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Understanding Customer Churn in Telecom - Corporate Presentation

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Understanding Customer Churn in Telecom - Corporate Presentation

This study attempted to formulate a regression model that identifies the characteristics that influence whether a customer is probable to switch telecommunications providers (Churn). We started with a full model, performed variable selection using AIC and BIC through the stepwise method and moved on to other models, using LASSO, or a few simple (aggregate) transformation of certain predictor variables. We concluded that the best logistic regression model we could find was produced with an aggregate transformation of the variables that concern domestic charges for various times of the day (Day Charges, Evening Charges, Night Charges).

This study attempted to formulate a regression model that identifies the characteristics that influence whether a customer is probable to switch telecommunications providers (Churn). We started with a full model, performed variable selection using AIC and BIC through the stepwise method and moved on to other models, using LASSO, or a few simple (aggregate) transformation of certain predictor variables. We concluded that the best logistic regression model we could find was produced with an aggregate transformation of the variables that concern domestic charges for various times of the day (Day Charges, Evening Charges, Night Charges).

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Understanding Customer Churn in Telecom - Corporate Presentation

  1. 1. SOTIRIS BARATSAS sotbaratsas@gmail.com UNDERSTANDING CUSTOMER CHURN U S I N G L O G I S T I C R E G R E S S I O N
  2. 2. The Problem Over the previous period ~15% Churn Rate *based on a sample of 3333 customers Which customers are probable to churn? Our preliminary analysis shows, that: § Gender and Area Code don’t seem to play a role § There is some variability between States, but didn’t know if it’s important § Having an International Plan and a Voicemail Plan seem to be important factors
  3. 3. Deeper Analysis using Logistic Regression We attempted to formulate a model with a good fit, that pinpoints the characteristics of customers probable to churn. Model_ID Description McFaddens R^2 Cox and Snell R^2 Nagelkerke R^2 Hosmer Lemeshow p-value Model15 Unifying the charges under one variable “Domestic.Charge” > Stepwise with AIC (multicollinearity fixed) 0.257 0.185 0.338 0.247 Model2 Starting with all the variables, and performing Stepwise Selection with AIC (multicollinearity fixed) 0.258 0.186 0.338 0.117 Model3 Starting with all the variables, and performing Stepwise Selection with BIC 0.258 0.186 0.338 0.117 Model9 Unifying the number of calls under one variable “Domestic.Calls” > Stepwise with AIC (multicollinearity fixed) 0.258 0.186 0.338 0.117 Model6 Unifying the minutes under one variable “Domestic.Mins” > Stepwise Selection with AIC (multicollinearity fixed) 0.258 0.186 0.338 0.108 Model12 Domestic.Calls + Domestic.Mins (aggregates) > Stepwise with AIC (multicollinearity fixed) 0.258 0.186 0.338 0.108
  4. 4. We managed to produce a model with a good fit and only 6 variables, that ranks well in all key metrics and has an accuracy of 88%. Variable Estimate Std. Error Significance 95% Conf. Interval (Intercept) -9,18 0,51 *** [-10.20 , -8.20] CustServ.Calls 0,54 0,04 *** [0.46 , 0.62] Has_Int.l.Plan 2,08 0,15 *** [1.78 , 2.38] Has_VMail.Plan -1,29 0,16 *** [-1.62 , -0.97] Intl.Calls 0,15 0,03 *** [-0.21 , -0.10] Intl.Charge 0,39 0,08 *** [0.24 , 0.55] Domestic.Charge 0,10 0,01 *** [0.08 , 0.11] The model we produced Confirming our preliminary analysis § Having an International Plan and a Voicemail Plan are indeed of major importance § The number of Customer Service Calls is a worrying indicator § Regular (domestic) as well as international charges play an important role
  5. 5. Customer A has 1 more Customer Service Call than Customer B Customer Service Calls Having an International Plan Having a Voicemail Plan # of International Calls International Charges Domestic Charges Customer A has an International Plan. Customer B doesn’t. Customer A has a Voicemail Plan. Customer B doesn’t. Customer A has 1 more International Call than Customer B Customer A has 10 more USD in International Charges than Customer B Customer A has 10 more USD in Domestic Charges than Customer B A B Odds of Churn for A are 1,7 times higher than customer B A B Odds of Churn for A are 8 times higher than customer B A B Odds of Churn for A are 70% lower than customer B A B Odds of Churn for A are 14% lower than customer B A B Odds of Churn for A are 14,8 times higher than customer B A B Odds of Churn for A are 11,1 times higher than customer B
  6. 6. Review the characteristics of our international plans and make them better Compare international and domestic rates with our competitors and adapt Promote VoiceMail Plans as they build switching barriers Implement a “pampering” period for customers with 2 or more recent customer service calls to get us back in their good graces How can we solve the problem?
  7. 7. Thank You for your attention Sotiris Baratsas sotbaratsas@gmail.com MSc in Business Analytics

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