Effect of Bundling of New Telecommunications Service: A Customer Life-Cycle Perspective

1. Effect of Bundling of New Telecommunications Service:A Customer Life-Cycle Perspective Ann Skudlark, AT&T Labs, skudlark@att.com with Jae-Hyeon Ahn, KAIST, jahn@kgsm.kaist.ac.kr Wen-Ling Hsu, AT&T Labs, hsu@homer.att.com Lynn Sichel, AT&T Labs, sichel @homer.att.com ITS 15th Biennial Conference Berlin, September 2004

2. Agenda • Background • Data Collection & Segmentation • Churn & Delinquency Analysis • Customer Comments Analysis • Conclusions

3. 1. Background • Traditional view as product-focused and transactions oriented • Environmental factors (deregulation, competition, shrinking margins) have fueled the recognition of the importance of life-cycle concept – both acquiring and retaining customers to achieve profitability • Currently a proliferation of choices • Provided customers with a wide array of options. • However, receiving services from many different service providers can be inconvenient. • Bundling service as a competitive advantage

4. Objectives • To understand the impact of bundled services • Statistical analyses were performed for a new bundled service in a major telecommunications service company. • We gained perspectives and insights for decision support • Which customers to target. • How to handle delinquency problems. • How to retain customers in the buying cycle during the entire customer life-cycle. • Empirical study with numerous data sources

5. Specifically, • Analyzed the churn and delinquency rates • For newly provisioned Local customers with or without unlimited telecommunications bundle. • Collected and analyzed the calls which were made into care centers by the customers who subscribed to the bundled service • Call frequency analyses. • Content analysis: • Comments of those who churned during a specific study period were analyzed through text classification.

6. aes: Quick Summary: • The impact on the bundle overall did not have the positive effect that we initially expected • Some value for current low risk accounts. • Negative value for NEW high risk accounts. • Based on the impacts of churn, delinquency and customer care cost, the NEW customer segment, in particular, warrants careful planning for acquisition, provisioning and management

7. 2. Data Collection & Segmentation • Data collected on Local customers who joined AT&T in June 2003 • ~ 200K accounts • Data analysis • Segmented by three dimensions • Tracked customers over 6 month time period

8. Customer Segmentation • First Dimension: Subscription to service bundle • Based on whether customers subscribe to a bundled Optional Calling Plan (OCP) – referred to as All-D OCP. • Second dimension: Customer’s prior LD PIC (Primary Inter Connect) status • NEW segment: Customers who did not have a prior relationship with AT&T and selected Local and Long Distance service in June 2003. • OCC segment: Customers that had a previous relationship with AT&T, but switched from an OCC (Other Common Carrier) to AT&T subscribing to both Local and LD services in June 2003. • ATT segment: Current AT&T LD customers who added AT&T Local service in June 2003. • Third dimension: Risk group determined by credit score • Split into two groups representing higher risk group and lower risk group.

9. Dependent Variables • Customer Status – Churn Analysis • As of December 31, 2003, we analyzed the customer status of Retained vs. Churned (customers who dropped Local service). • Customer Payment Status - Delinquency • As of December 31, 2003, we analyzed the customer payment status. • Delinquent (2+ CPD – Cycles Past Due) vs. Paid

10. 3. Churn Rate Analysis Relative churn rate for each segment - ATT with No All-D OCP is the default Relative churn rate for each risk group – Lower Risk Group with No All-D OCPis default

11. Observations – Churn Rate • No OCP churn is slightly higher than All-D OCP (statistically significant) with 1.7% difference • Significant across segments • NEW and OCC has higher churn with All-D OCP. • ATT has lower churn with All-D OCP. • Significant between credit scores • Higher risk group more churn with and without All-D OCP. • Lower risk group with All-D OCP 5% lower. • Importance of managing across dimensions! • Note also Chi-square statistic shows customer segment and risk group are not independent

12. Delinquency Rate Analysis Relative delinquency rate for each segment - ATT with No All-D OCP is the default Relative delinquency rate for each risk group -Lower Risk Group with No All-D OCPis default

13. Observations – Delinquency Rate • 0.3% difference (not statistically significant) with or without All-D OCP • Across segments • NEW and OCC have higher delinquency with All-D OCP. • Issues of Affordability? • ATT delinquency difference not significant with or without All-D OCP. • Significant between credit scores • Higher risk group more delinquent with and without All-D OCP. • Lower risk group not significant with or without All-D OCP.

14. 4. Customer Comments Analysis • Customer care cost is one of the major costs in providing telecommunications service • By studying customer comments, we can identify root causes of why customers call and fix and prevent additional problems. • Ultimate Objective • Reduce customer care costs and increase customer satisfaction. • Collected and analyzed 800K comments

15. Call Frequency Analysis • Customers who churned call more frequently. • Higher risk group call more frequently than the lower risk group. • NEW segment are most likely to call on a per customer basis. Call Frequency Index

16. Call Contents Analysis

17. Distribution of Overall Comments

18. Observation on Overall Comments • Billing, NDT (No Dial Tone), and Misdirected are reasons to call which warrant process improvement • Customers’ comments in ATT segment differ significantly (Chi-square at 0.05 significance) from those of OCC and NEW segments • Proportional difference between Retained and Churned (z-test 0.05 significance) • Results are similar on the Account Inquiry and Billing categories. • Statistically different on the Account Order, Cancel, Misdirected, and NDT/Repair categories. • Need for Machine Classification Tool • We identified additional reasons why the churned customers called.

19. Using Machine Classification Tool • Established a set of “reasons for churn” based on keyword analysis • Different perspective than the categories used by the Care Representatives. • Example of findings • Identified comments related to Move as a reason for churn: • Reasons such as Sickness, Death, and Abandoned Line are a subset of Move • Issues labeled previously as Account Inquiry by Reps are now classified into more detailed reasons such as Error/Delay, Move, Alternative Local Provider, and Repair. • A training sample was developed with 1,000 customer comments with assigned reasons for churn • The classification tool was then used to build the training model, and to classify about 20,000 randomly selected comments for each segment.

20. Reasons for Churn • Analysis by Machine Classification Algorithm

21. Observations on Machine Learning Results • Comment distributions by risk group are significantly different (Chi-square test at 0.05 significance level). • Similar to the results of analyses based on Rep categorization, issues such as Billing category happen equally across NEW, OCC and ATT segments. • while customers in the NEW segment reported more on issues related to NDT/Repair. • the Billing category is compared by risk group across all segments, the higher risk group tends to have more comments than the lower risk group. • Move turned out to be a primary reason for calling with all the segments.

22. aes: 5. Conclusions • The impact on the All-D OCP bundle overall did not have the positive effect that we initially expected • Bundling service appears to have more retention effect in the ATT with lower risk segment • Customers segments and risk group provided much more important information on customer churn and delinquency rate. • All-D bundle has a negative effect on the churn and delinquency rates in the high risk group • Customer are financially challenged to pay the price of the service. • Significant differences of call frequency by customer segment and risk group • Customers in the risk group of the NEW segment tend to call more frequently. • Depending on the customer segment and risk group, customers call in to care centers with different reasons • Based on the impacts of churn, delinquency and customer care cost, the NEW customer segment, in particular, warrants careful planning for acquisition, provisioning andmanagement