t-mobile: kiss churn goodbye with data-driven campaign management
TRANSCRIPT
T-Mobile: Kiss Churn Goodbye with Data-Driven Campaign Management
Eric Helmer,T-Mobile Sr Manager
Campaign Design and Execution
T-Mobile Overview1. America’s Un-Carrier (NYSE: TMUS)2. 38,000 employees3. 43 million wireless subscribers4. 70,000 distribution points5. $25 billion annual revenue6. Deutsche Telekom maintains 74% ownership
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Reduce Churn - Overview
1. Understand what your customer wants2. Organize around that3. 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”
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1. What Wireless Customers wantCustomer desires:1. No Contracts, they lock me in2. Keep my current phone, only pay for service3. Bring my own phone, only pay for service4. Upgrade to new phone whenever I want5. No “bill shock” – understand what I am paying
for with no hidden fees6. Great network coverage and service
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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
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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
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CRM system and data1. CRM System - Currently use combination of
vendor systems and home grown solutions2. Data - collect in a single data source:
• Current customer data• Current product and services• Historical customer, product, and services data• Customer interactions
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Direct Marketing Channels
Cover all the channels:Out-Bound:1. Direct Mail2. Bill Statements3. Email4. Outbound calling5. On Device
• SMS/MMS• Pop up panel• Notification panel
In-Bound:1. Retail Stores2. Customer Care3. Web site4. Social Media
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Direct Marketing Strategy
Communication types:1. Customer life cycle2. Cross sell/upsell opportunities
• Product (phones, tablets and other devices)• Service plan (voice, text, data)
3. Customer and legal service
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Example: Onboarding Customer Life Cycle
Onboarding 0 -3 Months
Day 0 Month 1 Month 3Month 2Day 1
FPO
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Example: CRM Selection diagram
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Example: Customer Life Cycle Dashboard
Calls #Selected Contact %
• Welcome Calls xx,xxxxx%
Non-Retail
• Welcome Calls xx,xxxxx%
B2B
• Welcome Calls xx,xxxxx%
MBB
• First Bill Calls xx,xxxxx%
• First Bill Calls (B2B) xx,xxxxx%
• Overage Calls xx,xxxxx%
• Welcome Calls xx,xxx(N/A)
Retail
• Welcome Calls(not briefed yet,
AALplanned 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 offersMar Apr May
xx.xM xx.xM xx.xM
# QV Retention offersMar Apr May
x.xM x.xM x.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 clicked2 Customers presented offer3 Dispositioned as accepted
XU Sell 2012 Targets Forecast
• Care $xxxMon target
• Retail $xxxMpending netMRC
• Marketing $xxxMn.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
Campaignsdelivered
Postponed to next month
Campaignscanceled
Postponed from
previous month
Additional ad-hoc
campaign requests
COB campaignsapproved
COB campaigns
deprioritized
COB campaign requests
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Example: Weekly Campaign Performance Report – Segment Analysis
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campaign_id Start_Date End_Date Campaign_Name GroupName Channel Status Take_Type14587 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 & ControlTreatedTaker% 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 & ControlTreatedTaker% 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 & ControlTreatedTaker% 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 & ControlTreatedTaker% 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 & ControlTreatedTaker% 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 & ControlTreatedTaker% CTRLTaker%
campaign_id
14276 1444114450 1454414587 1467514687 1469314703 1471214743 14750
Credit_Class
A B CL O Other
Division - Region
West SouthPacific NortheastCentral ~
Rate_Plan
Data EMEMP LegacyUnsegm... MBB
Churn_Decile
High LowMed Unsegm...
Pooled
FT SLUnseg...
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.
Example: Heat map of take rates
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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.
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Customer Link Analytics is a form of Social Network Analysis
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• 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
Social Network Analysis at T-Mobile – Process
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12 hrs
36 hrs
Cont.
4 hrs
Social Network Analysis at T-Mobile – Hardware and Software
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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)
SNA Population Summary
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-
50,000
100,000
150,000
200,000
250,000
300,000
0 5 10 15 20 25 30 35 40 45 50
Num
ber O
f Com
mun
ities
Community Size
Mean
Median
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 5 10 15 20 25 30 35 40 45 50Community Size
Non T-MobileT-Mobile
Virality Effects in T-Mobile’s Network
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• 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
Identification of Influencers and Followers
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• 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 10Pr
opor
tion
of V
aria
nce
Expl
aine
d
Factor Number
Virality Effect: Influencer Churn Increase the Follower’s Churn by 25%
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0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
0 1 2 3 4 5 6
Vira
lity
Chur
n Li
ft o
r Per
cent
age
Influ
ence
rs
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.
SNA Test Campaign Results
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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 campaign3. Test design: SMS message sent to 15k influencers and 15k non-
influencers offering $50 off any handset upgrade4. The community size affected is about 4 times the target population5. 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
Visualization of SNA Test Campaign Analysis
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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).
SNA Test Campaign Analysis
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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.
Summary - Social Network Analysis
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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. Backtesting clearly 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.
DMA 2013:T-Mobile: Kiss Churn Goodbye with Data-Driven
Campaign ManagementWhat we covered to help you reduce churn:1. What current wireless customers want2. How T-Mobile organized around what the customer wants3. How T-Mobile implements our data driven Direct Marketing strategy4. Case study on Customer Link Analytics CLA showing benefit of focusing on
“influencers”
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Eric Helmer,T-Mobile Sr Manager,
Campaign Design and [email protected]