T-Mobile: Kiss Churn Goodbye with Data-Driven Campaign Management
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T-Mobile: Kiss Churn Goodbye with Data-Driven Campaign Management

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T-Mobile: Kiss Churn Goodbye with Data-Driven Campaign Management Presentation Transcript

  • 1. T-Mobile: Kiss Churn Goodbye with Data-Driven Campaign Management Eric Helmer, T-Mobile Sr Manager Campaign Design and Execution
  • 2. T-Mobile Overview 1. America’s Un-Carrier (NYSE: TMUS) 2. 38,000 employees 3. 43 million wireless subscribers 4. 70,000 distribution points 5. $25 billion annual revenue 6. Deutsche Telekom maintains 74% ownership 2
  • 3. Reduce Churn - Overview 1. Understand what your customer wants 2. Organize around that 3. Implement Marketing communication strategy, informing new and current customers you have what they want 4. Case Study: T-Mobile “Customer Link Analytics” to focus our Marketing spend on “influencers” 3
  • 4. 1. What Wireless Customers want Customer desires: 1. No Contracts, they lock me in 2. Keep my current phone, only pay for service 3. Bring my own phone, only pay for service 4. Upgrade to new phone whenever I want 5. No “bill shock” – understand what I am paying for with no hidden fees 6. Great network coverage and service 4
  • 5. 2. T-Mobile aligns on customer needs ATT merger dropped Un-Carrier 2.0: Jump iPhone launch Metro PC merger New CEO John Legere and new CMO Michael Sievert Un-Carrier 1.0: Simple Choice Internal Mktg reorg 2011 2012 20142013 Un-Carrier 3.0: coming soon 2013 LTE roll out to 200 million people in 200 markets 5
  • 6. 3. Marketing Communication Strategy 1. Above the line advertising: • National ad campaigns – utilizing all channels • Sponsorship of leagues and events 2. Direct Marketing: • Outbound Marketing • In-Bound Marketing 3. Word of mouth: • Social Media, Friends and Family, JD Powers 6
  • 7. CRM system and data 1. CRM System - Currently use combination of vendor systems and home grown solutions 2. Data - collect in a single data source: • Current customer data • Current product and services • Historical customer, product, and services data • Customer interactions 7
  • 8. Direct Marketing Channels Cover all the channels: Out-Bound: 1. Direct Mail 2. Bill Statements 3. Email 4. Outbound calling 5. On Device • SMS/MMS • Pop up panel • Notification panel In-Bound: 1. Retail Stores 2. Customer Care 3. Web site 4. Social Media 8
  • 9. Direct Marketing Strategy Communication types: 1. Customer life cycle 2. Cross sell/upsell opportunities • Product (phones, tablets and other devices) • Service plan (voice, text, data) 3. Customer and legal service 9
  • 10. Example: Onboarding Customer Life Cycle Onboarding 0 -3 Months Day 0 Month 1 Month 3Month 2Day 1 10
  • 11. Example: CRM Selection diagram 11
  • 12. Example: Customer Life Cycle Dashboard Calls #Selected Contact % • Welcome Calls xx,xxx xx% Non-Retail • Welcome Calls xx,xxx xx% B2B • Welcome Calls xx,xxx xx% MBB • First Bill Calls xx,xxx xx% • First Bill Calls (B2B) xx,xxx xx% • Overage Calls xx,xxx xx% • Welcome Calls xx,xxx(N/A) Retail • Welcome Calls (not briefed yet, AAL planned after retail) Customer Journey coverage (should define campaigns) Nov Jan Feb Mar Apr May Customer Journey coverage XX% XX% XX% XX% XX% XX% % campaigns triggered by CJ XX% XX% XX% XX%. XX% XX% Campaign request and briefing stability # QV Growth offers Mar Apr May xx.xMxx.xMxx.xM # QV Retention offers Mar Apr May x.xMx.xMx.xM QuikView offer funnel Care Retail • Clicked1 xx% xx% • Presented2 xx% xx% • Accepted3 xx% xx% to be separated for S&D and C Onboarding (0-3 months) Serve & Develop (4-17 months) Confirm (18+ months) 1 Button clicked 2 Customers presented offer 3 Dispositioned as accepted  XU Sell 2012 Targets Forecast • Care $xxxM on target • Retail$xxxMpending netMRC • Marketing $xxxM n.a. Target: XX% Key KPI Key KPI Key KPI Retention 2012 Targets Forecast • # of recontracts • % on contract covered in Churn Dashboard % of delivered campaigns had at least one change request Briefing Changes: XX% ongoing Campaigns delivered Postponed to next month Campaigns canceled Postponed from previous month Additional ad-hoc campaign requests COB campaigns approved COB campaigns deprioritized COB campaign requests 12
