Customer acquisition is expensive. Understanding which customers are likely to churn and why allows product managers to identify potential pain points and friction within the digital experience that may be contributing to customer churn and for customer success to work to retain those customers most at risk of churn.
Based on a customers product and website usage, along with data about the customer, predict which customers are likely to churn and identify the key factors that are driving this churn.
Will a customer churn in a given time period?
The first challenge in churn prediction is defining what the business means by churn.
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.
Allow more than the two choices across multiple dimensions. Models the target as a function of the independent variables.
Build machine learning models to predict the target as a function of the independent variables. Use model interpretability packages to evaluate the impact of the independent variables on the prediction.