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Introduction to Customer Value Management
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CVM Introduction
Eric SmithJuly 12, 2001
Page 2
Introduction
Background on CVM
CVM Case Studies from Bell Mobility• Background• Prepaid• Postpaid
Engagement Structure
Agenda
Page 3
IntroductionSession 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
– 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
The two parts – lecture and case based – will ensure that both the tools and the examples of CVM are introduced.
Lecture based
Case workshop based
Page 4
• 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
IntroductionWhat is Customer Value Management (CVM)?
Page 5
IntroductionWhat is the difference between CVM and CRM?
Required Understanding• Customer needs and
expectations; channel usage• Customer migration patterns;
reasons for churn; value drivers
Lever for Change • Customer touchpoints• Product offers
Approach • Integrated, comprehensive• Hypothesis, data-driven
Attrition… • …should be reduced• …is acceptable for low value
customers
Customer Service• Streamline and improve
processes• Direct customer to most
profitable offerings
Metric • Customer retention• Customer profitability
Customer Expectations • Exceed on customer service• Exceed on customer value
Capabilities• Channel integration to offer a
consistent experience; “know the customer”
• Capture detailed customer data; ability to deliver micro-targeted offers
Focus• Retain customers by improving
customer interactions• Improve profitability by
delivering targeted offers
Customer RELATIONSHIP ManagementCustomer VALUE Management
CVM is focusing on creating profitable customer relationship.
Page 6
Introduction
Background on CVM
CVM Case Studies from Bell Mobility• Background• Prepaid• Postpaid
Engagement Structure
Agenda
Page 7
• 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:
Background on CVMDevelopment of CVM
Sprint PCS
Bell Mobility
TIM
Telesp
CW Optus
Telecom New Zealand
BellCanada
Page 8
The result of a successful CVM approach is the shifting out of the consumer demand curve and the capturing of consumer surplus.
Rate Plan 1Existing
Base Focus
Rate Plan 2Existing
Base Focus
Rate Plan 3Existing
Base Focus
Broad Rate Plan
- New Users
Price
Quantity
Price
Quantity
MarketDemand Curve Market
Demand Curve
Micro-Offers to Existing Base
Consumer demand curve shifts out with
tailored products
Additional potential revenue/ consumer surplus created by micro-offersUncaptured consumer surplusCarrier revenue
Uncaptured consumer surplusCarrier revenue
Before CVM approach After CVM Approach
New
Old
Background on CVMWhat is the Economic Foundation of CVM?
Page 9
Customer Economics
Customer Behavior
Offer Design
Results Tracking/Improvement Cycle
• Understand drivers of individual user profitability, profits by segment
• 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
• Quantification of impact, incorporate results into future offer design
FINANCIAL RESULTSMeasurable financial impact such as
usage stim for low users, prevented migration reprice, prevented churn
DATAall data at individual transaction level:
call data records from switch, daily account adjustments and transactions, daily account profile updates
Through micro-targeted offers, DiamondCluster has used subscriber-level data to create real financial results, in usage, migration, and churn.
Background on CVMHow Do We Approach CVM?
Page 10
5467
83
101
126
153
177
102
140
5467
8698
106114
120
5467
86
116129
0
20
40
60
80
100
120
140
160
180
200
1997
1998
1999
2000
*20
01*
2002
*20
03*
Merril LynchBear StearnsStrategis
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.
• VAS Services• Roaming inclusive
plans• Text messaging
services• WAP, browser
services• Location based
services• 3G services
# of subscribers
Product LaunchesPrice DeclinesIndustry Growth
43%
52%
66%
Year
Price / minute ($)Penetration
Source: Merril Lynch.(*) forecasted.
0.10
0.450.48
0.54
0.45
0.330.33
0.37
0.43
0.190.210.210.23
0.160.160.2
0.09
0.160.12 0.12
0.0
0.1
0.2
0.3
0.4
0.5
0.6
Apr. 27,1998
Feb. 15,1999
Aug. 9,1999
Apr. 17,2000
60 mins. 100 mins. 250 mins.
500 mins. 1,000 mins.
Source: Wireless week, Washington D.C.
Background on CVMMobile Markets in the US
Page 11
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.
