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L101 predictive modeling case_study


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Data Analysis related case studies.

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L101 predictive modeling case_study

  1. 1. Case Study: L101 -Fiberbits Predictive Modeling Case Study Customer retention strategy building using Predictive modeling Business Objective: “FiberBits”is an internetservice providercompany.Theyare inthe marketfromlast10 years. They lost almost42% of theircustomersinlast 3 years. Some new customers joinedduringthe same time period. But, the companyisconcernedaboutthe highattrition rate in the customerbase.Theywantto get an ideaonwhat are the mainfactors that leadtocustomerattrition. To reduce the attritionrate,they have introducedvouchersand otherbenefitsprogram.The objective istoidentifythe customerswho are mostlikelyto quitinnext2 yearsand try to retainthembyofferingfree vouchersandbenefits. Problem Statement and Scope of Model The company hascollectedaround10000 customerhistorical datafromlastthree years.We needto builda model thatidentifiesthe customerswhoare mostlikelytoleave.We needtoquantifythe chance of attrition foreach of the customer.The model will be usedonthe active customers.The free vouchersandbenefitswill be giventocustomerswithhigherprobabilitytoattrite innextthree years. For example beloware twocustomers,whoismostlikelytoleave.Whichcustomershouldwe tryto retainbysendingfree vouchers Customer Cust1 Cust2 income 2586 1581 months_on_network 75 35 Num_complaints 4 3 number_plan_changes 1 2 relocated 1 0 monthly_bill 121 133 technical_issues_per_month 4 1 Speed_test_result 85% 95%
  2. 2. Data: The data consistsof nearly10,000 customers.Below are the listof variablesandtheirdescriptions. Variable name Description active_cust The Dependent variable Active-1 (Customer Attrition=No) Not Active – 0 (Customer Attrition=Yes) income Estimated monthly income months_on_network Months on network (Moths from the servicestartday)) Num_complaints Total complaints till now number_plan_changes Number of times the serviceplan is changed relocated 1- Relocated 0 – Not relocated monthly_bill Average monthly bill technical_issues_per_month Average monthly bill Speed_test_result Percent of (Actual speed/Promised speed) Download data from belowlinks Analysis Steps  Data validation  Data cleaning  Identificationof analysistechnique  Buildingpredictivemodel  Removingmulticolliniarity  Final model  Calculatingprobabilitiesforcust-1& cust-2  Final observationsandinferences  Documentationof the approach,codesandresults Discussion forum  Facebook: o on-Guide/1539167863021194 o  Blog: o studycustomer.html  Slide share o
  3. 3. References:  Chapter-7 Data exploration&validationandChapter -11 LogisticRegressionfromthe book Practical BusinessAnalyticsUsingSAS:A Hands-onGuide Business-Analytics-Using-Hands/dp/1484200446  SAScode fromchapter-7& Chapter-11 fromthe book Practical BusinessAnalyticsUsingSAS:A Hands-onGuide Hands/dp/1484200446  Case studyid: Case Study: L101 -Fiberbits