Introduction to Customer Value Management

1. CVM Introduction
Eric Smith
July 12, 2001

2. Agenda
Introduction
Background on CVM
CVM Case Studies from Bell Mobility
• Background
• Prepaid
• Postpaid
Engagement Structure
Page 2

3. Introduction
Session Goals
• Goals:
– Introduce CVM concepts for C-Level Clients and Prospects
• Worksteps
– Outline why CVM is critical for companies to meet their financial objectives
– Explain components of CVM: data for analysis; understanding of customer
economics, and customer behavior patterns; offer design; results from tracking and
improvement cycles
– Show Bell Mobility examples in the prepaid and the postpaid segments
Lecture based
– Work on the Sprint PCS case
• Structure work – timeline, team, deliverables, initial hypotheses
• Deliver findings – story line analysis, create recommendations in the areas of
churn and migration - 2 teams
Case workshop based
The two parts – lecture and case based – will ensure that both the tools and
the examples of CVM are introduced.
Page 3

4. Introduction
What is Customer Value Management (CVM)?
• CVM helps corporations develop tailored products and services to their
customers, in order to maximize profits on an individual customer level
• The goal of CVM is to move towards mass customized offers and price
discrimination based on:
– Willingness to pay (both consumer and corporate)
– Current customer value and usage profiles
– Churn and migration risks
• CVM enables companies to manage their firm value in the face of rapidly
decreasing prices and potentially slower acquisition growth
• Specifically, CVM generates or preserves value through:
– Usage stimulation through micro-targeted offers
– Rate plan and feature migration management through improved understanding of
reprice potential and proactive offer design
– Churn prevention through improved predictive modeling and targeted retention
strategy
– Improved acquisition strategies which consider existing base impact
Page 4

5. Introduction
What is the difference between CVM and CRM?
Customer VALUE Management
Customer RELATIONSHIP
Management
Focus
• Improve profitability by
delivering targeted offers
• Retain customers by improving
customer interactions
Lever for Change
• Product offers
• Customer touchpoints
Approach
• Hypothesis, data-driven
• Integrated, comprehensive
Capabilities
• Capture detailed customer data;
ability to deliver micro-targeted
offers
• Channel integration to offer a
consistent experience; “know
the customer”
Metric
• Customer profitability
• Customer retention
Customer Expectations
• Exceed on customer value
• Exceed on customer service
• Direct customer to most
profitable offerings
• …is acceptable for low value
customers
• Customer migration patterns;
reasons for churn; value drivers
• Streamline and improve
processes
Customer Service
Attrition…
Required Understanding
• …should be reduced
• Customer needs and
expectations; channel usage
CVM is focusing on creating profitable customer relationship.
Page 5

6. Agenda
Introduction
Background on CVM
CVM Case Studies from Bell Mobility
• Background
• Prepaid
• Postpaid
Engagement Structure
Page 6

7. Background on CVM
Development of CVM
•
The CVM practice was developed by DiamondCluster in North America for wireless
carriers. Since then we have used it for LD and have developed the IC for retail
banking
•
Successful CVM efforts bring together a wide variety of skills in the DCI consulting
team, including marketing strategy, microeconomic analysis, statistical modeling,
and information technology deployment
•
Current CVM initiatives:
Bell
Canada
Bell
Mobility
Sprint PCS
TIM
Telesp
CW Optus
Telecom New Zealand
Page 7

8. Background on CVM
What is the Economic Foundation of CVM?
After CVM Approach
Before CVM approach
Additional potential revenue/ consumer
surplus created by micro-offers
Uncaptured consumer surplus
Carrier revenue
Price
Price
Uncaptured consumer surplus
Carrier revenue
Consumer demand
curve shifts out with
tailored products
Market
Demand Curve
Rate Plan 1
Existing
Base Focus
Rate Plan 2
Existing
Base Focus
Market
Demand Curve
Broad
Rate Plan
- New
Users
New
Rate Plan 3
Existing
Base Focus
Old
Quantity
Micro-Offers to Existing Base
The result of a successful CVM approach is the shifting out of the
consumer demand curve and the capturing of consumer surplus.
Page 8
Quantity

9. Background on CVM
How Do We Approach CVM?
DATA
all data at individual transaction level:
call data records from switch, daily account adjustments and transactions, daily account profile updates
Customer
Economics
•
Customer
Behavior
Understand drivers of
individual user
profitability, profits by
segment
•
•
•
Offer
Design
How do customers
behave over time?
What types of behavior
are linked?
What actions change
behavior and the
corresponding
economic drivers?
•
Target individual
users with specific
offers
Results Tracking/
Improvement Cycle
•
Quantification of
impact, incorporate
results into future
offer design
FINANCIAL RESULTS
Measurable financial impact such as
usage stim for low users, prevented migration reprice, prevented churn
Through micro-targeted offers, DiamondCluster has used subscriberlevel data to create real financial results, in usage, migration, and
churn.
Page 9

10. Background on CVM
Mobile Markets in the US
Industry Growth
# of subscribers
200
Penetration
160
60 mins.
177
Bear Stearns
Strategis
153
126
100
80
67
54
54
60
0.48
0.45
0.43
0.45
0.37
0.4
0.33
0.33
0.3
0.2
54
40
250 mins.
0.54
0.5
67
67
1,000 mins.
140
129 52%
116
101
120
102
114
83 86
106
43%
98
86
120
100 mins.
66%
0.6
140
Product Launches
Price / minute ($)
500 mins.
Merril Lynch
180
Price Declines
0.23
0.2
0.21
0.16
0.19
0.21
0.16
• VAS Services
• Roaming inclusive
plans
• Text messaging
services
• WAP, browser
services
• Location based
services
• 3G services
0.16
0.12
0.1
20
0
0.10
0.09
0.12
20
02
*
20
03
*
20
00
*
20
01
*
19
99
19
98
19
97
0.0
Apr. 27,
1998
Feb. 15,
1999
Aug. 9,
1999
Apr. 17,
2000
Year
Source: Merril Lynch.
(*) forecasted.
Source: Wireless week, Washington D.C.
The mobile telecom industry is unique in its rate of growth, price
declines, and changing nature of end user services, requiring dedicated
thinking about its base management issues.
Page 10

