Download Payment Behavior Analysis and Prediction Project at a Telco Company

Payment Behavior Analysis and
Prediction Project at a Telco Company
Özge Neslihan Çaycı
SAS Turkey
Copyright © 2002 , SAS Institute Inc. All rights reserved.
Agenda
!
Project Background
About the company
! Previous Marketing Department CRM project
!
!
Finance Department Payment Behavior Analysis Project
Business Needs
! Business Answers
! Business Benefits
! Project Architecture and Modelling techniques
!
!
Questions
Copyright © 2002 , SAS Institute Inc. All rights reserved.
About the company
The leading Mobile Operator in Turkey
! About 10 million subscribers
!
Copyright © 2002 , SAS Institute Inc. All rights reserved.
Marketing Department
CRM initiated project
Copyright © 2002 , SAS Institute Inc. All rights reserved.
Business needs (Marketing)
!
Customer Understanding
Discover who the customers are to gain a global market
knowledge
!
Segmentation and Profiling
Separate the customers into homogeneous groups according
to the call behavior and service usage
!
Churn Prediction
Predict which customers will churn and determine the causes
Copyright © 2002 , SAS Institute Inc. All rights reserved.
Business Answers (Marketing)
!
!
Customer Understanding
!
Identification of major pains (fraud, suspension)
!
Initiated a new project on the payment behavior
Segmentation and Profiling study
Know their customers call behavior better
! Started a new tariff which has received the most response
ever
!
!
Churn Prediction
Churners are not the most valuable customers
! Able to run models for different targets
!
Copyright © 2002 , SAS Institute Inc. All rights reserved.
Agenda
!
Project Background
About the company
! Previous Marketing Department CRM project
!
!
Finance Department Payment Behavior Analysis Project
Business Needs
! Business Answers
! Business Benefits
! Project Architecture and Modelling techniques
!
!
Questions
Copyright © 2002 , SAS Institute Inc. All rights reserved.
Business Needs
Score the customers for their payment risk
Apply different collection actions
Increase the rate of payment
Copyright © 2002 , SAS Institute Inc. All rights reserved.
Project Scope
Customer Understanding
Reaction to the current collection actions, realization of
different payment behaviors
Segmentation and Profiling
Separate the customers into homogeneous groups
according to their payment behaviors for applying proper
collection action sets
Non-payment Prediction
Predict which customers will pay late and which
customers will become suspended due to
nonpayment
Copyright © 2002 , SAS Institute Inc. All rights reserved.
Collection Process
!
Payment on due is very important for
• the cash flow planning
• for the reduced risk!
Copyright © 2002 , SAS Institute Inc. All rights reserved.
Customer Understanding
!
The percentage of the number of payments and
the amount of payments done are slightly different
in terms of the time it takes to pay.
• Higher invoices tend to be paid later...
!
The payment of the customers are studied in
terms of
• Their reactions to the collection process
• The way of payment they prefer
• The past payment behavior
Copyright © 2002 , SAS Institute Inc. All rights reserved.
Segmentation and Profiling
!
After the study of the variables for segmentation three
variables were selected:
• past payment behavior
• past payment amount
• age of the contract
!
Finance department grouped the segments into two groups
as good and late payers and planned different collection
actions for these groups
!
The 7 segments are studied and grouped into 3 for the new
collection actions as
• Strict, Moderate, Tolerant
Copyright © 2002 , SAS Institute Inc. All rights reserved.
Late Payment Prediction
Target Variables
• Paying after the due date
• Being Suspended due to non-payment
! Model variables
• past payment behavior
• past invoice amount
!
Can easily predict the late payers checking the
previous payment behavior
Copyright © 2002 , SAS Institute Inc. All rights reserved.
Business Benefits
With the differentiation of the collection actions an
improvement of 10% has been achieved first month
with the success of the collections.
The improvement has also significantly affected the
success of the collections in the coming months which
is sentenced as a great profit for the company.
The predictive models for non-payment and
suspension are going to be used by the marketing
department for the campaigns that will be generated.
Copyright © 2002 , SAS Institute Inc. All rights reserved.
Project Challenges
!
Limited time for the project delivery
The operational system for the differentiation of the
collection actions was in place and the finance
department was waiting for the model results
!
Human capital
Acquire analytical competence inside the finance
department
!
Methodology
Gain knowledge on data warehousing and data mining
!
Environment
Economic crisis caused sudden changes in the
payment behavior and the company objectives
Copyright © 2002 , SAS Institute Inc. All rights reserved.
Project Organization
SAS Data Mining Methodology
! Project Details
!
SAS team (200 man days)
Project Manager
Özge Çaycı (SAS Turkey)
Business Specialist
Guillaume Leorat (SAS France)
Warehouse Architect
Timothee Robert (SAS France)
! Customer team (5 people involved, 200 man days)
! Duration (5 months)
!
Copyright © 2002 , SAS Institute Inc. All rights reserved.
Project Architecture
Data sources
Decision oriented server
Production
Database
Reporting
Customer
Database
Reports on customer
and analysis
External Data
Select
Control
Agregate
Merge
Transform
Transpose
Payment Analysis
DM
Data Mining
DataWarehouse server
Enterprise
DataWarehouse
EMTL
Copyright © 2002 , SAS Institute Inc. All rights reserved.
Data sources quality Data Checking
improvement
STORAGE
ANALYTICS / VALUE ADDITIONS
Data Mining Modelling Techniques
!
SAS Enterprise Miner
!Segmentation and Profiling
! Variable Selection
! Clustering
!Predictive Modelling
! Decision Trees
! Regression
! Assessment
Copyright © 2002 , SAS Institute Inc. All rights reserved.
Conclusion
!
!
!
!
Implementing seperate segmentation models for Finance
and Marketing resulted with more accurate and useful
customer groups
The marketing call behavior segments and the finance
payment behavior segments are merged for a company
segmentation that will be used by the customer care and
sales departments
The marketing and the sales departments merged with a
reorganization after the marketing segmentation project and
divided into three departments as a marketing team serving
each one of the three main customer groups
Marketing Department is working on new data mining
projects for identifying cross selling opportunities
Copyright © 2002 , SAS Institute Inc. All rights reserved.
Upcoming Initiatives
!
!
Finance department is in need of a modelling
system that will serve their cash flow forecasting
need and SAS ETS is being evaluated
Fraud department is also evaluating Enterprise
Miner for the tuning and the assessment of their
Fraud Management System parameters
Management has put a lot of belief and hope into
the data mining teams in the company...
Copyright © 2002 , SAS Institute Inc. All rights reserved.
Questions ???
Copyright © 2002 , SAS Institute Inc. All rights reserved.