Airlines
Mobilecarriers
Financialservices
Low High
High
Low
Transactions per User
Po
ten
tial
Co
mp
lexi
ty o
f C
VM
Off
ers
Traditional retail:movies, clothing,music, books, etc
Long distance operators
• 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
• No separation of purchaser and consumer
• High potential transferability
• Large range of products• Competition per item, low
switching costs
• Limited separation of purchaser and consumer
• Some competition by call with override codes
• Low switching cost
• 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
Background on CVMRelative CVM Complexity for Mobile Operators
Page 12
Introduction
Background on CVM
CVM Case Studies from Bell Mobility• Background• Prepaid• Postpaid
Engagement Structure
Agenda
Page 13
CVM Case StudiesBell Mobility Overview
EOP Subscribers Revenue
Bell Mobility is the incumbent wireless carrier in Ontario and Quebec, with C$1.4B revenue and 2.8 M subscribers.
Market Share (Subs) MoU
857.0
1036.6
1221.0 1349.0 1335.4 1454.91825.5
126.3 509.1
97 98 99 00 01
Prepaid
Postpaid
000s subs
863 929 981 1,1341,394
97 98 99 00 01
32.2% 30.1% 28.4% 27.2% 27.1%
97 98 99 00 01
%
C$M
44
165186 195
221
95
37 42
98 99 00 01
Postpaid
Prepaid
Minutes per month
Due to platform error, incoming minutes are
not billed for
Notes: All 2001 figures are estimates. Source: company publications.
Total subs growing at 20-25% p.a.Prepaid share stable at 40%
Market share stabilizing after entry of two, digital only
competitors
Revenues growing at 7-22% p.a.
Postpaid MoUincreasing at
5-13% p.a.
Page 14
CVM Case Studies – BackgroundOverview of CVM Phases at Bell Mobility
Bell Mobility CVM Approach
Evaluation of the competitive positioning of Bell Mobility led to prioritization of CVM initiatives.
• Market growth focused in pre-paid segment (BM had no presence, competitor launched prepaid product)
• Low churn rates (1.5% per month) compared to industry average
• 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
• Sophisticated, third generation data warehouse prior to DCI presence, but no CDR level data and minimal tracking of campaign effectiveness
• 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
• 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
• 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
Page 15
CVM Case Studies – BackgroundBenefits of CVM at Bell Mobility
Impact of Successive CVM Phases
CVM has been extremely effective in generating new revenue streams and eliminating revenue loss resulting from poorly targeted programs.
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)
• 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
• Phase III: Enterprise- Strategic roll-out commencing March 2001.
Estimated revenue savings of C$18 million through targeted feature sales, migration management and improved acquisition strategies (based on savings proportional to consumer segment)
Annual EBITDA impact (C$, million)
18% improvement of annual EBITDA7
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 economics of the campaigns was negative.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.
18
6
108
4298
14
$0
$10
$20
$30
$40
$50
$60
$70
$80
$90
$100
Targeted feature sales2
Migration management3
Improved churn5
Improved acquisition strategies4
Total annual benefit
Pre-paid revenue1
Enterprise revenue6
Page 16
Introduction
Background on CVM
CVM Case Studies from Bell Mobility• Background• Prepaid• Postpaid
Engagement Structure
Agenda
Page 17
CVM Case Studies – PrepaidOverview of Prepaid
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.
Background & Issues CVM Analysis
• 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
Strategy/Results
• Limited understanding of relative lifetime profitability of new adds and the role of cannibalization (prepaid/postpaid)
• Applied model to prepaid and low-end 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
• Limited understanding of the distribution of lifetime profits across user base, role of value management
• 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
• Refined strategy to migrate top-end prepaid users to postpaid, avoiding expected revenue hit of 12%
• Gained support for general usage stim program
Page 18
CustomerAcquisition cost
Shift inMoU by 20%
Lifetime value of $100
Month 1 Month 3 Month 4 Month 6 Month 7 Month 8 Month 9Month 2
Cumulative customer EBITDA
Breakeven in 5 months
Month 5
Customermigrates from $60
plan to $40 plan
Usage chargesAccess chargesCost of acquisition
Key economic factor fixed for existing baseKey economic factor which can be influenced
Cost of maintenance
Our modelling of customer economics is the foundation of our CVManalysis.