11. Background on CVM
Relative CVM Complexity for Mobile Operators
• Limited separation of
purchaser and consumer
• Some competition by call
with override codes
• Low switching cost
Potential
Complexity of
CVM Offers
High
•
•
•
•
Frequent separation of purchaser and consumer
Limited transferability (name and ID)
Huge potential range of products (city pairs)
Competition per trip, with medium switching costs
Mobile
carriers
Airlines
Long distance
operators
Financial
services
Low
• No separation of purchaser
and consumer
• High potential
transferability
• Large range of products
• Competition per item, low
switching costs
Traditional retail:
movies, clothing,
music, books, etc
Low
High
Transactions per User
• Frequent separation of
purchaser and consumer
• No transferability (unique
mobile number)
• No competition per call,
competition by bundled
services only, with high
switching costs
• No separation of purchaser
and consumer
• Limited transferability
• Large range of products
• Competition per
transaction, low to medium
switching costs
The mobile sector is one of the most complex industries for CVM data
analysis, given the sheer volume of customer transactions and the
potential complexity of pricing each transaction.
Page 11

12. Agenda
Introduction
Background on CVM
CVM Case Studies from Bell Mobility
• Background
• Prepaid
• Postpaid
Engagement Structure
Page 12

13. CVM Case Studies
Bell Mobility Overview
EOP Subscribers
000s subs
Revenue
C$M
Total subs growing at 20-25% p.a.
Prepaid share stable at 40%
Prepaid
Revenues growing at 7-22% p.a.
Postpaid
1036.6
857.0
126.3
509.1
1335.4
1454.9
97
98
99
863
1825.5
00
929
97
1221.0
1349.0
98
99
MoU
Prepaid
95
30.1%
28.4%
27.2%
27.1%
98
99
00
01
98
99
44
42
37
Notes:
Postpaid
221
195
186
165
97
01
Postpaid MoU
increasing at
5-13% p.a.
Due to platform error,
incoming minutes are
not billed for
Minutes per
month
Market share stabilizing after
entry of two, digital only
competitors
32.2%
00
01
Market Share (Subs)
%
1,394
1,134
981
00
01
All 2001 figures are estimates. Source: company publications.
Bell Mobility is the incumbent wireless carrier in Ontario and Quebec, with
C$1.4B revenue and 2.8 M subscribers.
Page 13

14. CVM Case Studies – Background
Overview of CVM Phases at Bell Mobility
Bell Mobility
CVM Approach
• Market growth focused in pre-paid segment (BM
had no presence, competitor launched prepaid
product)
• Phase I: Prepaid
- Analyzed revenue impact of introducing prepaid
product through estimation of cannibalization of
low end post-paid revenue and growth in pre-paid
subscriber base and revenue
• Low churn rates (1.5% per month) compared to
industry average
• Phase II: Postpaid
- Analyzed revenue impact of existing strategies for
usage stim, customer retention, and rate plan
migrations. We widely deployed successful
initiatives and abandoned or modified currently
unsuccessful strategies
• Lagged competitors on MoU but led on average
revenue per minute (ARPM)
• Complex systems and offers - 1,200 separate rate
plans, 300 features
• No analysis of migration patterns
• Phase III: Enterprise
- Developed tool to calculate profitability of each
customer in the segment and the impact of
alternative offers in terms of value to customer
and profit to BM
• Sophisticated, third generation data warehouse
prior to DCI presence, but no CDR level data and
minimal tracking of campaign effectiveness
Evaluation of the competitive positioning of Bell Mobility led to
prioritization of CVM initiatives.
Page 14

15. CVM Case Studies – Background
Benefits of CVM at Bell Mobility
Impact of Successive CVM Phases
Annual EBITDA impact
(C$, million)
$100
18% improvement
of annual EBITDA 7
$90
Achievements from Each Phase
• Phase I: Prepaid
- Analysis of profitability of prepaid product led to
successful product launch and total revenue
gains of C$10 million per annum (based on
40% cannibalization of low-end post paid)
18
$80
6
$70
$60
• Phase II: Postpaid
- Postpaid analysis focusing on targeted feature
sales, migration management, churn and
improved acquisition strategies led to revenue
savings of C$70 million per annum
42
$50
98
$40
$30
14
$20
$10
8
10
$0
Pre-paid Targeted Migration
Improved Improved Enterprise
revenue1 feature management3 acquisition churn5
revenue6
sales2
strategies4
Total
annual
benefit
1. Due to successful launch of pre-paid product, after DiamondCluster analysis showed cannibalization of low -end
postpaid to be 25%, much lower than 40% breakeven. C$10M figure based on value of continuing prepaid offer and
conservative 40% cannibalization assumption.
2. Assuming 5% of feature repriced revenue saved for 10 months per customer, 600,000 features on customer
accounts
3. Assumes 100,000 migrations per month for 12 months. For serial migrants assumes 1,000 people per month causing
C$100 reprice loss per month. Backdating 10% of migrations by 2. 5 months at C$10 reprice per month. Proactively
offering alternatives to 10% of migrations thus reducing reprice by C$7 per months for ten months.
4. Prevented launch of new off -peak clock - value based on assumption that 20% of customers who would be at least
20% better off would have migrated to the new rate plan.
5. Stopped C$0.5M monthly outbound churn effort where the econom of the campaigns was negative.
ics
6. Based on similar usage, migration, and acquisition strategies applied to enterprise segment, and adjusting for relative
percentage of revenue for the base, including the cost of reprice and the benefit of increased account share.
7. Based on estimated 2001 EBITDA of C$534M.
• Phase III: Enterprise
- Strategic roll-out commencing March 2001.
Estimated revenue savings of C$18 million
through targeted feature sales, migration
management and improved acquisition
strategies (based on savings proportional to
consumer segment)
CVM has been extremely effective in generating new revenue streams and
eliminating revenue loss resulting from poorly targeted programs.
Page 15

16. Agenda
Introduction
Background on CVM
CVM Case Studies from Bell Mobility
• Background
• Prepaid
• Postpaid
Engagement Structure
Page 16

