This post is a continuation of an earlier post on Customer Retention Analytics by our guest bloggers Sandhya Kuruganti and Hindol Basu, authors of a book on Business Analytics titled “Business Analytics: Applications to Consumer Marketing”. The first part of the post Guide to Customer Retention Analytics- Part 1 focused on understanding retention, attrition and the various retention strategies used by marketers. Part two will explain retention analytics, along with a Telecom Case Study that showcases the Proactive Approach to Retention Management using Churn Model.
Telecom Case Study: Proactive Approach toRetention Management using Churn Model
The telecom industry has become very competitive with price cutting and service enhancement.
Subsequently annual churn rate have doubled from 18% to 36%. This implies that average tenure of a telecom subscriber has halved and the telecom subscriber is losing more than 1/3rd of its subscriber base between the beginning and end of the financial year.
The telecom subscriber is aggressively looking to arrest churn proactively
Predictive analytics is required to enable proactive retention efforts to reduce telecom churn. A churn model outputs a score which measures the likelihood to churn in the next one month
The churn model will prioritize subscribersin order of their propensity to churn that would be used for focussed targeting of “high risk of churn” subscribers
Exploratory business analysis and segmentation is done prior to deciding on the sample for model development
Pre Paid and Post Paid: Pre Paid has significantly higher churn rates than Post Paid. However, ARPU for Post Paid is 5x that of Pre Paid. ARPU is the average revenue per user.
Pre Paid Segment: This segment comprises 70% of the portfolio and contributes to 80% of total churners. Annual churn rate is 41%.
Post Paid Segment: This segment comprises 30% of portfolio and contributes to 20% of total churners. Annual churn rate is 24%.
Separate models may need to be developed for Pre Paid and Post Paid separately as these segments behave differently (churn, revenue and usage). Performance of a single model may be sub-optimal.
The model is first developed for the Post Paid segment as the telecom subscriber’s priority is to curtain potential value loss followed by potential subscriber loss.
Subsequently, the Pre Paid segment model would be developed.
Data Preparation for Churn Model (Post Paid Segment)
Telecom Churn Model:The following table shows the potential predictors of Churn and sign of relationship. Logistic Regression is a popular statistical method that is used.
Implementation:Based on the churn mode l, a cut-off for the score can be decided. Subscribers exceeding the cut-off should be considered for contact. A suggested strategy could be:
Segment 1 has a churn ratewhich is about 2x the currentsample churn rate
Campaign budget permits contacting 40,000 subscribersin a month for a proactive anti-attrition strategy
Significant marketing saves, to the tune of 50%. The telecom service provider would have had to contact 80% of the subscribers to reach out to 80% of the total potential churners. Now they have to contact only 30% using the churn model.
Sandhya Kuruganti and Hindol Basu are authors of the book “Business Analytics: Applications to Consumer Marketing”, recently published by McGraw Hill and available on Flipkart and Amazon India/UK/Canada. Jigsaw students can avail of a discount of 20% with a coupon code at McGraw Hill website(valid until Nov 30, 2015).. The authors are seasoned analytics professionals with a collective industry experience of more than 30 years.