CVM Case Studies – PrepaidOverview of Customer Economics
Customer churns in month 9
Illustrative
Page 19
Customer over Lifetime Present Value
($100.00)
$0.00
$100.00
$200.00
$300.00
1 4 7 10 13 16 19 22 25 28 31 34
Cumulative EBITDA
Breakeven in10.5 months
(C $
)
Lifetime value $183
EBITDA per month
(C$)
183
68454
100
35
68
$0
$100
$200
$300
$400
$500
LifetimeRevenues
Direct Cost of acquisition
(without advertising overheads)
Commis-sions ontop-ups
Networkcosts
Customer service costs /
Bad debt
EBITDA
Lifetime margin = 53%
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 $16handset 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%.
CVM Case Studies – PrepaidEconomics of Prepaid Subscriber
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 20
($400.00)
($300.00)
($200.00)
($100.00)
$0.00
$100.00
$200.00
$300.00
$400.00
$500.00
1 6 11 16 21 26 31 36 41 46 51 56 61 66
Cumulative EBITDA
Breakeven in23 months
(C $
)
Lifetime value $406
EBITDA per month
(C$)
405
279
515
167
1469
103
$0
$200
$400
$600
$800
$1,000
$1,200
$1,400
$1,600
LifetimeRevenues
Direct Cost of acquisition
(without advertising overheads)
Residuals Networkcosts
Customer service costs /
Billing / Bad debt
EBITDA
Lifetime margin = 28%
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%.
CVM Case Studies – Prepaid Economics of Low-End Postpaid Subscriber
While entry level postpaid users have roughly twice the lifetime values of prepaid users, their breakeven times are also twice as long.
Customer over Lifetime Present Value
Page 21
Users Year 2000 Revenue from New Users
Notes: 425,000 target prepaid users and 155,000 mobility postpaid users from year 2000 planIn year revenues from prepaid= $102/users ($17.00 ARPU x 6 months), lifetime revenue value $554In year revenues from postpaid user =$197.40/user ($32.90 ARPU x 6 months), lifetime revenue value $1519Lifetime value per user: $239 prepaid, $565 mobility postpaid
Lifetime EBITDA Value of New Users Added
88 136 160 184 208102
0
100
200
20% 30% 40% 50%With Prepaid Case Cannibalisation rate without Prepaid Case
$M190
43% breakeven cannibalisation rate, subscriber value
31 47 56 64 7343
0255075
100
20% 30% 40% 50%With Prepaid Case Cannibalisation rate without Prepaid Case
$M74
52% breakeven cannibalisation rate, revenue
Lifetime Revenues for New Users
235 365 430 494 559236
0
200
400
600
20% 30% 40% 50%With Prepaid Case Cannibalisation rate without Prepaid Case
$M471 36%
CVM Case Studies – PrepaidCannibalization Break-even
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$6M per year).
155240
283325
368
425
0
100
200
300
400
500
600
700
20% 30% 40% 50%
With Prepaid CaseCannibalization rate
Without Prepaid Case
000sPrepaidPostpaid
Page 22
0
100
200
300
400
500
600
700
800
900
1,000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
Daily GrossActivations
January Average 514 per day
February actual average 468 per day
February projected 632 average per day 26%GAP
Launch of low end postpaid
plan
Note: Reporting difficulties resulted in zeros for Jan 6 and 7, those subs added in days following Jan 7.Feb data through Feb 27.
The early impact of the low end postpaid plan suggested internal BM prepaid cannibalization of postpaid of around 26%. While substantial, this result represented the upper limit, given the postpaid advertising campaign and dealer incentive structures and training.
CVM Case Studies – PrepaidExisting Base Cannibalization – BM
Page 23
CVM Case Studies – PrepaidPrepaid Customer Distribution
Minutes of Use Revenue
Very few customers represent the majority of prepaid minutes and revenue, requiring targeted, segment specific action.
