FiberBits, an internet service provider, has seen a 42% customer attrition rate over the last 3 years and wants to build a predictive model to identify customers most likely to quit in the next 2 years. The model will be built using historical data on nearly 10,000 customers containing variables like income, time as a customer, complaints, billing amounts, and technical issues. Customers identified as higher risk will be offered incentives like vouchers to encourage them to stay.

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. 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
https://drive.google.com/file/d/0B7Zo00OSj1W6REM0bC1mRGpjRnM/view?usp=sharing
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 https://www.facebook.com/pages/Practical-Business-Analytics-Using-SAS-A-Hands-
on-Guide/1539167863021194
o https://www.facebook.com/groups/PracticalBusinessAnalytics/
 Blog:
o http://practicalbusinessanalytics.blogspot.in/2015/01/predictive-modeling-case-
studycustomer.html
 Slide share
o http://www.slideshare.net/21_venkat

3. References:
 Chapter-7 Data exploration&validationandChapter -11 LogisticRegressionfromthe book
Practical BusinessAnalyticsUsingSAS:A Hands-onGuide http://www.amazon.com/Practical-
Business-Analytics-Using-Hands/dp/1484200446
 SAScode fromchapter-7& Chapter-11 fromthe book Practical BusinessAnalyticsUsingSAS:A
Hands-onGuide http://www.amazon.com/Practical-Business-Analytics-Using-
Hands/dp/1484200446
 Case studyid: Case Study: L101 -Fiberbits