The first challenge in churn prediction is defining what the business means by churn.
- Cancel a subscription
- Last purchase more than a month or quarter ago
- Last used the product a month or quarter ago
- In the next month, quarter or year.
The next challenge is joining and transforming the data contained in multiple different tables to create the training dataset. This includes data that defines if a customer has churned from the CRM, data that describes how a customer interacts with the product from the product usage and web analytics DB, and enrichment data such as demographics for third party customer behavior and economics data for macro socio-economic influences. All this data needs to be cleaned, joined and transformed into valuable ML features before going into model training. This pre-modeling prep process can be frustrating and time consuming. We are here to help.