Avg. MoU for user groupAvg. ARPU for user group
# of subscribers (‘000s)- sorted by descending Net MoU # of subscribers (‘000s) - sorted by descending Net ARPU
sub #s0
50
100
150
200
250
300
0 50 100 150 200 250 300 350 400
Cumulative Net MoU
Avg net MoU
C$
0
20
40
60
80
100
120
140
0 50 100 150 200 250 300 350 400
Cumulative Net ARPU
Total Cumulative Revenue
Total Monthly Revenue C$ M
Top 25% of base has an MoU of 85 Bottom 25%
has an MoU of less than 2
7
5
4
3
2
6
1
0
Top 18% are responsible for 70%
of total revenue
Top 50% are responsible for
96% of total revenue
Note: Net revenue includes all contra elements.
Page 24
(251) (175) (39)
301
1,688
228
2590
65
(500)
0
500
1,000
1,500
2,000
Zero users Low users (<20 min)
Medium users (20-59 min)
High users (60-200)
Very high users(200+)
Lif
etim
e E
BIT
DA
per
use
r
(70)
0
70
140
210
280
Ave
rag
e M
OU
per
use
r
Lifetime EBITDA per user
Average MOU per user
On average, only High and Very high users have a positive EBITDA...
CVM Case Studies – PrepaidPrepaid Customer Profitability Segments
Page 25
Mo
nth
ly s
pen
d (C
$)
0
20
40
60
80
0 40 80 120 160 200 240
No upselltoo big of a
stretch
Upselltarget “Upsell” only to avoid churn Minutes
of Use
60
Reprice at MoU of 200 is C$29.50
Prepaid
RealTime 150
Revenue gain if upselling from MoU of 60 is C$7.00
MoU 0-60 MoU 60-80 MoU 80 +% of users 87.3% 4.5% 8.2%% of minutes 39.5% 10.6% 49.9%
CVM Case Studies – PrepaidMigration of Prepaid Subscriber to Postpaid
…As a result migrating high users to postpaid is expensive, representing an average reprice of 36% for users over 80 MoU.
Page 26
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.
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
# of accounts Daily MoU
# of accounts
Key MoU driver: number of days of use Key MoU driver: usage per day
Days of use
MOU per day
CVM Case Studies – PrepaidValue Drivers – Days of Use
Page 27
A usage stim initiative targeted to the prepaid segment showed that low users could be drastically stimulated with an off-peak offer.
-17 -14 -11 -8 -5 -2 1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70
Day Relative to Take-up (low users)
Day proxy dMoU Free proxy dMoU Nights proxy dMoU
• Phone used 17% of days• Daily ARPU $0.17• Daily MoU 0.45
• Phone used 32% of days• Daily ARPU$0.39 (excluding $25
subscription fee)• Daily MoU 2.29
Before After hours feature After After hours feature
Daily MOU Index
CVM Case Studies – PrepaidTargeted Usage Stim – Off-Peak
Page 28
Introduction
Background on CVM
CVM Case Studies from Bell Mobility• Background• Prepaid• Postpaid
Engagement Structure
Agenda
Page 29
CVM Case Studies – PostpaidOverview of Postpaid
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.
Background & Issues CVM Analysis Strategy/Results
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)
• 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
Churn• Relatively low churn rates (1.5%)• Most resources devoted to outbound
retention campaigns
Low usage• MoU is low compared to industry
average and drives revenue negative events.
• 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
• Commissions paid on usage features no matter what pre-existing usage streams were
• Reviewed profitability of feature sales, targeted accordingly
• Reprice reduced on feature sales by C$8M per year
• 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.
Test Environment• Lack of clean, controlled
environment makes product development slower, riskier, and lower impact
• No proper understanding of offer value vs. return
Data Sources• Existing data sources are aggregates.
Most requests are not lifecycle based
• 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
Page 30
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.
Traditional DataWarehouse
CVM Datamart
Data Level
Frequency ofupdate
Ease ofsupportand use
• Aggregated over time• Processed / billed data
• Weekly, monthly• Delayed by bill cycle• Shifted across users depending
on bill cycle
• Accessible by any user through a simplified graphical user interface
• Limited flexibility in creating new variables
• Mostly used for reporting
• Individual call records, account profile changes
• Highest possible level of granularity
• Twice a day• Date is absolute (not shifted in
time) across users
• 10+ times more data• Used by technically and statistically
more advanced analysts• Very flexible• Mostly used for strategy definition
CVM Case Studies – PostpaidMobile Industry Data Source Comparison
Page 31
Cluster has developed for its clients a CVM Datamart, which incorporates all customer transactions in a near real time format.
