Cell2Cell The churn game

Contents Business Understanding: Introduction ......................................................................................................... 3 Business Objective ........................................................................................................................................ 3 Data Mining Objective .................................................................................................................................. 3 Data Set ......................................................................................................................................................... 3 Data Preparation ........................................................................................................................................... 3 Data Modeling............................................................................................................................................... 4 1.

Decision Tree (Binary) ....................................................................................................................... 5

2.

Decision Tree (Three-way tree) ........................................................................................................ 6

3.

Logistic Regression ............................................................................................................................ 6

4.

Logistic Regression with Transform Variables .................................................................................. 7

5.

Neural Networks ............................................................................................................................... 7

6.

Neural Networks after transform variables and variable selection .................................................. 8

Evaluation ..................................................................................................................................................... 9 Profitability of a Proactive Retention Plan.................................................................................................. 10 The key variables predicting churn: ............................................................................................................ 11 Possible Incentives Offered ........................................................................................................................ 11 Test Measures ............................................................................................................................................. 12 Profitability Matrix .................................................................................................................................. 12 Net Additions minus Existing Customers ................................................................................................ 12

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List of Figures Figure 1: Process flow Diagram..................................................................................................................... 4 Figure 2: 2-way decision tree that resulted from cell2cell data set ............................................................. 5 Figure 3: Variables in descending order of their importance helping in splits for 2-way Tree .................... 5 Figure 4: Result summary of 2-way decision tree......................................................................................... 5 Figure 5: Variables in descending order of their importance helping in splits for 3-way Tree .................... 6 Figure 6: Result summary of 3-way decision tree......................................................................................... 6 Figure 7: Result summary of Regression without transformation variables ................................................ 7 Figure 8: Result summary of Regression with transformation variables ...................................................... 7 Figure 9: Result summary of Neural Network without Variable transformation and selection ................... 8 Figure 10: Result summary of Network with Variable transformation and selection .................................. 8 Figure 11: Comparison of cumulative lift value for different techniques..................................................... 9

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Cell2Cell: The Churn Game Business Understanding: Introduction Cell2Cell is the 6th largest wireless company in the US, giving service to nearly 10 million subscribers, serving more than 210 metropolitan markets & 2900 cities (covering nearly all 50 states). The company is currently facing a major problem of customer churn. We are using SAS EM 4.3 to develop a model for predicting customer churn at Cell2Cell.

Business Objective   

Reduce churn for the company Improve profitability Identifying incentives offered to the customers with high risk of churning

Data Mining Objective  

To develop an accurate predictive churn model (Lift value of at least 1.75) To identify the factors that are important in driving subscribers churning

Data Set The given data set consists of 71,047 rows & containing a total of 78 variables (including a variable named “CHURN”, signifying whether the customer had left the company two months after observation). One of the variables named “CALIBRAT” was used to differentiate the validation dataset from training dataset. Training dataset contained data of 40,000 customers and validation dataset contained 31,047 customers.

Data Preparation The dataset was divided in training and validation datasets, using “CALIBRAT” as the partition variable (value of 1 was used training and value of 0 was used for validation). Variable “CHURN” was set as target variable and some other variables (those not related to business objective) were rejected.

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Variable No.

Original Variable

Changed Variable

22 26 30

Churn CSA Customer

Target Rejected Rejected

77 Calibrat 78 Churndep Table1. Showing variables that were rejected

Rejected Rejected

Data Modeling Total of 6 different models were used to predict the churn of customers. These models were:      

Decision Tree (binary) Decision Tree (three way tree) Logistic Regression Logistic Regression with Transform Variables Neural Networks Neural Networks after transform variables and variable selection

SAS EM 4.3 was used to run these 6 models. Snapshot of the model is shown below.

Figure 1: Process flow Diagram

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1. Decision Tree (Binary) For both 2-way and 3-way tree gini-reduction method was used. The assessment criteria was set to be “Proportion of event in top 10%”.

Figure 2: 2-way decision tree that resulted from cell2cell data set

Figure 3: Variables in descending order of their importance helping in splits for 2-way Tree

As can be seen from above figure, EQPDAYS – Number of days of the current equipment (split at