Market Basket Analysis

Extract key business patterns from your sales: optimize product assortment and pave the road to recommendation.

This project offers a plug and play solution allowing retail analytics teams to conduct market basket analysis of their past customer transactions. With easy tailoring of the project through a Dataiku Application and capacity to adjust the data flow to their needs, the Market Basket Analysis solution enables retail companies to learn purchasing patterns and identify new business opportunities. More details on the specifics of the solution can be found on the knowledge base. This solution is available on installed instances and Dataiku Online.

Business Overview

Personalization is a huge opportunity for Retail and CPG businesses: 80% of companies report seeing an uplift since implementing personalization, which includes recommending relevant products to users. Several techniques can be used to build relevant recommendations: one of them is the Market Basket Analysis, used by retailers to increase sales by better understanding customer purchasing patterns. It relies on the analysis of large purchase history dataset to identify products that are likely to be purchased together.

One of the most famous examples of it is the well-known e-commerce giant which heavily uses “Frequently bought together” items on the product pages. It can also be leveraged by bricks-and-mortar stores: for example, a sports shop could choose to place running shoes next to swimsuits based on the analysis to increase sales. Overall, it is a great and powerful way to generate value through several use cases: optimizing product placement both online and offline, offering product bundles deals  etc. While driving additional sales for the retailer and enhancing the shopping experience for customers, Market Basket Analysis is a key asset to make the customers build brand loyalty toward the company.


  • Input transaction data to generate association rules across products
  • Define the solution settings through a user friendly Dataiku App: date filtering strategy, association rules computations, customer transactions dates filtering
  • Consume the output through interactive dashboards aiming at (i) analyzing the items frequency and (ii) identifying cross-sell and up-sell opportunities for your consumers
  • Rerun the entire data pipeline on new data using a simple application
  • Automate the pipeline to rebuild with new/fresh data
  • Learn purchasing patterns and identify new business opportunities
Solution available on Dataiku Online