  • 13. Example: Weekly Campaign Performance Report – Segment Analysis 13 campaign_id Start_Date End_Date Campaign_Name GroupName Channel Status Take_Type 14587 3/7/2012 4/6/2012 Family Data IB Data Inbound Closed SOC_General Segmentation Attributes 4.8% 1.9% 0.0% 3.1% 1.2% 0.0% 0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 3.5% 4.0% 4.5% 5.0% FT Unsegmented SL Pooled Treat & Control TreatedTaker% CTRLTaker% 5.7% 3.8% 1.9% 0.0% 0.0% 3.3% 2.9% 1.2% 0.0% 0.0% 0.0% 1.0% 2.0% 3.0% 4.0% 5.0% 6.0% EM EMP Unsegmented Data Legacy Rate Plan Treat & Control TreatedTaker% CTRLTaker% 5.4% 5.0% 3.1% 1.9% 0.3% 3.2% 4.3% 2.4% 1.2% 0.0% 0.0% 1.0% 2.0% 3.0% 4.0% 5.0% 6.0% Phone Type Treat & Control TreatedTaker% CTRLTaker% 2.0% 1.0% 0.9% 0.5% 1.3% 0.0% 0.0% 0.0% 0.0% 0.5% 1.0% 1.5% 2.0% 2.5% Unsegmented Med Low High Churn Decile Treat &Control TreatedTaker% CTRLTaker% 2.3% 2.0% 0.0% 0.0% 0.0% 0.0% 1.2% 0.0% 0.0% 0.0% 0.0% 0.5% 1.0% 1.5% 2.0% 2.5% South Central West Northeast Pacific Division Treat &Control TreatedTaker% CTRLTaker% 3.3% 3.3% 3.3% 2.1% 1.5% 1.0% 2.0% 2.0% 2.0% 1.2% 1.0% 0.6% 0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 3.5% L Other O C B A Credit Class Treat &Control TreatedTaker% CTRLTaker% campaign_id 14276 14441 14450 14544 14587 14675 14687 14693 14703 14712 14743 14750 Credit_Class A B C L O Other Division - Region West South Pacific Northeast Central ~ Rate_Plan Data EM EMP Legacy Unsegm... MBB Churn_Decile High Low Med Unsegm... Pooled FT SL Unseg... Phone_Type Data Non-S... SmartP... Uncate... Unseg... Segment Analysis view enables identification of sub-segments of customers where the campaign/offer worked and didn’t work Example: At a holistic level, it’s apparent who in the population the offer appealed most to: non-prime credit classes. Using the slicer, users can filter to one or more sub-segments, (device types, rate plan types, etc). In this example, the best target audience is non-prime, Even More Smartphone customers.
  • 14. Example: Heat map of take rates 14
  • 15. 4. Social Network Analysis (SNA) Social Network Analysis (SNA) is the study of interactions between customers with the goal of identifying relevant customer communities as well the importance of individuals within the community. How can SNA using Customer Link Analytics (CLA) improve marketing? Acquisition • Attract influencer outside the network in the expectation that the community will follow. • Induce T-mobile influencer to pull in off-network followers Cross / Up-Sell • Spread products throughout customer base by pushing to influencers. Retention • Reduce churn by holding on to influencers. 15
  • 16. Customer Link Analytics is a form of Social Network Analysis 16 • According to Wikipedia: ‘A social network is a social structure made up of individuals called "nodes", which are tied (connected) by one or more specific types of interdependency, such as friendship, kinship, common interest, financial exchange‘ etc. • These concepts are often displayed in a social network diagram, where nodes are the points and ties are the lines. • The social network can be mathematically viewed as a graph. Thus graph theoretical approaches to decomposed the network can be used. • Central concepts are community and some importance measure of each individual for the community (centrality). communities
  • 17. Social Network Analysis at T-Mobile – Process 17 Data Acquisition • Call Detail Records Aggregation • One record per interaction between two phone numbers monthly summarized (50M nodes + 1B links = 300GB) Pre- processing • Exclude nodes with low volume, no reciprocity. • Combine usage data to create link weights Customer Link Analysis • Detect communities • Calculate individual metrics Customer Scoring • Score subscribers as influencers/follower 12 hrs 36 hrs Cont. 4 hrs
  • 18. Social Network Analysis at T-Mobile – Hardware and Software 18 Hardware • HP Itanium rx8640 • Operating System: HP-UX v.11.31 • 24 Itanium 2 9100 processors running at 1.6 GHz • 144 GB of RAM Software • SAS v. 9.2 • SAS CLA v. 2.2 (Customer Link Analytics)
