Customer churn, also known as customer attrition, occurs when customers stop doing business with a company or stop using a company’s services. By being aware of and monitoring churn rate, companies are equipped to determine their customer retention success rates and identify strategies for improvement. We can use a machine learning model to understand the precise customer behaviors and attributes which signal the risk and timing of customer churn.
Churn prediction is difficult. Before you can do anything to prevent customers leaving, you need to know everything from who’s going to leave and when, to how much it will impact your bottom line.
Before we go to create machine learning model we should clearly understand meaning of churn-rate for our research problem. Follow the next questions we can explore churn comprehensively.
Analysis Framework
The following questions will serve as a framework to analyze the data set and to provide business insights.
Churn-based Analysis
- Who is likely to churn?
- What behavior and characteristics can we learn from the churned customers?
- What features differentiates the churned customers from the current customers?
- Who is the most valuable customers?
- Which features do they likely to have?
- Reduce the churn-prone customers?
- Maintain and maximize the most valuable customers?
Will they, won’t they The way many data analysts try to model this problem is by thinking in black-and-white terms: churn vs no-churn. It’s really easy to view the problem in this way as it’s a pattern we all know — supervised classification. But doing so leaves out a lot of the nuance of the churn prediction problem — the risk, the timelines, the cost of a customer leaving.