17. CVM Case Studies – Prepaid
Overview of Prepaid
Background & Issues
CVM Analysis
Strategy/Results
•
No lifetime profitability model to
determine absolute returns for a
new acquisition campaign
(prepaid/postpaid)
•
Developed simple economic model
of lifetime profits per user, gaining
support for all inputs from relevant
departments
•
Process in place to apply model to
all new acquisition programs,
handover to client completed
•
Limited understanding of relative
lifetime profitability of new adds
and the role of cannibalization
(prepaid/postpaid)
•
Applied model to prepaid and lowend postpaid users, determined
relative profits and breakeven
cannibalization rates
Case study analysis to determine
how actual cannibalization rates
compared to breakeven
•
Gained support for C$5M in
prepaid marketing by showing
actual cannibalization rates close
to 25%, much less than breakeven
rates of 40%+
Total value of segment C$10M per
year, even at high cannibalization
rates
Applied model to each individual
prepaid user, quantifying months
to breakeven and total lifetime
returns
Reviewed scope for prepaid usage
stim, prepaid to postpaid migration
•
•
•
Limited understanding of the
distribution of lifetime profits
across user base, role of value
management
•
•
•
•
Refined strategy to migrate top-end
prepaid users to postpaid, avoiding
expected revenue hit of 12%
Gained support for general usage
stim program
Using CVM tools, we are able to measure lifetime profits for prepaid and
postpaid users, manage cannibalization before prepaid programs were
rolled out, and prioritize prepaid migration and usage stim strategies.
Page 17

18. CVM Case Studies – Prepaid
Overview of Customer Economics
Illustrative
Lifetime
value of $100
Shift in
MoU by 20%
Month 1
Month 2
Month 3
Month 4
Month 5
Breakeven in 5
months
Month 6
Month 7
Month 8
Customer
migrates from $60
plan to $40 plan
Month 9
Customer churns in
month 9
Cumulative customer
EBITDA
Usage charges
Access charges
Cost of acquisition
Cost of maintenance
Key economic factor fixed for existing base
Key economic factor which can be influenced
Customer
Acquisition cost
Our modelling of customer economics is the foundation of our CVM
analysis.
Page 18

19. CVM Case Studies – Prepaid
Economics of Prepaid Subscriber
Customer over Lifetime
$300.00
Present Value
(C$)
Lifetime
value $183
$500
100
$400
(C $)
$200.00
Cumulative
EBITDA
Breakeven in
10.5 months
Lifetime
margin
= 53%
68
$300
68
454
$100.00
EBITDA
per month
35
$200
$100
183
34
31
28
25
22
19
16
13
10
7
4
1
$0.00
$0
Lifetime
Direct Cost
Revenues of acquisition
(without
advertising
overheads)
($100.00)
Commissions on
top-ups
Network
costs
Customer
service
costs /
Bad debt
EBITDA
Notes: Assumes no pre-to-post upsell.
Lifetime revenues based on ARPU of $17.00 / month (includes $50 increase in package price from $99 to $149)
Direct COA costs include: $13 dealer bonus, $6 coop, $40 dealer margin, $10 activation costs, $15 packaging costs, and $16
handset subsidy ($115 phone cost - $99 revenue before $50 package price increase)
Commissions on top-up at 15%. CS costs at $1.25 / month, bad debt at 0.25%. Lifetime churn at 3%, discount rate of 15%.
Using actuals, our model showed that the lifetime value of a new prepaid
user was $183, with a breakeven time of 10.5 months.
Page 19

20. CVM Case Studies – Prepaid
Economics of Low-End Postpaid Subscriber
Customer over Lifetime
Present Value
Lifetime
value $406
$500.00
$400.00
Breakeven in
23 months
$300.00
(C$)
$1,600
$1,400
Cumulative
EBITDA
279
$1,200
(C $)
103
$200.00
EBITDA
per month
$100.00
515
1469
$400
66
61
56
51
46
41
36
31
26
21
16
11
167
6
1
$800
$600
$0.00
($100.00)
Lifetime
margin
= 28%
$1,000
405
$200
($200.00)
$0
($300.00)
($400.00)
Lifetime
Revenues
Direct Cost Residuals
of acquisition
(without
advertising
overheads)
Network
costs
Customer
service
costs /
Billing /
Bad debt
EBITDA
Notes: Assumes no 2nd headset subsidy over customer life.
Lifetime revenues based on $25 access revenue + LD charges (10% of traffic at $20/minute)
Usage at 150 minutes out of 200 min bundle each month
$50 bad credit, Residuals at 7%
Direct COA costs include: $13 dealer bonus, $15 coop, $60 dealer commission, $15 activation costs, $0 packaging costs, and $176
phone subsidy ($295 cost -$119 revenue)
CS costs at $2.50 / month, bad debt at 1.5%. Billing at $0.63 / month.
Lifetime churn at 3%, discount rate of 15%.
While entry level postpaid users have roughly twice the lifetime values of
prepaid users, their breakeven times are also twice as long.
Page 20

21. CVM Case Studies – Prepaid
Cannibalization Break-even
Year 2000 Revenue from New Users
Users
000s
700
100
75
50
25
0
600
500
400
74
43
47
31
With Prepaid Case
56
64
73
20%
30%
40%
50%
Cannibalisation rate without Prepaid Case
Lifetime Revenues for New Users
425
$M
300
600
240
283
325
368
236
200
365
235
With Prepaid Case
0
20%
430
494
559
0
155
With Prepaid Case
36%
471
400
200
100
52% breakeven cannibalisation rate, revenue
$M
Prepaid
Postpaid
30%
40%
50%
20%
30%
40%
50%
Cannibalisation rate without Prepaid Case
Lifetime EBITDA Value of New Users Added
Cannibalization rate
Without Prepaid Case
$M
Notes: 425,000 target prepaid users and 155,000 mobility
postpaid users from year 2000 plan
In year revenues from prepaid= $102/users ($17.00 ARPU x 6 months),
lifetime revenue value $554
In year revenues from postpaid user =$197.40/user
($32.90 ARPU x 6 months), lifetime revenue value $1519
Lifetime value per user: $239 prepaid, $565 mobility postpaid
200
43% breakeven cannibalisation rate, subscriber value
190
102
100
88
136
160
184
208
0
With Prepaid Case
20%
30%
40%
50%
Cannibalisation rate without Prepaid Case
Even at a 40% cannibalization rate, prepaid was a net positive contributor to both
BM’s year 2000 revenue (C$10M per year) and the lifetime EBITDA value from new
users (C$6M per year).
Page 21

