Wikipedia states that the churn rate (also called attrition rate) measures the number of individuals or items moving out of a collective group over a specific period. It applies in many contexts, but the mainstream understanding of churn rate is related to the business case of customers that stop buying from you.
- Telecommunication (cable or wireless network segment),
- Software as a service provider (SaaS),
- Retail market,
- Subscription-based businesses (media, music and video streaming services, etc.), 5.Financial institutions (banking, insurance companies, Mortgage Companies, etc.),
- Marketing,
- Human Resource Management (Employee turnover).
The overall scope to build an ML-powered application to forecast customer churn is generic to standardized ML project structure that includes the following steps:
- Defining problem and goal: It’s essential to understand what insights you need to get from the analysis and prediction. Understand the problem and collect requirements, stakeholder pain points, and expectations.
- Establishing data source: Next, specify data sources that will be necessary for the modeling stage. Some popular sources of churn data are CRM systems, analytics services, and customer feedback.
- Data preparation, exploration, and preprocessing: Raw historical data for solving the problem and building predictive models needs to be transformed into a format suitable for machine learning algorithms. This step can also improve overall results by increasing the quality of data.
- Modeling and testing: This covers the development and performance validation of customers churn prediction models with various machine learning algorithms.
- Deployment and monitoring: This is the last stage in applying machine learning for churn rate prediction. Here, the most suitable model is sent into production. It can be either integrated into existing software, or become the core of a newly built application.
