Developing predictive models for churn

Accurate identification of churn possibility can minimize customer defection if you first identify which customers are likely to cancel a subscription to an existing service, and offering a special offer or plan to those customers. When it comes to employee churn prediction and developing a predictive model, where the process is heavily data-driven, machine learning can be used to understand a customer's behavior. This is done by analyzing the following:

• Demographic data, such as age, marital status, and job status
• Sentiment analysis based on their social media data
• Behavior analysis using their browsing clickstream logs
• Calling-circle data and support call center statistics

An automated churn analytics pipeline can be developed by following three steps:

1. First, identify typical tasks to analyze the churn, which will depend on company...