22. CVM Case Studies – Prepaid
Existing Base Cannibalization – BM
Daily Gross
Activations
1,000
900
January Average 514 per day
800
Launch of low
end postpaid
plan
February projected 632 average per day
700
26%
GAP
600
500
400
300
February actual average 468 per day
200
100
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
Note: Reporting difficulties resulted in zeros for Jan 6 and 7, those subs added in days following Jan 7.
Feb data through Feb 27.
The early impact of the low end postpaid plan suggested internal BM prepaid
cannibalization of postpaid of around 26%. While substantial, this result represented
the upper limit, given the postpaid advertising campaign and dealer incentive
structures and training.
Page 22

23. CVM Case Studies – Prepaid
Prepaid Customer Distribution
Minutes of Use
Revenue
Avg. ARPU for user group
C$
Avg. MoU for user group
Total Monthly Revenue
C$ M
7
140
300
Cumulative Net ARPU
Cumulative Net MoU
6
120
250
Total Cumulative Revenue
Avg net MoU
5
100
200
4
80
150
Top 25% of
base has an
MoU of 85
100
60
Bottom 25%
has an MoU of
less than 2
50
3
2
1
20
0
0
50
100
150
200
250
300
# of subscribers (‘000s)- sorted by descending Net MoU
Top 18% are
responsible for 70%
of total revenue
40
Top 50% are
responsible for
96% of total
revenue
350
0
0
sub #s
400
0
50
100
150
200
250
300
350
400
# of subscribers (‘000s) - sorted by descending Net ARPU
Note: Net revenue includes all contra elements.
Very few customers represent the majority of prepaid minutes and revenue,
requiring targeted, segment specific action.
Page 23

24. CVM Case Studies – Prepaid
Prepaid Customer Profitability Segments
2,000
280
Lifetime EBITDA per user
Lifetime EBITDA per user
Average MOU per user
1,000
210
140
1,688
65
500
70
25
0
9
Average MOU per user
228
1,500
301
0
0
(251)
(175)
(39)
(500)
(70)
Zero users
Low users
(<20 min)
Medium users (2059 min)
High users
(60-200)
Very high users
(200+)
On average, only High and Very high users have a positive EBITDA...
Page 24

25. CVM Case Studies – Prepaid
Migration of Prepaid Subscriber to Postpaid
Monthly spend (C$)
80
Reprice at MoU of
200 is C$29.50
60
aid
Prep
Revenue gain if
upselling from MoU
of 60 is C$7.00
40
RealTime 150
20
0
0
40
No upsell
too big of a
stretch
% of users
% of minutes
MoU 0-60
87.3%
39.5%
60
80
Upsell
target
120
160
“Upsell” only to avoid churn
MoU 60-80
4.5%
10.6%
200
240
Minutes
of Use
MoU 80 +
8.2%
49.9%
…As a result migrating high users to postpaid is expensive, representing
an average reprice of 36% for users over 80 MoU.
Page 25

26. CVM Case Studies – Prepaid
Value Drivers – Days of Use
Key MoU driver: number of days of use
# of
accounts
Key MoU driver: usage per day
# of accounts
1
2
3
4
5
6
7
8
9
MOU per
day
Daily MoU
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
Days of use
For low to medium prepaid users, MoU / day is surprisingly constant. The
main driver of usage is the number of days the phone was used. For high
users, MoU / day is the main revenue driver.
Page 26

27. CVM Case Studies – Prepaid
Targeted Usage Stim – Off-Peak
Before After hours feature
•
•
•
Daily
MOU
Index
After After hours feature
Phone used 17% of days
Daily ARPU $0.17
Daily MoU 0.45
•
•
•
Day proxy dMoU
-17
-14
-11
-8
-5
-2
1
4
7
10
13
16
19
22
25
Free proxy dMoU
28
31
34
37
Phone used 32% of days
Daily ARPU$0.39 (excluding $25
subscription fee)
Daily MoU 2.29
Nights proxy dMoU
40
43
46
49
52
55
58
61
64
67
70
Day Relative to Take-up (low users)
A usage stim initiative targeted to the prepaid segment showed that low
users could be drastically stimulated with an off-peak offer.
Page 27

28. Agenda
Introduction
Background on CVM
CVM Case Studies from Bell Mobility
• Background
• Prepaid
• Postpaid
Engagement Structure
Page 28

29. CVM Case Studies – Postpaid
Overview of Postpaid
Background & Issues
Data Sources
• Existing data sources are aggregates.
Most requests are not lifecycle based
Low usage
• MoU is low compared to industry
average and drives revenue negative
events.
• Commissions paid on usage features
no matter what pre-existing usage
streams were
Declining ARPU/Migrations
• Downward migrations accounted for
52% of lost access revenue (48% loss
from churn)
• Upward migrations accounted for
37% of gain in access revenue gain
(63% from new acquisitions)
Churn
• Relatively low churn rates (1.5%)
• Most resources devoted to outbound
retention campaigns
Test Environment
• Lack of clean, controlled
environment makes product
development slower, riskier, and
lower impact
• No proper understanding of offer
value vs. return
CVM Analysis
Strategy/Results
•
Crated new transaction level (CDR) data
sources, linked them with existing
profile data bases
•
Reduced time to track impact of
initiatives, greatly increased targeting
precision
•
Analyze psychology effects of
alternative stim offers and effects of
training on multiple usage streams
•
Implement targeted offers based on
observed stim in trial offers
•
Reviewed profitability of feature sales,
targeted accordingly
•
Reprice reduced on feature sales by
C$8M per year
•
Calculate revenue gain from alternative
offers that replace downward
migrations
Analyze migration impact resulting from
new acquisition offers
•
Generate recommendations for CSRs to
avoid downward migrations where
possible. Savings of C$14M per year
Revise outbound acquisition strategies,
avoided reprice of C$42M per year
•
•
Enhance churn prediction model
Calculate relative returns from
outbound retention campaigns based
on model predictions and inbound save
offers
•
•
Shift resources to inbound save efforts
Saving of C$6 million per annum
•
Created cross functional team to launch
and support small scale initiatives very
quickly across all inbound and
outbound channels
•
Executed 8 campaigns in short time
frame
Trained customer management
resources on product development
cycle, including feedback from CS and
tracking results.
•
•
•
CVM activities in the postpaid segment focused on stimming low MoU customers,
managing upward and downward migrations, improving customer retention and
creation of ongoing test environment.
Page 29

