AI product recommendation engine
NUT Botanicals · E-Commerce / Retail
Measured against a holdout/control group over the test period.
- Client
- NUT Botanicals
- Industry
- E-Commerce / Retail
- Scope
- Personalized product-recommendation engine for e-commerce.
Inside the project
Challenge
The client needed personalized product recommendations to improve conversion rates and customer engagement.
Solution
An intelligent recommendation engine tailored to user preferences and behavior — RFM clustering for customer segmentation, with cleaned and analyzed transaction and browsing data driving tailored recommendations.
Our approach
We cleaned and analyzed transaction and browsing data, segmented customers with RFM clustering, and modeled behavior to serve tailored recommendations. It runs a full lifecycle — data preparation, segment creation, product-affinity analysis, candidate generation, ranking, storefront/campaign delivery, experiment measurement and model refresh — surfacing loyal customers, at-risk customers and complementary product pairs, and measuring uplift against a control. Personalization respects consent, data minimization and frequency control, with no sensitive-health inference — so relevance improves without over-contacting customers.