![]() ![]() Traditionally this analysis is based on customer data like age, gender and historic sales behavior. Therefore, customer churn analysis is one of the most popular use cases of predictive analytics. So why not try to prevent customers from seeking their luck elsewhere while they are still customers instead of trying to get them back after they have had (good) experiences with our competition? Wouldn’t it be great if we could identify customers who are at risk of leaving beforehand? Taking actions to prevent them from leaving instead of winning them back with a much bigger effort? Winning back customers who have lost faith in our services or products is a time consuming and costly task.
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