  • 19. SNA Population Summary 19 Total phone numbers = 200M After exclusions = 89M T-Mobile phone numbers = 23M Off-Network phone numbers = 66M - 50,000 100,000 150,000 200,000 250,000 300,000 0 5 10 15 20 25 30 35 40 45 50 NumberOfCommunities CommunitySize Mean Median 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0 5 10 15 20 25 30 35 40 45 50 CommunitySize NonT-Mobile T-Mobile
  • 20. Virality Effects in T-Mobile’s Network 20 • Virality is the effect of influencers on followers. • In particular, what is the churn rate of followers given that the corresponding influencer churned compared to the churn rate when the influencer stays. Influencer churn Follower churn
  • 21. Identification of Influencers and Followers 21 • Customer Link Analytics (CLA) software creates many new attributes for each customer • Approximately 200 SNA attributes like betweenness and closeness • These 200 attributes are condensed into four factors scores: • Centrality • Outbound Connections • Outbound Usage • Connected to Churn • Further analysis shows that the centrality score has the strongest association with virality. 0% 5% 10% 15% 20% 1 2 3 4 5 6 7 8 9 10ProportionofVarianceExplained Factor Number
  • 22. Virality Effect: Influencer Churn Increase the Follower’s Churn by 25% 22 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 0 1 2 3 4 5 6 ViralityChurnLiftorPercentageInfluencers Threshhold on Centrality Factor Virality Churn Lift Percentage Influencers • Based on the centrality factor score, we label subscribers as influencers and followers. • Virality churn lift is the churn rate delta of the followers. • The more selective we are with the influencer labeling, the higher the churn lift but the smaller the campaign potential.
  • 23. SNA Test Campaign Results 23 1. Social Networking Analysis (SNA) groups subscribers into non- overlapping communities and identifies leaders and followers within the communities 2. We ran a small SNA test campaign 3. Test design: SMS message sent to 15k influencers and 15k non- influencers offering $50 off any handset upgrade 4. The community size affected is about 4 times the target population 5. The results confirm the virality effect identified during our initial back tests 6. For the test campaign, when the influencer took the offer, the take rate among the followers almost doubled
  • 24. Visualization of SNA Test Campaign Analysis 24 1. The subscribers are grouped into communities (boxes). 2. The communities contain influencers (red) and followers (unfilled). 3. The test campaign targeted some leaders and some followers (cross). 4. Some of the target influencers accepted the offer (check mark). 5. The virality is the community take rate among accepting influencers (green) as compared to the community take rate of accepting followers (orange).   
  • 25. SNA Test Campaign Analysis 25 1. Since SNA campaigns rely on virality, the direct effect on the targeted population is not as important as the indirect effect on the rest of the community. 2. Our test confirmed, virality only occurs if an influencer is targeted and the influencer accepted the offer. Otherwise, the take rates remain flat.
  • 26. Summary - Social Network Analysis 26 1. Customer Link Analysis (CLA), while difficult, provides a promising opportunity to reduce churn and focus campaign resources. 2. SNA identifies communities and influencers within the communities 3. T-Mobile’s average community size is about 18 subscribers. 4. 5% of subscribers are influencers. 5. Backtestingclearly establishes that influencer churn is associated with a 25% increase in follower churn. 6. Focusing marketing dollars on influencers will reduce churn for the whole community.
  • 27. DMA 2013: T-Mobile: Kiss Churn Goodbye with Data-Driven Campaign Management What we covered to help you reduce churn: 1. What current wireless customers want 2. How T-Mobile organized around what the customer wants 3. How T-Mobile implements our data driven Direct Marketing strategy 4. Case study on Customer Link Analytics CLA showing benefit of focusing on “influencers” 27 Eric Helmer, T-Mobile Sr Manager, Campaign Design and Execution Eric.Helmer@T-Mobile.com