Account Change Data
Historical Data
Usage andBill Data
• Individual call records
• No delay (up to 1 day)
• Roaming usually not included
• Prerated CDR (includes call type definitions, distance)
• Individual account / user profile transactions- Activation- Deactivation- Migration between RPs,
features
• Creates near real time customer profile and historical profile by day
• 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
CVM Case Studies – PostpaidKey Components of CVM Datamart
Page 32
CVM Case Studies – PostpaidData Foundation
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.
Real-time DatamartNeeds
Real-time usage variables (for
usage database)
Each account transaction (for
profile database)
User information for entire lifecycle
Real-timeDatamartSystem Architecture
Postpaid
Prepaid
Voicemail
Browser
Roaming
Switches
Assign User Info
Split M2M/ Remove
DuplicatePre-rating
Daily Activity
Log
User Profile Change
Billing
Data warehouse (monthly
summary of bill cycles)
Customer Service
1
2
3
1
3
2Feeds
captured twice daily before
billing
Update once per month
• 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 (demo-graphics)
Page 33
As a result of our modelling of customer economics, we have centered our CVM analyses on usage, rate plan and feature migration, and churn.
EXISTING CUSTOMER VALUE
• High breakage users have high churn rates
• Usage declinesprior to churn
• Usage trends precede migration both upward and downward
• Partial value loss• Total value loss
• Out of bundle revenues
• LD • Roaming
USAGE
CVM Case Studies – PostpaidExisting Base Value Drivers
CHURN MIGRATION
Page 34
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.
60
70
80
90
100
110
120
130 Migrations Up
Migrations Down
MoU Index (100)
Month of Migration
52% 52%45%
73%
45%28%
48% 48%55%
27%
55%72%
0%
25%
50%
75%
100%
Usage drop in month 1 - 6 prior to churn
Usage in month 1-6 prior to churn compared tomonth 7-12 prior
Usage before Migration Usage before Churn
Rate group 1
Rate group 2
Rate group 3
Rate group 4
Rate group 5
Rate group 6Months prior
to migrationMonths after
migration
Notes: 100%is the average usage through month 7 - 12 prior to churn.
CVM Case Studies – PostpaidUsage as a Predictor of Migration and Churn
Page 35
020406080
100120
- 3 6 9 12 15 18 21 24 27 30 33 36 39
0
20
40
60
80
100
- 2 4 6 8 10 12 14 16 18 20 22 24 26 28
0
20
40
60
80
100
- 2 4 6 8 10 12 14 16 18 20 22 24 26 28
01020304050607080
- 2 4 6 8 10 12 14 16 18 20 22 24 26 28
020
406080
100120
- 3 6 9 12 15 18 21 24 27 30 33 36 39
0
20
40
60
80
100
- 3 6 9 12 15 18 21 24 27
150 ANALOG 150 DIGITAL
Incoming Minutes
1 min incoming to 1 min outgoing
1 min incoming to 3.6 min outgoingOu
tgo
ing
Incoming Minutes
1 min incoming to 1.2 min outgoing
1 min incoming to 3.2 min outgoing
Pea
k
Off-Peak Minutes
1 min off-peak to 0.7 min peak
1 min off-peak to 3.6 min peak
Off-Peak Minutes
1 min off-peak to 0.8 min peak
1 min off-peak to 3.3 min peak
Toll Minutes
No Association
1 min toll to 1.7 min non-toll
Non
-To
ll
Toll Minutes
No Association
1 min toll to 1.8 min non-toll
Pea
k
Ou
tgo
ing
N
on-T
oll
Theory Observations
Off-Peak
Incoming
Long Distance
• Psychology is main hurdle to usage/ revenue stim
- Mobile for safety only
- Price perception vs. actual price
• 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
Changing the number of modes of use dramatically increases total usage, as customers begin to think of their mobile like their home phone.
