The Most Overlooked Factors

There is no magical one ultimate answer to this, even more often than not these are quite overlooked factors. Rather this can be explained as the by-standard product of the accumulated effects over a period of time. Here is what's wrong from a data scientist's view;

  • Opinion oriented presumtions
  • Ignoring the facts
  • Targeting the wrong audience for marketing campaigns
  • Not knowing what the customers actually need

Double Trouble

The first two points are very closely tucked together. In the corporate world, there are so many opinion-oriented presumptions present, that's very easy to overlook the facts. One; it is hard to dig out the facts from the data. Two; "well he/she is experienced so they know the best". I think you get me.

Just because a person is very experienced does not mean you must overlook the facts that are present in the data. Let's say the data holds the information that 25–30 years old customers are the most churn customers. You would know this if you did go through the data. However, you might be convinced by the seniors that a marketing campaign for customers above 30 years is the best choice. In such a case the marketing campaign will not be effective despite the top-notch quality of the campaign.

That brings me to the third point, you are simply targeting the wrong audience for your marketing campaigns for churn reduction. More often than not this is the most crucial error for failing to retain churn customers. The consequences for this are;

  • The marketing campaigns are wasted
  • The marketing campaign money is wasted
  • The real churn customers are unreached

Ultimately you fail to retain the churn customers.

The Last Straw

The last reason mentioned above is harder to comprehend. This is due to the fact that we don't always know what the customers need, fail to address their behavior leading to a churning case.

For instance, say a customer became churn because the customer is very unhappy with the shipping cost and time. Even if you are to reach this customer with a 50% discount coupon the customer might not respond to that. The customer knows the shipping will cost a lot and probably the product will reach them very late. However, if you are to offer that customer faster free premium shipping, the customer is more likely to respond and give another shot. More importantly, the customer will feel important in the sense that you reached him/her with what they actually wanted.

Therefore, reaching the right customer with the right approach and for the right reason is what everyone always overlooks.


Everyone has a different opinion on how to reduce churn. But opinion can take you only so far. So how do you know all these factors? The straightforward answer is your data. The most effective way is to let your data speak and spill out the ways to reduce churn cases. All companies have their CRM data which holds a tremendous amount of insights. You can extract the behavior of the customers, more importantly, the behaviors that lead to a churn case. You can extract the customers' needs, why they are unsatisfactory with your brand, and more insights.

We have all gotten accustomed to the idea of decision-making using data in today's world. So let's apply the same thing here as well.

The most effective way to reduce churn is to prevent it before it happens. Suppose you know a customer/user will churn next month and know the possible reasons why that customer is about to churn. In that case, you can reach out to that customer and offer some coupons or engage him/her to retain a customer and prevent a possible churn. In other words, Churn Prediction .

How to dig in those data and extract such insights? That can be done using many software packages out there. Here is an article to help you out in that regard: Reducing Customer Churn in Data Science Fashion .

I will present a more detailed way using Software called Enhencer in the next blog

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