Machine learning for customer retention analytics uses past customer data to predict future customer behavior. This is done using big data. In today’s data-driven world, companies can track hundreds of data points about thousands of customers. Therefore, the input data for the customer retention model could be any combination of the following:
- Customer demographics
- Membership/loyalty rewards
- Transaction/purchase history
- Email/phone call history
- Any other relevant customer data
During the model training process, this data will be used to find correlations and patterns to create the final trained model to predict customer retention. Not only does this tell you the overall churn risk of your customer base, but it can determine churn risk down to the individual customer level. You could use this data to proactively market to those customers with higher churn risk or find ways to improve your product, customer service, messaging, etc. in order to lower your overall churn rate.