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case study

Using algorithms to predict customer churn for a food delivery platform

Profile

A food delivery platform in the UK.

 

Situation

No business likes to lose customers and experience customer churn. The food delivery platform asked Faculty to build a data model that would help it identify which customers were most likely to churn, so it could put in place measures, such as special offers, to try to stop them leaving.

 

Action

Separating loyal customers from customers likely to churn is a common “classification” problem, suitable for a wide range of machine learning (ML) models. In this example we needed to be aware of “imbalanced classes”. With an annual churn rate of about 6% there were more people in the “will not churn” class compared to the “will churn” class with a proportion of about 17:1.

We investigated several models to determine the customers likely to churn. We fed in data such as order frequency, the length of time a customer had been with the client, their average order value, and how recently they had placed an order. We chose a random forest model, which not only gave the best accuracy, but is also notable for computational reasons. A random forest is a combination of many decision trees which means it is highly parallelisable and so was quick to train on the company’s data, which is many terabytes in size.

 

Impact

The random forest algorithm was able to identify 92% of customers who went on to churn, a four-fold improvement on the 22% previously achieved by the food delivery platform. If the business could retain these identified customers, it could prevent £5.9 million of revenue being lost.

To find out more about what Faculty can do for you and your organisation, get in touch.

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