CVM Case Studies – PostpaidValue Drivers – Modes of Use
Page 36
-3 -2 -1 1 2 3
239227
255
307
277261
312
267
239
-3 -2 -1 1 2 3
CVM Case Studies – PostpaidValue Drivers – Mobile Browser Usage
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.
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.
Low freq., 1-2 weeksMed freq., 3-5 weeksHigh freq., 6-10 weeksBrowser MoU
MoU/User
Relative Month
284
330350
272
319
363
268
323338
3
9
32
3
0.2
40
26
3
0.1
Before they started using the browser, high frequency users had declining MoU. After using the browser, they had the highest MoU stim.
Page 37
0%
5%
10%
15%
20%
0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200+
18.4% 19.2% 25.0%
Bundle Utilization Percentage
Per
cen
tag
e o
f U
sers
Usage Pattern
Action
Priority
Expected Benefit
High breakage
Sell subsidized/free usage features
High
• Reduced churn and downward migration
• No risk of reprice• Some LD stim
Low breakage
Sell full price/discounted VAS features
Medium
• Out of bundle usage revenue
• Some LD stim
Overage
Offer stretch features, other VAS (2)
Low
• Keep out of bundle usage revenue
Upward migrate/Offer stretch features, other VAS
Low
• Reduce churn of high value users• Keep out of bundle usage revenue• Secure higher access fee
12.1% 25.3%
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.
CVM Case Studies – PostpaidCampaign Targeting
Page 38
Downward Migration vs. Deactivation Upward Migration vs. Activation
Migration activity is a large value driver previously untracked. It represents 52% of all gains and 37% of all losses in access revenues.
CVM Case Studies – Postpaid Migration Importance – Value Compared to Activation/ Deactivation
Number of Users
Drop in Access Charges
Access Value Number % of Total Value Change
Migrations Down $-622,985 44,600 52%Churn $-581,034 20,001 48%
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
0 -10 -20 -30 -40 -50 -100 <-100
Churn
Migrations
Notes: 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.
Number of Users
Access Value Number % of Total Value Change
Migrations Up $987,384 35,732 37%New Users $1,707,074 72,991 63%
Increase in Access Charges
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
45,000
0 10 20 30 40 50 100 <100
New Users
Migrations
Page 39
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.
CVM Case Studies – Postpaid Migration Addressability – Complexity of Combinations
Ranking According to Number of Migration Events
Ranking According toRevenue Impact
Rate Group CombinationsSorted by Number of Migration Events
1,272 Total Combinations for February
Note: Expected revenue combination based on differences on average ARPU per plan.
0%
20%
40%
60%
80%
100%
120%
0 500 1000 1500
0%
20%
40%
60%
80%
100%
120%
0 500 1000 1500%
of T
otal
Rev
enue
Cha
nge
5 rate plan combinations represent
42% of migrations9% of revenue impact
12 rate plan combinations represent
61% of migrations12% of revenue impact
38 rate plan combinations
represent80% of
migrations39% of expected 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
% o
f Tot
al M
igra
tions
Rate Group CombinationsSorted by Revenue Impact
7 rate plan combinations represent
17% of migrations and 20% of revenue impact
26 rate plan combinations represent41% of migrations and 40% of revenue impact
38 rate plan combinations
represent48% of migrations
and 47% of revenue impact
Must examine 186 rate plan combinations to include
80% of migrations and 80% of revenue impact
1,272 Total Combinations for February
Page 40
• Do not call
Prevent CertainMigrations
• 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 CertainMigrations
• 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
Shift Customersto Certain RPs
• 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
• Target certain outbound migrations based on feature sales
• Recommendation engine for targeting and offer design
• Stretch features for upsell
Flashcut
• 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
• Only accomplished where in year revenue constraints are met
• Recommendation engine for finding ideal plan or targeting
• Recommendation engine for targeting and offer design
Leave Intact
• 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
• Fulfill Requests
CVM Case Studies – Postpaid Migration Example: Policy Recommendations
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.
