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Churn in a Prepaid Cellular Market Marcelle Georgiev - MTN (S.A.) Mohan Namboodiri - SAS Institute (S.A.) South African Situation ! ! ! South Africa has a fast growing cellular market Prepaid is growing the fastest Two main cellular providers - third provider on the way Retain Those Customers ! ! ! ! ! Retention vs. Acquisition Highest value customers in particular Contracts vs. Prepaid Prepaid - volatile and impersonal a big challenge A Forward Looking Business Unit ! ! ! ! ! Targeted retention strategy Segment specific retention managers Loyalty programmes Direct marketing Drive for integration of all the above Classic Churn ! ! ! What is it? The view from the contract (post pay) side The month when the contract expires… Information rich study Why a Study on Prepaid? ! ! ! ! The prepaid segment has higher churn than the contract segment. Are there distinguishing call behaviour patterns that predict churn Is it churn or is it dormancy? A functional definition of churn is needed Predictive Data Mining We would like to identify factors influencing… ! ! Early inactivity - within four months of activation Late inactivity - by the end of year one Multiple Card Loading ! ! Service Time Cards - 30 days access to network with which one can receive calls and make non-chargeable calls Air time Cards - three denominations for making chargeable calls Study Population ! ! 90% of customers enrolled in November ‘98, Promotional and Non-Promotional cases Behaviour for the period December ‘98 February ‘99 was examined against two different outcomes: ! ! Missing value band for March ‘99 Missing value bands for the period September - November ‘99 Time Windows Approach ... December January February call data November ‘99 churners Illustration of the time windows approach Some Descriptive Results ! ! ! Relative stability of highest value segments Highest value segment has lower churn Promotional vs. Non-Promotional behaviour Classification Accuracy Year One Model Overall Prediction Accuracy: 80% Correct Classification of Inactives: 64% Lift: Substantial Improvement From Chance Alone Model Chance What Does It Mean? ! ! I could take a new data set of 18,000 individuals and ask for the 5,000 most likely to go inactive Of the top 4,989 predicted to become inactive, 4,113 did. Scoring New Data Actual A D Predicted D 17.56% 876 82.44% 4113 Of roughly 5,000 scored cases, 82% turned out to be cases that had become inactive... Model Results for Predicting Early Inactivity ! ! ! ! Inactive in February ‘99 Older handsets related to inactivity Customers are at risk for inactivity,if they show low outgoing peak week activity Even greater risk if they have higher incoming weekend activity Classification Accuracy Month Four Model Overall Prediction Accuracy: 88% Correct Classification of Inactives: 73% Scoring New Data Actual Predicted D A 10.11% 368 D 89.89% 3273 Of roughly 4,000 scored cases, 90% turned out to be cases that had become inactive ... Spin-offs (for Free) ! ! ! ! Stability of highest value segment ‘Intervene with a bullet’ A lot for a little Insight into success of promotions a possible fallacy Steps Forward ... ! ! ! ! Putting the scores into operation for campaigns and reporting Targetting the vulnerable customers proactively Measuring the effectiveness of the whole process Revisit the model Enterprise Miner™ vs. SAS/STAT® ! ! ! ! Speed of turn-around Ease of use in trying new ideas Point and click / drag and drop Integrated presentation capability helps Data Volumes and Special Thanks ! ! ! Base table - 200 Gigabytes Usage component - 32 Gigabytes Final table - 800 Megabytes Data Volumes and Special Thanks ! ! ! Base table - 200 Gigabytes Usage component - 32 Gigabytes Final table - 800 Megabytes Special Thanks -- Allister Viljoen
