Payment Behavior Analysis and Prediction Project at a Telco Company

Transcription

1 Payment Behavior Analysis and Prediction Project at a Telco Company Özge Neslihan Çaycı SAS Turkey

2 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

3 About the company! The leading Mobile Operator in Turkey! About 10 million subscribers

4 Marketing Department CRM initiated project

5 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

6 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

7 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

8 Business Needs Score the customers for their payment risk Apply different collection actions Increase the rate of payment

9 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

10 Collection Process! Payment on due is very important for the cash flow planning for the reduced risk!

11 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

12 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

13 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

14 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.

15 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

16 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)

17 Project Architecture Data sources Decision oriented server Production Database Customer Database Reports on customer and analysis Reporting External Data Select Control Agregate Merge Transform Transpose Payment Analysis DM Data Mining DataWarehouse server Enterprise DataWarehouse Data sources quality improvement Data Checking EMTL STORAGE ANALYTICS / VALUE ADDITIONS

18 Data Mining Modelling Techniques! SAS Enterprise Miner!Segmentation and Profiling!Variable Selection!Clustering!Predictive Modelling!Decision Trees!Regression!Assessment

19 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

20 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...

21 Questions???