Solving Customer Churn with Machine Learning
"It’s been so successful that there is now a program built around the output of these machine learning algorithms. It’s also given us a lot of ideas on where to use machine learning, so the inventory of projects we have going into next year has grown because people have seen the impact we made on consumer churn and how successful that program has been."Julian Bharadwaj Paypal’s Senior Data Scientist
- Customer Churn
- Sentiment Analysis
Overview of the Challenge
For Paypal, consumer churn can have a big impact on its bottom line. Previously, the company looked at the problem in specific increments of time, noting that a customer who hadn’t used its platform in that time period must have churned. Paypal would run a report that showed the churn date for all the customers that fell into this category as the date the report was run. The report also showed which features were the last ones to be used for all customers who churned during that time period. While this information was useful, it wasn’t fully accurate, and, as such, the timeliness and effectiveness of Paypal’s marketing efforts to win-back customers was less than ideal.