Improving Customer Conversion

Objective

A leading apparel retailer wanted to improve personalized marketing to drive additional transactions. They had a high (80%) one-time purchase rate. It was costing them 5X more to acquire new customers compared to retain existing customers.

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Solution

Our approach to personalization started with building two Machine Learning models leveraging all of the retailers digital customer data:

1.Purchase Propensity:
Likelihood of a customer purchasing in the next two weeks

2.Category Affinity:
Product category the customer will most likely purchase next

Models generated predictive information used for personalization across channels

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Impact

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•Improvement of test over control groups in nearly every KPI measured

•Over the course of a year, the conversion lift represents an incremental 10% lift in sales in this channel alone, applied to all customers