Detecting Early Signs of Customer Churn

Discover how a telecom company used Actnable AI to predict and reduce customer churn, improving retention and saving costs.

Business Problem

To reduce customer churn, a telecom company needs to predict which customers are at high risk of churn.

To detect early signs of potential churn, one must first develop a holistic view of the customers and their interactions across numerous channels, including store/branch visits, product purchase histories, customer service calls, Web-based transactions, and social media interactions.

As a result, by addressing churn, these businesses may not only preserve their market position but also grow and thrive. The more customers they have in their network, the lower the cost of initiation and the larger the profit. As a result, the company’s key focus for success is reducing client attrition and implementing an effective retention strategy.

Solution Approach

Used an openly available dataset of 7043 customers and 21 features about services that each customer has signed up for, Customer account information, and Demographic information about customers.

Carried out Exploratory Data Analysis (EDA)

Applied Machine Learning (ML) models – Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine to identify customers who are more likely to churn in the near future.

Analysis Impact

EDA helped to identify patterns in Churn customers.
• Around 75% of customers with month-to-month contracts opted to move out compared to customers with One-year / two-year contracts.
• Majority of customers choose the Fiber optic service. However, because of dissatisfaction with the internet service, customers who use Fiber optic have a high churn rate.
• Customers having DSL service have alower churn rate compared to Fiber optic service.
• Customers joined the service recently, i.e. New Customers are more likely to churn.
• Customers with higher Monthly Charges are also more likely to churn.
ML algorithms identified potential customers likely to churn/stop using the services.
It would be helpful to focus customer retention efforts only on these “high-risk” customers to avoid potential customer churn.

Conclusion

Customer churn is bad for a firm’s profitability. Various strategies can be implemented to eliminate customer churn. The best way to avoid customer churn is for a company to know its customers truly. This includes identifying customers at risk of churning and working to improve their satisfaction. Improving customer service is, of course, the top priority for tackling this issue. Building customer loyalty through relevant experiences and specialised service is another strategy to reduce customer churn. Some firms survey customers who have already churned to understand their reasons for leaving in order to adopt a proactive approach to avoiding future customer churn.

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