Product Recommendation

Push the right product to the right consumer by building a recommendation system using collaborative filtering and machine learning.

This project offers an plug-and-play solution allowing retail analytics teams to build a recommendation system to push the right product to the right customers. More details on the specifics and requirements of the solution can be found in the knowledge base. This solution is available on installed instances and Dataiku Online.

Business Overview

Companies that successfully implement personalization drive 40% more revenue than the average company. And this should come as no surprise: indeed, 71% of consumers expect companies to deliver personalization and are more likely to shop with brands that they recognize, that understand them, and provide relevant offers and recommendations that grow lasting relationships. Recommending the right product to customers is now a must-do in order to secure market share and build loyalty. This can notably be done by implementing a recommendation engine based on a collaborative filtering approach which aims at answering a simple question: what items will appeal to customers who share similar preferences?

By answering this important question, brands can in turn recommend products that have not yet been purchased by a customer. The resulting outcome: product discovery; increased customer engagement; and improved revenue. With this solution, companies open an opportunity to optimize their customer engagement activities, starting with online experiences: offer a website landing page specifically tailored to logged-in users; a digital app connecting customers to personalized offers; promotional emails personalized based on purchase history; and much more…


  • Leverage our recommendation plugin to calculate user < > item affinity scores
  • Easily adjust the solution settings with our Dataiku App
  • Leverage Dataiku auto ML features to train a Random Forest model that will predict user propensity to buy an item
  • Allow Marketing professionals and beyond to navigate results and understand which products should be pushed to which customers, and for each product what are the top ten similar ones
  • Automate the pipeline to rebuild with new/fresh data.