Project : Customer Churn (Customer Attrition)

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

Banks, 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 (along with cash flow, EBITDA, etc.) 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.

Companies usually make a distinction between voluntary churn and involuntary churn. Voluntary churn occurs due to a decision by the customer to switch to another company or service provider, involuntary churn occurs due to circumstances such as a customer's relocation to a long-term care facility, death, or the relocation to a distant location. In most applications, involuntary reasons for churn are excluded from the analytical models. Analysts tend to concentrate on voluntary churn, because it typically occurs due to factors of the company-customer relationship which companies control, such as how billing interactions are handled or how after-sales help is provided.

When companies are measuring their customer turnover, they typically make the distinction between gross attrition and net attrition. Gross attrition is the loss of existing customers and their associated recurring revenue for contracted goods or services during a particular period. Net attrition is gross attrition plus the addition or recruitment of similar customers at the original location. Financial institutions often track and measure attrition using a weighted calculation, called Monthly Recurring Revenue (or MRR). In the 2000s, there are also a number of business intelligence software programs that can mine databases of customer information and analyze the factors that are associated with customer attrition, such as dissatisfaction with service or technical support, billing disputes, or a disagreement over company policies. More sophisticated predictive analytics software 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.

You can find the data here: https://www.kaggle.com/blastchar/telco-customer-churn

Let's look at the project at the Kaggle site: https://www.kaggle.com/samansiadati/telco-customer-churn-nn