Stop customer churn with data science

First things first

Firstly, you need to get insights into your existing customers:

• What are the characteristics of your loyal customers, and maybe more important;
• What are the characteristics of the customers that will probably leave your organization?

Lots of companies are focusing on getting new customers instead of keeping their existing customer base. For example, why are gyms and telecom providers giving a discount to their new customers, and not to their current customers? Due to these strategies, a lot of customers are hopping from company to company, which will not lead to a loyal customer base.

Recognizing churners’ behavior

This one-pager will go into the practical way of creating insights into the customers who have the highest probability of leaving your organization. These customers are going to churn, so from now on, they will be called churners.

Getting a list of your future churners is incredibly valuable to your business. You could give them a little bit extra TLC (tender, love & care) or a discount, depending on which marketing strategy is the best fit for each churner, to make sure that these churners will not leave.

You will need to do some study so you can determine which characteristics are most important for customers to become a churner. This one-pager will keep it clean and simple, to give you the information you need to start your analytical churn journey right away!

To define which characteristics of customers are good predictors for customer churn, you have to take a look at your historical data. This dataset must contain data from customers that have left your company in the past, and from customers that remained a customer. In this dataset, you will try to discover (hidden) patterns in the customers’ behavior that separate the churners from the not-churners. For example, you might find out that almost all the churners filed a complaint in the last six months of being a customer. This might indicate that the follow-up of the claims is not sufficient. Improving the follow-up could lead to fewer churners.

Stop Churn with Machine learning

Once your dataset is clean and complete, the real science of data begins. Machine learning models will help you find the (hidden) patterns within your dataset that separate the churners from not-churners. Once a model can discover these patterns in your historical dataset, it can predict whether your existing customers are going to churn or not. If existing customers have the same characteristics/behavior as the clients that did churn in the past, you could start alerting the marketing department and account managers to take action!

Conclusion

Preventing churn can be valuable for your organization. Knowing which customers are likely to churn not only brings in extra money, it can change your business model. If you reward your loyal customers regularly, you do not need to spend a fortune on finding new customers.

Curious how we could help you prevent your customers from churning? Or are you curious how data science could help your organization with other business problems? View some more information on a free data science discovery session.

Webinar Predict Churn with Artificial Intelligence:

Do you want to know how predictive models can help your company make better decisions? Watch our recording webinar “Predict churn with artificial intelligence“!