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