Effect of Bundling of New Telecommunications Service: A Customer Life-Cycle Perspective ITS 15th Biennial Conference Berlin, September 2004 Ann Skudlark, AT&T Labs, with Jae-Hyeon Ahn, KAIST, Wen-Ling Hsu, AT&T Labs, Lynn Sichel, AT&T Labs,

2 Agenda 1. Background 2. Data Collection & Segmentation 3. Churn & Delinquency Analysis 4. Customer Comments Analysis 5. 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 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 aes:

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 –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 –ATT segment: Current AT&T LD customers who added AT&T Local service in June  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 Segment Plan NewOCCATT No All-D OCP +25.0%+17.2%0% With All-D OCP +26.8%+18.2%-3.7% Segment Plan Higher Risk Group Lower Risk Group No All-D OCP +25.0%+0% With All-D OCP +27.1%-5.0% Relative churn rate for each risk group – Lower Risk Group with No All-D OCP is 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 Segment Plan NewOCCATT No All-D OCP +35.8%+19.4%0% With All-D OCP +39.7%+20.9%-0.1% Segment Plan Higher Risk Group Lower Risk Group No All-D OCP +46.0%+0% With All-D OCP +48.3%+0.1% Relative delinquency rate for each risk group - Lower Risk Group with No All-D OCP is 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 1. Customers who churned call more frequently. 2. Higher risk group call more frequently than the lower risk group. 3. NEW segment are most likely to call on a per customer basis. Risk Group HigherRisk GroupLowerRisk Group SegmentRetainedChurnedRetainedChurned NEW OCC ATT 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 1. Comment distributions by risk group are significantly different (Chi-square test at 0.05 significance level). 2. 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. 3. 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. 4. Move turned out to be a primary reason for calling with all the segments.

22 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 and management aes: