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Using data prediction to identify when a certain customer is at high risk of churn allow clients to prevent attrition and keep existing clients. This helps immeasurably and keeps you with one in the hand rather than two in the bush.
Using data prediction to identify when a certain customer is at high risk of churn allows a company to do something to prevent the churn. This will prevent the loss of revenue from that customer. Data prediction is also cheaper to implement than the marketing costs required to bring a replacement customer on board.
Acquiring a new customer costs about 6 times more than retaining an existing one. Customers ‘churn’ when they cancel a subscription, close an account, don’t renew a service when it ends or simply, choose to take their business to another provider. Customer churn happens for many reasons and at different phases of the service or contract. If we can identify the reasons and the likelihood of churn at any point in time then we can implement effective customer retention strategies.
We start with the history of customers who cancelled contracts, but in order to build a better churn model, we pull data from as many sources as possible. Linking data from the service or complaints handling system is valuable, as is linking financial data to show the customer’s payment history. External data is very useful such as credit scoring and credit history. Finally, data from marketing campaigns is also very valuable. Knowing if a customer opens a contact email or otherwise interacts with us is important in studying customer churn.
It is often the case that many companies hold data relating to their customers in separate systems. This means it can take a lot of work to produce a single database that joins both internal data and external data together.
We build an exploratory model to predict who will leave and when they will leave. This kind of model correlates the observed churn in our history with the various input variables. It is important to measure the degree of influence that each input variable has in determining the outcome. This is what allows us to prioritize on the key indicators.
A churn analysis will be easier to implement and to understand in the context of lifetime value (“LTV”). If a company has estimates of LTV for different classes of customer then this can be used to prioritize the remedial action.
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