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Machine Learning Use Cases for Retail

Recommendation Engines

Recommender systems are key tools to develop personalized relationships with customers. Retailers may have to develop several types of recommender models to answer different use cases.

Recommender systems rely on well documented algorithms and can combine several of them to provide the best level of interaction according to the context of interaction.


Personalization of the customer experience


Almost half of shoppers and 58 percent of millennials would agree to share data if in return retailers would offer personalized services and offerings (source: Deloitte Study).

This should encourage retailers to leverage the capacity of data-driven services to provide a fully personalized experience on-line and in-store.

Building an ML platform dedicated to the personalization of the customer experience is now within the reach of any retailer committed to competete in a data-driven economy.

Mix Management Optimization

Machine learning can be used to develop predictive models with the objective to reduce out-of-stock rates and to improve gross margins. Optimizing price, choice and availability for each store is now within the reach of data savvy businesses.

The optimization of product mix and replenishment operations with ML-driven services is a smart move for retailers wishing to improve their performances and make the best allocation of their resources.