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TripleBlind at Work: Retail Store Recommender System

Our Use Case blog series has shown how TripleBlind’s Blind Exchange API can facilitate collaborative breakthroughs for healthcare and financial institutions, allowing the sharing of insights derived from sensitive data that has historically been guarded by privacy regulations. TripleBlind’s Blind AI Tools also allow collaboration in everyday scenarios that directly benefit consumers, while respecting and enforcing their rights to privacy.

Two industries that stand to greatly benefit from this new set of tools are the consumer packaged goods (CPG) and retail industries. Apart from usage to manage functions that directly benefit consumers, like more accurate inventory management and personalized coupon recommenders, retail outlets can collaborate with other outlets they share customers with to better predict consumer buying behavior.

Customers often frequent multiple retail outlets to do their shopping due to preferences in different products, availability, price promotions and more; however, no two consumers follow the same path to purchase. Using data including point-of-sale (POS), customer transaction, geolocation, online retail activity, and more, retailers can better understand customers’ shopping patterns and interests, making it easier to predict buying cycles, anticipate repeat purchases, and ensure the right goods are always in stock.

With a conglomerate of each customer’s buying personalities across brick-and-mortar stores and online, outlets can provide more personalized recommendations and reduce spam. Customers and retailers also benefit from a security standpoint, privately combining activity from multiple stores can be used to logically detect suspicious activity or potential credit card fraud.

Most people agree that targeted advertisements can be uncomfortable. While we would all likely enjoy more specialized promotions and deals, currently we are often left with a feeling that our privacy was potentially violated in the process of delivering ads that, say, relate directly to a casual conversation you were having with a friend. TripleBlind offers a solution which gives a win-win option for consumers and retailers. Consumers’ privacy is protected, as retailers and advertisers never get to learn any private information about individuals, and retailers get to deliver specialized ads to their target audiences.

Centralizing data across companies can be time-consuming and expensive, but because TripleBlind’s APIs present themselves similarly to widely-used frameworks like Pandas, PyTorch, Scikit-Learn and Tensorflow, only a few lines of code are needed to add privacy to an enterprise’s existing infrastructure, saving retail companies significant IT spend and reducing time-to-insights during collaboration.


To learn more about TripleBlind, connect with us on Twitter and LinkedIn. Contact us at to schedule a free demo.


Read more from our Use Case blog series:

TripleBlind At Work: Use Case Series
TripleBlind at Work: Brokering Genetic Data
TripleBlind at Work: Mayo Clinic
TripleBlind at Work: Alternative Data
TripleBlind at Work: Early Indication Trial Reporting