Customer attrition, also known as customer churn, customer turnover, or customer defection, is the loss of clients or customers.
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Banks, telephone and internet service providers, pay TV companies, insurance firms, and companies providing some sort of digital services often use customer attrition analysis and customer attrition rates as one of their key business metrics (along with cash flow and other financial indicators), 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.
Research on customer attrition data modeling may provide businesses with several tools for enhancing customer retention. Using data mining and software, one may apply statistical methods to develop nonlinear attrition causation models.
Even though the behaviour of customers differ between industries, the underlying pattern remains same. So, the knowledge gained from analysis of churned customers of one sector can be applied on other industries, at least to get started. In today's highly competitive business landscape, the ability to correctly predict which customer is going to churn will give a huge of over competitors.
In this repository, I have made an attempt to analyze customer churns across multiple industries, and build mining models to not only predict the next possible churns, but derive insights into which factors are playing a major role in this.