30. CVM Case Studies – Postpaid
Mobile Industry Data Source Comparison
Traditional Data
Warehouse
CVM Datamart
• Aggregated over time
• Processed / billed data
• Individual call records, account
profile changes
• Highest possible level of
granularity
Frequency of
update
• Weekly, monthly
• Delayed by bill cycle
• Shifted across users depending
on bill cycle
• Twice a day
• Date is absolute (not shifted in
time) across users
Ease of
support
and use
• Accessible by any user through
a simplified graphical user
interface
• Limited flexibility in creating
new variables
• Mostly used for reporting
• 10+ times more data
• Used by technically and statistically
more advanced analysts
• Very flexible
• Mostly used for strategy definition
Data Level
To achieve the full potential CVM in the mobile telecoms market, near real
time datasets at the individual transaction level need to be constructed and
maintained.
Page 30

31. CVM Case Studies – Postpaid
Key Components of CVM Datamart
Usage and
Bill Data
• Individual call records
• No delay (up to 1 day)
• Roaming usually not
included
• Prerated CDR (includes call
type definitions, distance)
Account
Change Data
• Individual account / user
profile transactions
- Activation
- Deactivation
- Migration between RPs,
features
• Creates near real time
customer profile and
historical profile by day
Historical
Data
• Usually available from DWH
• Up to 24-48 months of
observations
• Bill (usage & revenue)
aggregates
• Profile (Activation, rate plan,
features
activation/deactivation)
• Information is delayed but
100% accurate and rich in
history
Lifecycle View
Cluster has developed for its clients a CVM Datamart, which incorporates
all customer transactions in a near real time format.
Page 31

32. CVM Case Studies – Postpaid
Data Foundation
Real-time Datamart
Needs
Real-time
Datamart
System Architecture
Switches
1
Real-time usage
variables (for
usage database)
Postpaid
User
Profile
Change
Assign
User Info
Customer
Service
Prepaid
2
Each account
transaction (for
profile database)
Voicemail
Split M2M/
Remove
Duplicate
Pre-rating
Daily
Activity
Log
1
2
Browser
Feeds
captured twice
daily before
billing
Billing
3
User information
for entire lifecycle
Roaming
Data warehouse
(monthly
summary of bill
cycles)
3
• Usage
database
- 3-6 months of
CDRs
- 6-12 month
of daily
aggregates
• Profile
database
- 12 months of
real-time
profile
• Other data as
needed
- Irate calls to
CS
- External
agency data
(demographics)
Update once
per month
DiamondCluster initially constructed the CVM datamart as proof of concept, then
productionalized it later. Our CVM analysis also relied on historical data from bill line
item based datawarehouse.
Page 32

33. CVM Case Studies – Postpaid
Existing Base Value Drivers
• High breakage users
have high churn rates
• Usage declines
prior to churn
USAGE
• Usage trends precede
migration both upward and
downward
• Out of bundle
revenues
• LD
• Roaming
EXISTING
CUSTOMER VALUE
CHURN
MIGRATION
• Total value loss
• Partial value loss
As a result of our modelling of customer economics, we have centered our
CVM analyses on usage, rate plan and feature migration, and churn.
Page 33

34. CVM Case Studies – Postpaid
Usage as a Predictor of Migration and Churn
Usage before Migration
Usage before Churn
MoU Index (100)
130
100%
Migrations Up
Migrations Down
27%
120
75%
48%
48%
55%
55%
72%
110
50%
73%
100
25%
52%
52%
45%
45%
28%
90
0%
80
Months prior
to migration
Rate
group 1
Months after
migration
Rate
group 2
Rate
group 3
Rate
group 4
Rate
group 5
Rate
group 6
70
Usage drop in month 1 - 6 prior to churn
60
Usage in month 1-6 prior to churn compared to
month 7-12 prior
Month of
Migration
Notes: 100%is the average usage through month 7 - 12 prior to churn.
Usage changes precede customer transitions. As observed at client, migrants up
have usage stim of 13%, migrants down usage loss of 10%, and churners usage loss
averaging 50% in the 6 months prior to status change.
Page 34

35. CVM Case Studies – Postpaid
Value Drivers – Modes of Use
Theory
No Association
80
60
40
20
0
1 min toll to 1.8 min non-toll
-
3
6
120
100
80
60
40
20
0
150 DIGITAL
Non-Toll
Non-Toll
Long
Distance
9 12 15 18 21 24 27 30 33 36 39
No Association
1 min toll to 1.7 min non-toll
-
3
Toll Minutes
80
1 min incoming to 1.2 min outgoing
40
20
1 min incoming to 3.2 min outgoing
Outgoing
Incoming
80
60
40
1 min incoming to 3.6 min outgoing
20
0
0
-
3
6
9
12
15 18
21
24
27
-
Incoming Minutes
80
70
60
50
40
30
20
10
0
2 4 6 8 10 12 14 16 18 20 22 24 26 28
Incoming Minutes
1 min off-peak to 0.7 min peak
100
80
1 min off-peak to 0.8 min peak
1 min off-peak to 3.3 min peak
Peak
Off-Peak
9 12 15 18 21 24 27 30 33 36 39
1 min incoming to 1 min outgoing
100
60
6
Toll Minutes
100
Outgoing
• Shift consumer
psychology in two
phases
- Deeply discount
usage features to
encourage new
modes of use
- Customer gets in
habit of making
more calls, break
association of
expense with
each call
120
100
Peak
• Psychology is main
hurdle to usage/
revenue stim
- Mobile for safety
only
- Price perception
vs. actual price
Observations
150 ANALOG
60
40
1 min off-peak to 3.6 min peak
20
0
-
2
4 6
8 10 12 14 16 18 20 22 24 26 28
Off-Peak Minutes
-
2
4 6 8 10 12 14 16 18 20 22 24 26 28
Off-Peak Minutes
Changing the number of modes of use dramatically increases total usage, as
customers begin to think of their mobile like their home phone.
Page 35

