Retail markets are becoming increasingly competitive. In a world where advertising is ubiquitous, the threat of substitution of traditional bricks and mortar retailers by online retailers is becoming more and more pronounced.

In this world, effective customer segmentation is paramount to the success of campaigns to drive customer engagement, increase average transaction value and foster customer loyalty. In a GDPR driven world, the cost of irrelevant communication to customers means the loss of that channel of communication.

This project aims to develop a set of customer segments to facilitate more effective conversion through better targeting with promotional material or offerings. “Market segmentation is to divide a market into smaller groups of buyers with distinct needs, characteristics, or behaviors who might require separate products or marketing mixes.” Goyat, Sulekha. (2011).

To develop this solution, the Instacart Historical sample shopping data (https://www.instacart.com/datasets/grocery-shopping-2017) will be used simulate typical retail data. This dataset was released initially as part of a Kaggle competition to develop robust association rules but in this case will be used to inform behaviour to identify customer segments.

The solution is developed following the CRISP-DM methodology, an iterative method of model development.