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

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

  1. 1. T-Mobile: Kiss Churn Goodbye with Data-Driven Campaign Management Eric Helmer, T-Mobile Sr Manager Campaign Design and Execution
  2. 2. T-Mobile Overview 1. 2. 3. 4. 5. 6. America’s Un-Carrier (NYSE: TMUS) 38,000 employees 43 million wireless subscribers 70,000 distribution points $25 billion annual revenue Deutsche Telekom maintains 74% ownership 2
  3. 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. 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. 5. 2. T-Mobile aligns on customer needs 2011 2012 New CEO John Legere and new CMO Michael Sievert ATT merger dropped 2013 2014 Internal Mktg reorg Un-Carrier 3.0: coming soon Un-Carrier 1.0: Simple Choice iPhone launch Metro PC merger 2013 LTE roll out to 200 million people in 200 markets Un-Carrier 2.0: Jump 5
  6. 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. 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. 8. Direct Marketing Channels Cover all the channels: Out-Bound: 1. Direct Mail 2. Bill Statements 3. Email 4. Outbound calling 5. On Device In-Bound: 1. Retail Stores 2. Customer Care 3. Web site 4. Social Media • SMS/MMS • Pop up panel • Notification panel 8
  9. 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. 10. Example: Onboarding Customer Life Cycle Onboarding 0 -3 Months Day 0 Day 1 Month 1 Month 2 Month 3 10
  11. 11. Example: CRM Selection diagram 11
  12. 12. Example: Customer Life Cycle Dashboard Customer Journey coverage (should define campaigns) Target: XX% 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% Briefing Changes: XX% Campaign request and briefing stability ongoing COB campaign requests Onboarding (0-3 months) Calls Key KPI COB COB campaigns campaigns deprioritized approved Key KPI Contact % Welcome Calls Non-Retail xx,xxx xx% • Welcome Calls B2B xx,xxx xx% • Welcome Calls MBB xx,xxx xx% • First Bill Calls xx,xxx xx% • • • • First Bill Calls (B2B) xx,xxx xx% XU Sell 2012 • Overage Calls xx,xxx xx% • Welcome Calls Retail xx,xxx(N/A) • Welcome Calls AAL (not briefed yet, planned after retail) Postponed from previous month Serve & Develop (4-17 months) #Selected • Additional ad-hoc campaign requests Mar Apr Campaigns Postponed to Campaigns canceled next month delivered Confirm (18+ months) # QV Growth offers May xx.xMxx.xMxx.xM QuikView offer funnel Clicked1 Presented2 Accepted3 Care  Mar  Retail xx% xx% xx% Targets Forecast Retention 2012 $xxxM on target • % on contract to be separated for S&D and C 1 Button clicked 2 Customers presented offer 3 Dispositioned as accepted # of recontracts • Key KPI # QV Retention offers Apr May x.xMx.xMx.xM xx% xx% xx% % of delivered campaigns had at least one change request • Care • Retail$xxxMpending netMRC • Marketing $xxxM n.a. Targets Forecast covered in Churn Dashboard 12
  13. 13. Example: Weekly Campaign Performance Report – Segment Analysis Segmentation Attributes campaign_id 14441 14544 14675 14693 14712 14750 campaign_id Credit_Class 4.8% 6.0% 3.1% 1.9% 1.2% 1.0% 0.3%0.0% 0.0% 1.2% 0.0% 0.0% 0.0% 0.0% 0.0% Data Legacy Unsegmented Division Treat & Control 0.0% 0.0% 0.0% 1.0% 0.0% 0.5% 0.0% 0.0% 0.5% 0.0% Unsegmented Med Low High 1.0% 1.5% 0.5% 0.6% 2.0% 1.0% 1.5% 0.9% 1.0% 2.5% 1.0% 1.5% 1.0% 2.1% 3.0% 2.0% 1.3% CTRLTaker% 1.2% 2.0% TreatedTaker% 3.5% 3.3% 2.5% Credit Class Treat & Control CTRLTaker% 3.3% TreatedTaker% 2.0% CTRLTaker% 3.3% EMP 2.0% EM 2.0% SL 2.0% Non-S... Uncate... 3.1% 2.4% 0.0% Unsegmented TreatedTaker% 1.5% 3.2% 2.0% 1.9% Churn Decile Treat & Control SL 5.0% 4.3% 3.0% 2.9% CTRLTaker% 1.0% 0.0% Low Unsegm... Phone_Type Data SmartP... Unseg... 3.8% 2.0% 1.2% 5.4% 4.0% 3.3% 3.0% 1.9% 2.5% FT Unseg... 6.0% 5.0% 4.0% FT Pooled TreatedTaker% 5.7% 5.0% Churn_Decile High Med Phone Type Treat & Control CTRLTaker% 0.0% EM Legacy MBB TreatedTaker% 2.0% Data EMP Unsegm... Take_Type SOC_General Rate Plan Treat & Control CTRLTaker% 0.0% Rate_Plan 5.0% 4.5% 4.0% 3.5% 3.0% 2.5% 2.0% 1.5% 1.0% 0.5% 0.0% Status Closed 0.0% South Northeast ~ TreatedTaker% Channel Inbound 1.2% West Pacific Central GroupName Data Pooled Treat & Control C Other Division - Region Campaign_Name Family Data IB 0.0% B O End_Date 4/6/2012 2.3% A L Start_Date 3/7/2012 14587 0.0% 14276 14450 14587 14687 14703 14743 0.5% 0.0% South Central West Northeast Pacific L Other O C B A 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. 13
