LG Electronics Recommendation systems
Make the next recommendation personal.
A next-purchase model that translates customer history into ranked product-category recommendations for more relevant cross-sell and outreach.
Hands-on model development and delivery as part of the customer intelligence platform.
Production customer model
What will this customer want next?
A ranked set of product categories for each known customer, usable for category audiences, cross-sell planning, and personalized recommendations.
Conceptual workflowThe business problem
A broad product catalog gives marketing teams more choices than any email or recommendation slot can hold. A shared best-seller list cannot reflect what each customer already owns or may need next.
What I built
Built personalized category ranking from purchase history and ownership context, turning customer signals into a shortlist teams can act on.
Connected category relevance with purchase readiness and category-level spend, so audience selection can consider more than product affinity alone.
Preserved prediction history and evaluated recommendations against purchases that happened later, with best-seller rankings as a practical comparison.
How I evaluated it
Compared stored predictions with subsequent registered purchases. Examined ranking quality across purchase channels and customer histories, rather than relying on a single development score.
Built to operate
A recurring scoring workflow refreshes recommendations after new purchase activity and preserves historical outputs for evaluation.
What the work enables
Historical testing showed stronger next-category ranking than a best-seller baseline, with additional audience-selection value when paired with customer purchase readiness.