Personalized Product Discovery Engine

Online catalogs are large and customer attention is short: shoppers see the same generic banners and lists, browse without buying, and abandon search. Marketing teams spend weeks creating audience segments and creatives, so personalization is shallow and costly, driving high acquisition costs and low repeat rates.

Build a real-time personalization layer that combines behavioral signals, product metadata and business rules with a learn-to-rank model and template-driven creative generation; put humans in the loop for guardrails and ongoing tuning.

• Use a unified customer profile (CDP + event stream) and product embeddings (title, attributes, image vectors) as model inputs.
• Train a contextual bandit / learning-to-rank model to prioritize products per session objective (CTR, add-to-cart, margin-aware conversion).
• Generate and localize micro-copy and imagery variants with controlled LLM and image-generation templates; require marketer approval for new templates.
• Deploy via real-time API to web, mobile, and email; run continuous A/B/n experiments and automated policy-based rollout.
• Governance: privacy-preserving feature engineering (hashing, PII removal), bias checks on recommendations, drift monitoring and human escalation workflows.

Teams typically see 10-30% lift in CTR and 5-15% lift in conversion for personalized slots, with average order value rising 3-8% when ranking accounts for margin and bundle uplift. Operationally, marketing campaign setup time can fall 40-70%, enabling more frequent tests and a faster learning cycle; overall revenue-per-visitor improvements are illustrative and will vary by catalog size and traffic mix.