Description -Key Segment Affected Outbound Inbound
Page 41
$40.28
$11.94 $12.91 $16.40
$0
$25
$50
Original ideamatching
across thebase
Alternative A(match on OP
plans)
Alternative B(match on OP
+ RT plans)
Alternative C(match on OP,RT and flashed
old)
By analyzing the expected reprice using CDRs, saved Bell Mobility an expected $26M from avoided reprice.
Background
• Competitor changed off-peak clock, beginning off-peak at 6 PM instead of 8 PM
• Initial reaction was to match competitor clock across entire base
• 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
• Final recommendation was to match only on certain rate plans, limiting reprice
• Result was an expected savings of $26M annual EBITDA
0%2%4%6%8%
10%12%14%16%
-100 -80
-60
-40
-20 0 20 40 60 80
($30)($25)($20)($15)($10)($5)$0$5$10$15
% of SubsIn year revenue reprice (annual)
% Reprice ($M)
In y
ear
rev.
imp
act
(rep
rice
) $M
)
(assuming 20% of users with 10% or more better off switch)
% better offer new plan
CVM Case Studies – Postpaid Acquisition Reprice
Page 42
Deactivation Impact ARPU Impact
The targeting difficulties on outbound churn campaigns have driven poor actual results, contrary to carrier’s previous perception.
During the period between pull and mailing 13% of both the target and control group deactivated
implying late action on save attempt
Campaign launch
Peak in deactivation rate 2 months prior to campaign suggests outdated data
ARPUDeactivationrate
$137
$143 $142$140
$140
$135
$142 $141 $142
$153
$146
$141
$143
$152
$144
$137
$125
$130
$135
$140
$145
$150
$155
Mar Apr May Jun Jul Aug Sep Oct
TargetControl
3.6%3.4%
3.2%2.9%
3.4% 3.5%
2.6% 2.5%
3.6%3.7%
5.2%
1.7%
3.0%3.2%
5.5%
1.9%
0%
1%
2%
3%
4%
5%
6%
Mar Apr May Jun Jul Aug Sep Oct
TargetControl
CVM Case Studies – PostpaidChurn — Difficulty with Outbound Campaigns
Reduction in ARPU indicates that high
value users churned at higher rate.
Campaign launch
Page 43
In almost all outbound loyalty programs, the majority of users taking up a retention offer are not actually churners, limiting total returns.
CVM Case Studies – Postpaid Churn — Outbound Loyalty Funnel Illustrative
1089
Non-churners
Churners or potential
churners over next
six months
Existing postpaid consumer base
(1.0M users)
Targeted users based on predictive churn model
score calls (96,000 users per month)
Contacted users taking up offer (25,920 users)
Targeting Process• Predictive churn model• Call center support
~100,000 users/month• 1.5% monthly churn in base• 4.5% monthly churn in list
Contact Offer Process• RPC rate of ~30%• Uptake rate of ~90%• Assumes equal RPC and Uptake
for churners and non-churners
90,000 25,9206,999
Users remaining on network after 6 months
(21,566 users)
Realized Save Rate• Save rate of 20% for churners
910,000
70,080
18,921
1,400
5,599
93% of users taking up the offer, however, are
non-churners over next six months
18,921
Customers who churn despite loyalty offer
Page 44
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.
Move away from migration offers to feature offers
Room to enrich offers depending on results
Increase investment depending on observed
results for targeted winbacksegments
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.
• Targeting:- 100% of those called are
churners• Offer Uptake and Save Rate:
- 5%• Cost of Contact:
- $6.70 per contact
• Give away revenuebreakeven: $33.50, 74% of ARPU
• 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
• Give away revenue breakeven: $12.50,28% of ARPU
• Targeting:- 27% churners over 6 months
in lists• Offer Uptake Rate:
- 90%• Save Rate:
- 20%• Cost of Contact:
- $6.70 per contact
• Give away revenue breakeven: $2.31, 5% of ARPU
Assumptions: C$45 ARPU, saved users remain on network for 12 months, C$5.00 per contact outbound, C$4.00 inbound
Outbound Inbound retention Winback
CVM Case Studies – PostpaidChurn — Channel Economics Illustrative
Page 45
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.
