The traditional and most simplistic definition of customer churn is the number of customers you’ve lost in a defined period.
Your customer churn rate therefore, is calculated by dividing the number of customers you’ve lost by the number of customers you started with at the beginning of the period being measured. For example, you may start the month with 100 customers. At the end of the month your customer count is 94. You have churned 6 customers or have a churn rate of 6%.
Whilst the definition seems simple enough, according to a recent report from McKinsey and Co. many organizations are struggling to get complete commitment to reducing customer churn as a core strategy.
The use of analytics tools using the latest machine learning technologies is beginning to help businesses predict the various stages of customer success. In fact, depending on your requirements and availability of the appropriate data, customer churn software can actually allow you to define churn based on variables meaningful to your specific business.
For example, you may want to predict when a customer chooses not to proceed with a specific proposal for your services, when they choose to upgrade their subscription or when they’re likely to drop a specific product in your range.
The increasing sophistication of these tools doesn’t have to mean complexity or barriers to adoption. Look for nimble, agile platforms that are easy to use, can process high volumes of transactional data in real time and that deliver actionable insights in minutes.
How you define customer churn is your business. Leveraging technology to predict customer success outcomes makes your business smarter and provides your teams with timely insights for better decision making and business growth.
If you are curious to learn more about churn management software request a demo with us!