36. CVM Case Studies – Postpaid
Value Drivers – Mobile Browser Usage
Low freq., 1-2 weeks
Med freq., 3-5 weeks
High freq., 6-10 weeks
Browser MoU
Before they started using the
browser, high frequency
users had declining MoU.
After using the browser, they
had the highest MoU stim.
MoU/User
40
32
9
350
307 312
3
3
267
323
0.2
338
0.1
272
284
255 261
239
3
319
330
277
26
363
268
239
227
-3
-2
-1
1
2
3
Relative Month
Notes: User base: 473 browser users started to use the browser in June - July cycles and who did not have ESN# change or migration within ±3 months
from the time when first used the browser and has more than one browser call. MoU adjusted for seasonality. User base for seasonality indexes
users who activated before Nov. 1999 and were active as of Sept. 2000, did not have and ESN change and did not activate the browser.
All users who started using the mobile browser experienced voice stim in addition to
the other, direct benefits. Furthermore this voice stim has proved to be stickier than the
data minutes themselves for all data users.
Page 36

37. CVM Case Studies – Postpaid
Campaign Targeting
Percentage of Users
20%
18.4%
19.2%
25.0%
12.1%
25.3%
15%
10%
5%
0%
0
Usage Pattern
10
20
30
40
50
60
70
80
90
100 110 120
Bundle Utilization Percentage
130
140
150
160
170
180
190 200+
High breakage
Low breakage
Action
Sell subsidized/free usage
features
Sell full price/discounted VAS
features
Offer stretch features,
other VAS (2)
Upward migrate/Offer stretch features, other
VAS
Priority
High
Medium
Low
Low
Expected Benefit
• Reduced churn and
downward migration
• No risk of reprice
• Some LD stim
• Out of bundle usage
revenue
• Some LD stim
Overage
• Keep out of bundle
usage revenue
• Reduce churn of high value users
• Keep out of bundle usage revenue
• Secure higher access fee
All campaigns have been carefully targeted on customer behavior, such as bundle
utilization, to maximize effectiveness while avoiding reprice. Estimated in year
EBITDA savings of C$8M per year.
Page 37

38. CVM Case Studies – Postpaid
Migration Importance – Value Compared to Activation/
Deactivation
Downward Migration vs. Deactivation
Access Value
Number of
Users
Migrations Down
Churn
$-622,985
$-581,034
Upward Migration vs. Activation
Number % of Total Value
Change
44,600
52%
20,001
48%
Access Value
Number of
Users
Migrations Up
New Users
$987,384
$1,707,074
Number % of Total Value
Change
35,732
37%
72,991
63%
45,000
35,000
Churn
40,000
Migrations
30,000
35,000
25,000
New Users
Migrations
30,000
20,000
25,000
15,000
20,000
15,000
10,000
10,000
5,000
5,000
0
0
0
-10
-20
-30
-40
-50
-100
0
<-100
Drop in Access Charges
Notes:
10
20
30
40
50
100
<100
Increase in Access Charges
Data taken from CLUSTER migration model, based on May usage and access revenues.
Includes prepaid rate plans.
Migration direction defined by an increase/decrease in access revenue after the migration.
Migration activity is a large value driver previously untracked. It represents
52% of all gains and 37% of all losses in access revenues.
Page 38

39. CVM Case Studies – Postpaid
Migration Addressability – Complexity of Combinations
Ranking According to
Number of Migration Events
38 rate plan
combinations
represent
80% of
migrations
39% of expected
revenue impact
120%
Ranking According to
Revenue Impact
Last 1,234 rate group combinations
contribute 24% of revenue impact,
but are too small to analyze (less
than 20 migrants per month
120%
Must examine 186 rate plan
combinations to include
80% of migrations and 80% of
revenue impact
100%
80%
% of Total Revenue Change
% of Total Migrations
100%
1,272 Total
Combinations
for February
60%
12 rate plan combinations
represent
61% of migrations
12% of revenue impact
40%
5 rate plan combinations
represent
42% of migrations
9% of revenue impact
20%
1,272 Total
Combinations
for February
80%
60%
26 rate plan
combinations represent
41% of migrations and
40% of revenue impact
40%
7 rate plan combinations
represent
17% of migrations and
20% of revenue impact
20%
0%
0%
0
Note:
38 rate plan
combinations
represent
48% of migrations
and 47% of revenue
impact
500
1000
1500
Rate Group Combinations
Sorted by Number of Migration Events
0
500
1000
1500
Rate Group Combinations
Sorted by Revenue Impact
Expected revenue combination based on differences on average ARPU per plan.
While total migration activity is complex the distribution of effects is highly skewed.
Approximately 3% of migration combinations could provide 80% of the migration
events or 39% of total access revenue created and lost.
Page 39

40. CVM Case Studies – Postpaid
Migration Example: Policy Recommendations
Description Key Segment Affected
Outbound
Inbound
Prevent Certain
Migrations
•
Impose fees or future date all downward
migrations to prevent abuse through multiple
migrations
•
Do not call
•
•
CS policy
Systems issues on validity of
future dated transactions
Substitute
Certain
Migrations
•
Instead of allowing customers to downward
migrate, give them a free feature and secure
the higher access fee
Example: instead of 400 to 200, 400 to 400 with
free feature
•
Do not call
•
Recommendation engine for
targeting
Systems issues: free feature
— Rate Package lock
Recommend customers a rate plan which is
more beneficial to the company and to
customer
Example: move customers from old rate
package to new rate package
•
Instead of contacting customers individually —
slow and expensive — move them to a new rate
plan automatically
Flashcut those users on Flex with long term
average less than 50
•
Changing the migration policy would cause too
high churn risk
Example: Digital North America / Real Time
Canada where migration reprice is significant,
but churn risk is even higher
•
•
•
Shift Customers
to Certain RPs
•
•
Flashcut
•
•
Leave Intact
•
•
•
Target certain outbound
migrations based on
feature sales
•
Only accomplished
where in year revenue
constraints are met
Recommendation engine
for finding ideal plan or
targeting
•
Recommendation engine for
targeting and offer design
Do not call
•
Fulfill Requests
•
Recommendation engine for
targeting and offer design
Stretch features for upsell
None of these tactics are universally applicable, but on a targeted basis
they can address the majority of migration reprice, saving C$14M per year.
Page 40

