Solving Customer Churn with a hammer!

Learning when data should take a back seat and give way to domain knowledge is a valuable skill.

Suppose you built a machine learning model on the data of your customers to predict churn risk. Now that you have a risk score for each customer, what do you do next?

Do you filter the top n% based on the risk and send them a coupon with a discount in the hopes that it will prevent churn?

But what if price is not the factor driving churn in many of these customers?

Customers might have been treated poorly by customer service, which drove them away from your company's product.

Or there might have been an indirect competitor's product or service that removes the need for your company's product altogether (this happened to companies like Blockbuster and Kodak in the past!)

There could be a myriad of factors, but you get the point!

Dashboards and models cannot guide any company's strategic actions directly. If companies try to use them without additional context, more often than not, they are just throwing away the money.

I read a comment on a Reddit thread about customer churn that said, "When someone recommends a hammer, make sure all your problems are nails."

Domain knowledge is what you need to make sure that all your problems are nails.

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