Customer attrition, also known as customer churn, customer turnover, or customer defection, is the loss of clients or customers.

Telephone service companies, Internet service providers, pay TV companies, insurance firms, and alarm monitoring services, often use customer attrition analysis and customer attrition rates as one of their key business metrics because the cost of retaining an existing customer is far less than acquiring a new one. Companies from these sectors often have customer service branches which attempt to win back defecting clients, because recovered long-term customers can be worth much more to a company than newly recruited clients.

Predictive analytics use churn prediction models that predict customer churn by assessing their propensity of risk to churn. Since these models generate a small prioritized list of potential defectors, they are effective at focusing customer retention marketing programs on the subset of the customer base who are most vulnerable to churn.

In this project I aim to perform customer survival analysis and build a model which can predict customer churn.

Some of the insights ideas I consider:

Using customer data to identify and target customers who are at a high and medium risk of churning to counter this effect with relevant customer service initiatives.

Analyzing the effects of promotional campaigns and loyalty programs on customer retention rates and overall revenue.

Machine learning models that predict future chances of customer churn which can be used by businesses to improve strategies for better retention & profitability.