41. CVM Case Studies – Postpaid
Acquisition Reprice
• Final recommendation was to match
only on certain rate plans, limiting
reprice
• Result was an expected savings of
$26M annual EBITDA
$50
$40.28
$25
Reprice ($M)
$15
$10
$5
$0
($5)
($10)
($15)
($20)
($25)
($30)
80
60
40
20
0
-20
-40
-80
• By analyzing actual reprice on the
existing base, calculated that it would
require a 3% increase in market share
to compensate for the expected reprice
-100
• Initial reaction was to match competitor
clock across entire base
16%
14%
12%
10%
8%
6%
4%
2%
0%
In year rev. impact
(reprice) $M)
• Competitor changed off-peak clock,
beginning off-peak at 6 PM instead of
8 PM
% of Subs
In year revenue reprice (annual)
%
-60
Background
% better offer
new plan
(assuming 20% of users with
10% or more better off switch)
$11.94
$12.91
$16.40
$0
Original idea
matching
across the
base
Alternative A
(match on OP
plans)
Alternative B Alternative C
(match on OP (match on OP,
+ RT plans) RT and flashed
old)
By analyzing the expected reprice using CDRs, saved Bell Mobility an
expected $26M from avoided reprice.
Page 41

42. CVM Case Studies – Postpaid
Churn — Difficulty with Outbound Campaigns
Deactivation Impact
Deactivation
rate
6%
5%
ARPU Impact
During the period between pull and mailing 13%
of both the target and control group deactivated
implying late action on save attempt
5.5%
ARPU
$155 $153
Target
Control
$150
5.2%
3.7%
4%
3.6%
3.6%
3.4%
$152
3.2%
1.9%
2%
1.7%
1%
3.4%
$146
$143
$145
3.2%
$144
2.9%
3%
Target
Control
3.5%
2.6%
$140
2.5%
$130
0%
$137
$142
$141
$142
$137
$135
Campaign launch
$142
$140
$140
3.0%
Peak in
deactivation
rate 2 months
prior to
campaign
suggests
outdated data
$143
$141
Reduction in ARPU
indicates that high
value users churned
at higher rate.
$135
Campaign launch
$125
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
The targeting difficulties on outbound churn campaigns have driven poor
actual results, contrary to carrier’s previous perception.
Page 42

43. CVM Case Studies – Postpaid
Churn — Outbound Loyalty Funnel
Illustrative
Existing postpaid
consumer base
(1.0M users)
Targeted users based on
predictive churn model
score calls (96,000 users
per month)
Nonchurners
910,000
93% of users taking up
the offer, however, are
non-churners over
next six months
Contacted users
taking up offer
(25,920 users)
70,080
18,921
Users remaining on
network after 6 months
(21,566 users)
18,921
1089
1,400
Churners
or potential
churners
over next
six months
6,999
25,920
90,000
Targeting Process
Contact Offer Process
• Predictive churn model
• Call center support
~100,000 users/month
• 1.5% monthly churn in base
• 4.5% monthly churn in list
• RPC rate of ~30%
• Uptake rate of ~90%
• Assumes equal RPC and Uptake
for churners and non-churners
5,599
Realized Save Rate
Customers who
churn despite
loyalty offer
• Save rate of 20% for churners
In almost all outbound loyalty programs, the majority of users taking up a
retention offer are not actually churners, limiting total returns.
Page 43

44. CVM Case Studies – Postpaid
Churn — Channel Economics
Outbound
Illustrative
Inbound retention
Winback
Assumptions: C$45 ARPU, saved users remain on network for 12 months, C$5.00 per contact outbound, C$4.00 inbound
• Targeting:
- 27% churners over 6 months
in lists
• Offer Uptake Rate:
- 90%
• Save Rate:
- 20%
• Cost of Contact:
- $6.70 per contact
• Targeting:
- 60% of callers are churners
• Offer Uptake and Save Rate:
- 100% for non-churners
- 25% for churners
• Cost of Contact:
- $0.00 per contact
• Targeting:
- 100% of those called are
churners
• Offer Uptake and Save Rate:
- 5%
• Cost of Contact:
- $6.70 per contact
• Give away revenue
breakeven: $2.31,
5% of ARPU
• Give away revenue
breakeven: $12.50,
28% of ARPU
• Give away revenue
breakeven: $33.50,
74% of ARPU
Move away from migration
offers to feature offers
Room to enrich offers
depending on results
Increase investment
depending on observed
results for targeted winback
segments
Notes: Monthly revenue saved is multiplied by 9 months (since churners would leave in an average of 3 months); Give away cost lasts for 12
months, 3 months for churners who accept the offer.
Inbound and winback efforts, however, can show substantialy higher returns due to
their inherent targeting benefits. By shifting resources to the inbound channel, we
improved in year EBITDA by C$6M.
Page 44

45. CVM Case Studies – Postpaid
Test Environment — Description
Definition:
Launch inbound and outbound campaigns on a small scale in a clean, single offer environment with
precisely controlled execution across multiple channels, using CDR level data for rapid return tracking
for each variation tested.
Typical Process
• Due to large scale approval and
production process is lengthy
• Reading results from bills delays
campaign performance evaluation by 2
- 3 months
• Easy to hit extremes of either rich offer
with high risk of reprice, or less
attractive offer with high marketing
cost per take-up
• Usually not at all or not properly
measured.
• Lack of hypothesis testing at offer
design usually results in neutral or
negative return
•
•
•
•
Overlapping campaigns
Improperly defined control groups
Improper return calculation
Limited feedback from tracking or CS
into new offer hypotheses
Area of Impact
Time to Market
Risk of Reprice
Expected Returns
Customer Management
Process
Test Environment
• Due to small scale and cross functional
team offers are launched very quickly
• Due to single offer environment and access
to CDR level data results are available in 2 3 weeks
• As a result of the small scale and the
testing of various offers the reprice risk is
limited and is known in advance
• Hypotheses driven design improves returns
• Correctly measured returns are available
very quickly
• Sensitivity and elasticity information is also
available
• Complete cycle of hypothesis generation,
testing, tracking, feedback prior to broad
based launch
• Knowledge handover from DiamondCluster through on the job training
The test environment is operated by a cross-functional team to ensure that test
initiatives can be launched on a small scale with short turn around and proper return
tracking.
Page 45