Area of Impact Test EnvironmentTypical Process
• Due to large scale approval andproduction process is lengthy
• Reading results from bills delayscampaign performance evaluation by 2 - 3 months
• Easy to hit extremes of either rich offerwith high risk of reprice, or lessattractive 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
CVM Case Studies – PostpaidTest Environment — Description
• Due to small scale and cross functionalteam offers are launched very quickly
• Due to single offer environment and accessto CDR level data results are available in 2 -3 weeks
• As a result of the small scale and thetesting of various offers the reprice risk islimited 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 broadbased launch
• Knowledge handover from Diamond-Cluster through on the job training
Time to Market
Risk of Reprice
Expected Returns
Customer ManagementProcess
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.
Page 46
By creating a test environment, DiamondCluster built a testing mentality within the organization which improved the product development process.
• 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 net-
negative 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
All Departments Realized Immediate Benefits...
…and in the Long Term, the Product Development Process Flow Was Improved
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
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 post-campaign behavior (churn, migration, usage)
CVM Case Studies – PostpaidTest Environment — Benefits
Page 47
Weekend
Evening
Peak
183%
168%
-4%
342 users taking Afterhours at Free
0
100
200
300
400
500
600
700
-24 -18 -12 -6 0 6 12 18 24 30 36 42 48 54 60 66
Date relative to take-up date
seco
nd
s / u
ser
/ day
evening peak weekend
Take-Up Date:April 20, 2000
342 users taking Afterhours at Free
0
50
100
150
200
250
300
350
400
-3 -2 -1 0 1 2 3 4 5 6 7 8 9
Week Relative to Take-Up
Seo
nds
/ use
r / d
ay (i
ndex
ed b
ased
on
befo
re a
vg.)
evening
peak
weekend
Notes: Graph shows daily variation of 342 users who took AH freeAll users shifted to same relative take-up day (day zero)Graph shows usage in terms of seconds
Notes: Graph shows daily variation of 342 users who took AH freeAll 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)
CVM Case Studies – PostpaidTest Environment — Improved Targeting
Daily Tracking Weekly Tracking
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 48
22%
2%
0%
10%
20%
30%
Hardware Upgrade
Attempts
Control
% Stim
0.0%
1.0%
2.0%
Hardware Upgrade
Attempts
Control
% Churn
0.0%
1.0%
2.0%
3.0%
4.0%
5.0%
6.0%
7.0%
8.0%
Hardware Upgrade
DownsellControl DownUpsellControl Up
% Migration
439 (62 take-up)#
# 439 3008 SAME AS CHURN#
CVM Case Studies – PostpaidTest Environment — Tracking Results
Usage Churn Migration
In order to establish complete and accurate metrics, tracking incorporates usage, churn, and migration impacts.
Notes: 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.
3008
Page 49
Introduction
Background on CVM
CVM Case Studies from Bell Mobility• Background• Prepaid• Postpaid
Engagement Structure
Agenda
Page 50
Phase 1AInitialDiagnostic & Phase 1B Testing
Phase 2:Proof of Concept for Tool
Phase 3:Implementation
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
Resources:• 4-6 persons (DCI)• Approximately 4 client team mambers from
marketing, sales, finance, IT and CS
3 Month
Engage-ment
Resources:• Approximately 4-6 persons (DCI)• Approximately 2 client
resources from department/division under study
3-5 Month
Engage-ment
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
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:• 4-6 persons• 6+ fully dedicated internal resources
from marketing, sales, finance, IT and CS
6-12 Month
Engage-ment
A pilot consists of 3 months to construct an initial diagnostic and testing.
Engagement StructureCVM Project Phasing and Resources
Page 51
CVM can only be successful through cross-department planning and collaboration, with marketing in the coordinating role.
• Coordination• Education
Project Design/Management Office
Marketing Team
• Data modelling
• Database marketing
• Customer loyalty
• Turnover prevention
• Other functions
Other Functions/Departments
IT
IT/CS/Systems
Finance
CS/Systems
CS/Finance
Infrastructure
Data Feeds/Construction of Variables
Functions Using Variables/Reports
Offer Design/Implementation
Tracking
• Support• Management
• Management• Design
Feedback and improvement
loops
Internal project
dependencies
Work Steps
Engagement StructureProject Team Structure