Churn Analytics

Churn Analytics is used to identify the customers who are most likely to discontinue using the services / product. This is applicable horizontally across various industries like Telecom, Banking, Financial, Retail etc.

Why Churn Analytics is important:

As the competition among the industries is growing day by day, the cost of acquiring new customer is high when compared to retaining the existing customer. So it is advisable to highly competitive industries to concentrate on customer retention.

There are two types of churns, voluntary churn and involuntary churn. Voluntary churn is the customers’ decision to discontinue the services / product. Involuntary churn is due to the reasons like death or relocating to distant locations etc.

If companies’ concentrates on voluntary churn they can mitigate and address the customer issues before ahead of their decision to discontinue.

Features of Churn Analytics:

  1. Improved customer retention: eliminates the risk of churn by predicting it ahead
  2. Target Marketing: Identifies who are the relevant segments to prioritize in taking the marketing campaigns
  3. Churn Profiling: Provides information why customers are leaving
  4. Customer Satisfaction: Connect customer satisfaction information with all your other data to better understand customers and improve loyalty

Churn Determinants:

Churn Analytics

Big data Analytics and Predictive Algorithms to determine churn:

Big Data analytics provides the opportunity to process and correlate new data sources and types with traditional ones, to achieve better results more efficiently and receive insights that will set alarm bells ringing before any damage has been done, so giving companies the opportunity to take preventive measures.

Predictive algorithms like Logistic Regression, Decision Trees and Support Vector Machines are used to predict churn in advance.

K-means clustering algorithm, Hierarchical clustering algorithms are used to segment the customers based on their behaviours (usage, profitability, life time value, complaints etc.). These results are used for the targeted segments to carry appropriate marketing campaigns.

Collaborative filtering techniques like Apriori algorithms are used to recommend the suitable products to each customers which is helpful in promoting cross and upselling products / services.


  • Minimize acquisition costs and increase marketing efficiency
  • Keep customers engaged and loyal over time
  • Decrease the likelihood that competitors will lure existing customers
  • Activate and strengthen the existing customer base
  • Detect customer value loss and react sooner
  • See the value of individual customer loss and create targeted strategies
  • Allow teams to analyse quickly and easily, with no dependence on IT