Customer Churn Prediction How to Use Churn Prediction and Analysis to Increa | Page 4

PUTTING THE CUSTOMER
CHURN PREDICTIONS IN USE
At this stage, the main goal is to give the company a targeted set of actions to
improve customer retention. Once an accurate machine learning model has
been trained, all of the past and present customers will be run through the
predictive model. This could give three different results. Table below shows
sample data set with prediction results and actions a company should take on
each particular customer:
If the prediction matches the actual status of the customer, then no action is
necessary. However, if an active customer is likely to be a churned customer,
this will imply defining the customer as being at-risk of churning. Alternatively,
if a customer is currently inactive (meaning that they previously churned) but
the prediction is that they will become an active customer, this indicates that
they are a good target to attempt to reinstate business.
It is also useful to build a Customer Churn dashboard that enhances reporting
by providing account managers with a list of clients to target with retention
efforts, as well as identifying the best prospects to try and win back. These
crucial insights maximize customer preservation and accurately pinpoint
marketing efforts.
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