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Churn model for telecom

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This is one of the practice across telecom to manage the churn.

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Churn model for telecom

  1. 1. Predictive Churn ModelPredictive Churn Model Segment 9Segment 9 20th Nov ‘ 2014
  2. 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. 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. 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 Business Analytics – Corporate Marketing | Confidential
  5. 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 Business Analytics – Corporate Marketing | Confidential
  6. 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 Business Analytics – Corporate Marketing | Confidential
  7. 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. 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 Business Analytics – Corporate Marketing | Confidential
  9. 9. Featured Variables and Impact on Churn Slide: 9 Business Analytics – Corporate Marketing | Confidential  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. 10. Model Performance Slide: 10 Business Analytics – Corporate Marketing | Confidential
  11. 11. Thank you Business Analytics – Corporate Marketing |Business Analytics – Corporate Marketing | ConfidentialConfidential For any query or concerns please contact: Ankur Shrivastava – ankur.shrivastava@tatatel.co.in or call +91-8655007666
  12. 12. List of Abbreviations frequently used Business Analytics – Corporate Marketing | Confidential 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

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