Retail: Customer Behaviour Analytics

Scenario: Fashion retailer StyleCorp wants to improve recommendation algorithms but has limited customer data compared to e-commerce giants. Traditional data sharing partnerships require exposing sensitive customer shopping behaviors and preferences.

RONNE Solution: StyleCorp encrypts customer purchase history, browsing patterns, and demographic data, then contributes to a privacy-preserving recommendation model. Multiple retailers participate without revealing individual customer preferences or competitive pricing strategies.

Business Impact: StyleCorp's recommendation engine improves conversion rates by 31% through access to broader behavioral patterns while protecting customer privacy. The company maintains competitive advantage by keeping specific product preferences and pricing data confidential.

Revenue Generation: StyleCorp earns $450K annually licensing encrypted customer behavior data while improving sales performance by $2.8M through enhanced AI recommendations. Customer trust increases as privacy-first approach becomes a competitive differentiator in the market.