Retail

Challenge

The role of data in retail has expanded significantly with the rise of global e-commerce and mobile shopping, especially during and after the COVID-19 pandemic. Retailers increasingly rely on AI-driven analytics to optimize marketing strategies, recommend products, and drive customer engagement. However, consumers are concerned about who accesses their data, for what purposes, and whether their privacy is respected. 

Customers demand highly personalized recommendations that aggregate insights across different retailers, but strict privacy regulations prohibit direct data sharing between competitive entities. As a result, current recommendation systems are typically siloed and based solely on each retailer’s own transaction history, limiting their effectiveness.

TANGO solutions

TANGO provides a federated learning framework that allows competing retailers to collaboratively train AI models on anonymized purchasing data—without ever exchanging raw customer data. In this pilot, retail branches in Greece and Cyprus (both owned by the METRO Group) participate in cross-border data analytics using historical anonymized datasets spanning two years of transactions.

Retail IT managers use TANGO’s privacy-preserving platform to trigger data preparation, initiate analysis, and receive insights through secure authentication and access control. TANGO integrates tokenization, Federated Learning, and Explainable AI (XAI) to deliver accurate, transparent recommendations while preserving data confidentiality. Data processing is performed securely via TANGO’s EDAE and VIS components, providing managers with visual insights into customer preferences and market trends.

Managers from marketing, product, and sales departments at various levels can request analysis, visualize results, and make data-driven decisions to improve product offerings and marketing campaigns, all without compromising consumer privacy.