or exposing any consumer private data,” said Seth Sarelson, Chief Operating Officer of RevTrax. “Brands need privacy-focused data partners and we are pleased to make a major effort to lead by example.”

Partners can leverage a real-time API or file sync to take advantage of this offering.  Data Services include:

1. Price Sensitivity: Returns a score between 0-100 based on the likelihood a customer is discount sensitive.
2. Full Price Buyer: Returns Y/N value to determine if a consumer is willing to pay full price or needs a discount to make a purchase.
3. Retailer Preference: Returns specific retailer names and rank of preference for a consumer.
4. Retailer Class of Trade Preference: Returns preferred retailer class of trade and rank of preference where a consumer is likely to make a purchase.
5. Best Time to Engage: Returns the “Day Part” for a given consumer that represents the best time to engage for a category.

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