Churn Rate can be defined as the probability of a customer to stop using a product or a service. To prevent customer loss resulting from customer dissatisfaction, companies tend to analyze this rate. Churn Analysis is one of the most popular analytical services that we offer to our clients. In this article, the main benefits of Churn Analysis are summarized and the process is explained.  


What kind of benefits does Churn Analysis offer?


An effective Churn Analysis helps understanding customer journeys better. Taking the risk of customers leaving into consideration, companies may develop a series of CRM activities to fulfill their needs better and to create more comfortable customer experience. 


Churn Analysis enables the companies to make more accurate estimates, quantifying the risk of losing a specific customer. The analysis can also be conducted on the basis of customer segment and amount of loss. Looking at the results of these analysis, you may improve customer communications and engage in activities improving loyalty.
Customer retention is a critical business goal in today's competitive environment. Consulta helps you estimate which customers will be lost in the future. Churn Analysis helps developing direct marketing efforts for these customers and enables you to make offers to establish re-interaction with these customers.


Some of the areas that use Churn Estimates widely are
Music and video streaming services - YouTube, Netflix, Spotify
Media - Bloomberg, The Guardian
Telecommunication - Vodafone, T-Mobile
Software as a Service (SaaS) - Adobe Creative Cloud, Microsoft,


How do we start working with churn rate estimate? Which data do we use? What are the implementation steps?
Churn estimate model is built using machine learning methodologies. As in any other machine learning project, analytics experts also initially need data to provide any service. Depending on the goal, our experts identify the data required to be collected. Then, these data is compiled, processed and converted to a proper format for creating machine learning models. Finding the correct methods, fine-tuning the models and selecting the best model are other important steps. After selecting a model with estimates in highest accuracy, the model can be put into use.



A typical machine learning based churn analysis Project of Consulta team follows the activities as below:
Understanding the problem and the final goal
Collecting data
Preparing and pre-processing data
Modeling and testing
Distributing and monitoring the model
Suggesting actions


Excellent customer experience is expected to be the top brand differentiator as we are in an era which to not be a loyal customer is a new normal. . Churn Analysis enables businesses to continuously improve their customer experience and general brand image. Do you want to be one of these companies? Contact Consulta to gain competitive advantage and offer better customer experience.

Our solution partners

 The European House - Ambrosetti
 Software AG
 Centric Software
 Bilge Adam