46. CVM Case Studies – Postpaid
Test Environment — Benefits
All Departments Realized Immediate Benefits...
• Marketing benefits from increased creativity and stronger business cases in low risk environment
• Finance benefits from selecting only the most profitable campaigns from those tested, and avoiding any netnegative campaigns
• Database marketing benefits from easier environment to track results
• Customer care benefits from fewer marketing initiatives for non-test customer care advocates, and an
opportunity to provide feedback on what works and what does not
…and in the Long Term, the Product Development Process Flow Was Improved
GENERATE HYPOTHESES
• Develop detailed hypotheses on
how specific products offered
through specific channels to
targeted subscriber groups will
impact profitability
- How channel of communication
affects take-up rate
- How certain offers impact postcampaign behavior (churn,
migration, usage)
TEST HYPOTHESES
• Design specific test to confirm
initial hypotheses
- Vary offer and channel as needed
to gain significant results
- Establish a control group of
statistically significant size, and
isolate target and control group
from all other campaigns
ANALYZE RESULTS
• Track churn, migration, and usage
impacts to determine overall
impact on profitability
By creating a test environment, DiamondCluster built a testing mentality
within the organization which improved the product development process.
Page 46

47. CVM Case Studies – Postpaid
Test Environment — Improved Targeting
Daily Tracking
Take-Up Date:
April 20, 2000
Weekly Tracking
342 users taking Afterhours at Free
342 users taking Afterhours at Free
Seonds / user / day (indexed based on
before avg.)
700
seconds / user / day
600
500
400
300
200
100
peak
300
250
evening
peak
200
weekend
150
100
50
0
0
1
2
3
4
5
6
Week Relative to Take-Up
7
8
9
weekend
Weekend
183%
Evening
Date relative to take-up date
evening
350
-3 -2 -1
66
60
54
48
42
36
24
30
18
6
12
0
-6
-1
2
-2
4
-18
0
400
168%
Peak
Notes: Graph shows daily variation of 342 users who took AH free
All users shifted to same relative take-up day (day zero)
Graph shows usage in terms of seconds
-4%
Notes: Graph shows daily variation of 342 users who took AH free
All users shifted to same relative take-up day (day zero)
Graph shows usage indexed to before avg. (i.e. avg. of weeks -3 to -1 = 100)
In this example, a tested free off-peak product, targeted at high breakage
users, led to weekly usage stim of greater than 100% with no reprice.
Page 47

48. CVM Case Studies – Postpaid
Test Environment — Tracking Results
Usage
Churn
% Stim
Migration
% Churn
% Migration
Downsell
Control Down
Upsell
Control Up
Attempts
30%
Attempts
Control
2.0%
Control
8.0%
7.0%
22%
6.0%
20%
5.0%
1.0%
4.0%
3.0%
10%
2.0%
2%
0%
1.0%
0.0%
Hardware Upgrade
#
Notes:
439
(62 take-up)
3008
0.0%
Hardware Upgrade
#
439
3008
Hardware Upgrade
#
SAME AS CHURN
Usage stim is avg. of 29 days after take-up compared to 29 days before take-up. Includes all usage. Both churn and migration compare
all attempted contacts to a control group. Migration chart includes migration events past the CS induced migration.
In order to establish complete and accurate metrics, tracking
incorporates usage, churn, and migration impacts.
Page 48

49. Agenda
Introduction
Background on CVM
CVM Case Studies from Bell Mobility
• Background
• Prepaid
• Postpaid
Engagement Structure
Page 49

50. Engagement Structure
CVM Project Phasing and Resources
Project Scope/Deliverables:
• Review and analyze sample client data feeds
• Illustrate key existing base trends based on sample
• Provide detailed assessment of time/budget to build
productionalized Cluster analysis tool, provide ongoing
base analysis and marketing support
• Relevant examples of analysis tool output from other
projects
Phase 1A
Initial
Diagnostic
&
Phase 1B
Testing
3
Month
Engagement
Phase 2:
Proof of
Concept for
Tool
Project Scope/Deliverables:
• Develop analysis engine using client real
time feeds
• Use engine to create new finance
revenue/profitability reports
• Customer analysis to understand user
behavior, micro offer opportunities with
expected benefits for implementation
• Test offer implementation
• Productionalized analysis engine
Phase 3:
Implementation
Project Scope/Deliverables:
• Integrate Cluster analysis engine with
campaign management/tracking tools, rules
based recommendation engine
• Implement series of targeted offers
previously identified
• Track results and refine offers
• Provide detailed financial reports on value
created
Resources:
• Approximately 4-6 persons (DCI)
• Approximately 2 client
resources from
department/division under study
Resources:
• 4-6 persons (DCI)
• Approximately 4 client team mambers from
marketing, sales, finance, IT and CS
3-5
Month
Engagement
Resources:
• 4-6 persons
•
6+ fully dedicated internal resources
from marketing, sales, finance, IT and
CS
6-12
Month
Engagement
A pilot consists of 3 months to construct an initial diagnostic and testing.
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51. Engagement Structure
Project Team Structure
Project Design/Management Office
• Support
• Management
• Management
• Design
• Coordination
• Education
Work Steps
IT
Data Feeds/
Construction of Variables
IT/CS/Systems
Functions Using Variables/Reports
Finance
Offer Design/Implementation
CS/Systems
Tracking
• Data modelling
Other Functions/
Departments
Infrastructure
Marketing Team
CS/Finance
• Database marketing
• Customer loyalty
• Turnover prevention
• Other functions
Internal
project
dependencies
Feedback and
improvement
loops
CVM can only be successful through cross-department planning and collaboration,
with marketing in the coordinating role.
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