Retail Store Recommender System

Example Customer

Major Retailers

Data Owner

Retail Outlets

Data Users

Marketers, Campaign Directors

Type of Data

Point-of-Sale, Transaction, Geolocation, Online Retail Activity, other Purchase-Related Data

Summary of Pain Point

Retailers could offer better recommendations if they knew what their customers were buying at outlets across their system (store, website, partner and subsidiary points of sale).

Access and use of customer data is complicated – regulations (GDPR) prevent data from being used across borders and subsidiaries.

Summary of TripleBlind’s Solution

Better recommendations, reduced spam, enforced privacy & compliance with global aggregation of data.

Coupon Recommender – Train a single model on multiple remote datasets (keeping the data private).

Return Trip Predictor – Using item and quantity purchased, predict when customers will return to the store.

Suspicious Activity Surveillance – Logically (and privately) combine multiple store’s data to perform aggregated queries.

Privacy enhancing computation allows the internal data science team to work without added complexities and within regulations.

TripleBlind’s distributed processing capability saves significant IT spend.

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TripleBlind’s innovations build on well understood principles of data protection. Our innovations radically improve the practical use of privacy preserving technologies, by adding true scalability and faster processing, with support for all data and algorithm types. We support all cloud platforms and unlock the intellectual property value of data, while preserving privacy and enforcing compliance with HIPAA and GDPR.