Customer Value Management basics

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



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



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



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


$M74

52% breakeven cannibalisation rate, revenue

Lifetime Revenues for New Users

235 365 430 494 559236

0

200

400

600


$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



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


Off-Peak Minutes



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


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


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



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

Date post:	10-May-2015
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