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E-commerce & Retail

18% lift in average order value

AI Product Recommendations for Online Retail

Problem

An online retailer relied on static category pages. Shoppers struggled to discover relevant products, and merchandising updates required developer involvement for every campaign.

Solution

We integrated a recommendation layer powered by purchase and browse behavior, with a merchandising UI so the marketing team could boost collections without code changes.

Outcome

Average order value increased 18% within the first quarter, and campaign launches moved from weeks to days.

Workflow

  • →Aggregate product catalog and behavioral events
  • →Train lightweight recommendation rankings
  • →Surface personalized modules on PDP and cart
  • →Give marketers controls to pin and boost collections
  • →Track conversion impact in analytics dashboards

Tech stack

Next.jsPythonscikit-learnSegmentPostgreSQL
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