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.
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. 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
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. 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
In-Bound:
1. Retail Stores
2. Customer Care
3. Web site
4. Social Media
• SMS/MMS
• Pop up panel
• Notification panel
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
Day 1
Month 1
Month 2
Month 3
10

11. Example: CRM Selection diagram
11

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. 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. 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
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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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.
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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. 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
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