| Project Objective |
To develop a 'Churn Probability' scoring model and an accompanying DSS to enable the client to facilitate recovery of bills from most likely defaulters. |
Client |
| One of the largest telecom companies in India with annual revenues of $110 mn. The company has subscriber base of more than 2.6 million spread across 209 cities.
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Approach |
| Each month's data comprised of more than 150 variables and 65,500 records, wherein each record represents an individual customer. We used PROC LOGISTICS of SAS to identify variables that have no relevance to the probability of churning. Thereafter, various methodologies were considered for building the model.
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Solution |
| The model identified users with positive affinity values as more likely candidates for visiting a particular category of websites; as compared to zero or no affinity values. It takes into consideration the typical duration of browsing sessions, the time-varying propensity of users to visit a particular category of websites and also identifies typical web traversal sequences that allow preemptive ad targeting. A scalable system was designed for handling data for 1 million users on a daily basis (25 GB/day). The entire solution is implemented as a fully automated system using remote processing on the client end.
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Neural
Networks |
Decision
Tree |
Logit |
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Output is required as a Probability number -
A Continuous variable |
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Decision Tree
Method for Model
Generation |
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Output is always a
Discrete variable |
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Output is Continuous
variable |
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Output is always a Discrete variable |
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Better at predicting Binary
or Discrete variable |
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Better at
predicting Continuous
variable |
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Better at predicting
Discrete variable |
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Rationale for Methodology Selection
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Solution & Benefits |
| We built a DSS system to drive better decision-making. The DSS allows managers to control and analyze using a 'What if' simulator, the return on investments in churn management. The DSS also provides a comprehensive comparative assessment of customer profitability as measured by 'Average Revenue Per User' (ARPU) vis-à-vis customer loyalty indicated by the 'Tenure Base' of the customer on its network.
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Preemptive identification of customer churn there by guiding customer retention activities |
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Major cellular service provider with more than 2.7 million subscribers in India |
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Provided insight on identification and characterization of customer churn. |
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- Behavioral Online Ad Targeting
- Demographic Ad Targeting
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Predictive Analytics