  14. 14. Example: Heat map of take rates 14
  15. 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 Cross / Up-Sell • Spread products throughout network in the expectation that customer base by pushing to the community will follow. Retention • Reduce churn by holding on to influencers. influencers. • Induce T-mobile influencer to pull in off-network followers 15
  16. 16. Customer Link Analytics is a form of Social Network Analysis • 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. • communities Central concepts are community and some importance measure of each individual for the community (centrality). 16
  17. 17. Social Network Analysis at T-Mobile – Process Data Acquisition Preprocessing Customer Link Analysis Customer Scoring • Call Detail Records Aggregation • One record per interaction between two phone numbers monthly summarized (50M nodes + 1B links = 300GB) Cont. • Exclude nodes with low volume, no reciprocity. • Combine usage data to create link weights 36 hrs • Detect communities • Calculate individual metrics • Score subscribers as influencers/follower 12 hrs 4 hrs 17
  18. 18. Social Network Analysis at T-Mobile – Hardware and Software 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) 18
  19. 19. SNA Population Summary 300,000 Median Total phone numbers = 200M Number Of Communities 250,000 Mean 200,000 150,000 100,000 50,000 After exclusions = 89M 0 5 10 15 100% 20 25 30 35 40 45 50 35 40 45 50 Community Size 90% 80% 70% 60% T-Mobile phone numbers = 23M Off-Network phone numbers = 66M Non T-Mobile 50% T-Mobile 40% 30% 20% 10% 0% 0 5 10 15 20 25 30 Community Size 19
  20. 20. Virality Effects in T-Mobile’s Network • Influencer churn 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. Follower churn 20
  21. 21. Identification of Influencers and Followers • 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: • • Outbound Connections • Outbound Usage • • Centrality Connected to Churn Proportion of Variance Explained • 20% 15% 10% 5% 0% 1 Further analysis shows that the centrality score 2 3 4 5 6 7 8 9 Factor Number has the strongest association with virality. 21 10
  22. 22. Virality Effect: Influencer Churn Increase the Follower’s Churn by 25% 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. 45% Virality Churn Lift or Percentage Influencers • 40% 35% 30% 25% 20% Virality Churn Lift 15% Percentage Influencers 10% 5% 0% 0 1 2 3 4 Threshhold on Centrality Factor 5 6 22
  23. 23. SNA Test Campaign Results 1. 2. 3. 4. 5. Social Networking Analysis (SNA) groups subscribers into nonoverlapping communities and identifies leaders and followers within the communities We ran a small SNA test campaign Test design: SMS message sent to 15k influencers and 15k noninfluencers offering $50 off any handset upgrade The community size affected is about 4 times the target population 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 23
  24. 24. Visualization of SNA Test Campaign Analysis 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).    24
  25. 25. SNA Test Campaign Analysis 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. 25
  26. 26. Summary - Social Network Analysis 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. 26
  27. 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” Eric Helmer, T-Mobile Sr Manager, Campaign Design and Execution Eric.Helmer@T-Mobile.com 27

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