This document discusses the development of a predictive churn model for Segment 9 subscribers in REC base. It defines churn, outlines the modeling process including creating an aggregated billing table (ABT) from multiple data sources, selecting important variables, and iterating the model 30-40 times. Key variables that impact churn are identified, such as average recharge amount and number of days since last recharge. The final section notes a model performance slide was included but its contents are not described.

1. Predictive Churn ModelPredictive Churn Model
Segment 9Segment 9
20th
Nov ‘ 2014

2. Please Observe Safety procedures andPlease Observe Safety procedures and
take time to note location of nearesttake time to note location of nearest
Fire ExitsFire Exits

3. Slide: 3
Content
Definition, Objective and Scope
Modeling Process
 ABT Creation
 Variable Selection
 Model Iterations
Final Model – Select Variables
Model Performance
Business Analytics – Corporate Marketing | Confidential

4. Churn Definition, Objective & Scope
 Definition – A subscriber who moves from REC base to Non-REC base in a period of
one month (Performance period)
 Objective – To predict probability of moving from REC base to Non-REC base over
the next 1 month for each of the subscriber
 Scope –
REC base
Segment 9: “FEATURE PHONE + VOICE+DATA(1 Mb+) + Single S ”
AON >90 days
Slide: 4
# of Subscribers
Total Population
6,77,367
# of Churners 48,09
Churn Rate 1.%
Start Date End Date
M2 30-JULY-14 30-AUG-14
M1 31-AUG-14 30-SEP-14
Performance Period 01-OCT-14 30-OCT-14
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5. Modeling Process (1/4)
 Multiple CMDM tables (IN Dump, Leg-wise, Usage, Recharge etc.) are referred
and daily level data is extracted for the defined time period.
 ABT is created at Subscriber level from the above extracted data
 ~300 variables are created
Slide: 5
ABT Creation Variable SelectionModel Iteration
RATIO/PERCENTAGE
TOTAL
MIN, MAX
COUNT
RANK / PERCENTILE
TEMPORAL FIELDS
BINNING
MEAN, MEDIAN, MODE
ABT VariablesRaw Variables
MOU
REVENUE
SMS
VAS
RECHARGE
DECREMENT
LEG-WISE USAGES
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6. Modeling Process (2/4)
 The variables are screened through multiple techniques (Correlation, GINI, Variable
Clustering, Chi-sq. etc.) to arrive at more significant and select list of variables
Slide: 6
ABT Creation Variable SelectionModel Iteration
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7. Modeling Process (3/4)
Slide: 7
 30 to 40 iterations are performed , with key iteration mentioned above
 Through selection and rejection of variables, a manageable no of variables and
desired lift is achieved through these iteration.
 Reds mark the variables dropped in subsequent iterations .
 Highlighted the red oval shows the number of variables used in a particular iteration.
Business Analytics – Corporate Marketing | Confidential
ABT Creation Variable SelectionModel Iteration

8. Modeling Process (4/4)
 At each stage of iteration variables are removed / added basis statistical significance of
variable, multicollinearity, VIF and biz importance.
Slide: 8
ABT Creation Variable SelectionModel Iteration
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9. Featured Variables and Impact on Churn
Slide: 9
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 In order of impact on churn
Variables Description Impact on Churn
TOT_PRR_D123_W1 Avg Recharge Amount in Month 1 Inversely Proportionate
TOT_REC_CNT_M1 No of days Since last Recharge Inversely Proportionate
TOT_PRR_W2 Ration of PRR for Last 3 days and week 1 Inversely Proportionate
Days_Since_Last_Rech Total PRR incured in week 2 Directly Proportionate
AVG_REC_AMT_M1 Recharge count in Month 1 Inversely Proportionate

10. Model Performance
Slide: 10
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11. Thank you
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For any query or concerns please contact: Ankur Shrivastava – ankur.shrivastava@tatatel.co.in or call +91-8655007666

12. List of Abbreviations frequently used
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Chi-square :A statistical test used for comparison of goodness of fit. In other words, the difference between observed and expected outcome
Clustering :A group of elements shows similar characteristics put together giving a certain statistical inference
Co-relation :A mutual linear relationship between any two elements without infer to causal impact.
GINI Ordering/Index A statistical measurement of dispersion or inequality of population
GVC : Good value customer segment
HVC : High value customer segment
LVC : Low value customer segment
Multicolinearity/VIF : A statistical event to measure the multiple relationship of predictor/independent variables and target variable
PCM: Predictive Churn model
Segment -1: SmartPhone - V+D (300MB+)-S
Segment -10: Data Phone - V+D (1MB+)-M
Segment -11: Data Phone - V/D only-S
Segment -12: Data Phone - V/D only-M
Segment -13: Basic - V/D only-S
Segment -14: Basic - V/D only-M
Segment -2: SmartPhone - V+D (300MB+)-M
Segment -3: SmartPhone - V+D (1MB+)-S
Segment -4: SmartPhone - V+D (1MB+)-M
Segment -5: SmartPhone - V/D only-S
Segment -6: SmartPhone - V/D only-M
Segment -7: Data Phone - V+D (300MB+)-S
Segment -8: Data Phone - V+D (300MB+)-M
Segment -9: Data Phone - V+D (1MB+)-S
uHVC – Ultra high value customer segment
uLVC – ultra low value customer segment