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CVM Introduction
Eric Smith
July 12, 2001
Agenda

Introduction

Background on CVM

CVM Case Studies from Bell Mobility
• Background
• Prepaid
• Postpaid

Engagement...
Introduction

Session Goals
• Goals:
– Introduce CVM concepts for C-Level Clients and Prospects

• Worksteps
– Outline why...
Introduction

What is Customer Value Management (CVM)?
• CVM helps corporations develop tailored products and services to ...
Introduction

What is the difference between CVM and CRM?
Customer VALUE Management

Customer RELATIONSHIP
Management

Foc...
Agenda

Introduction

Background on CVM

CVM Case Studies from Bell Mobility
• Background
• Prepaid
• Postpaid

Engagement...
Background on CVM

Development of CVM
•

The CVM practice was developed by DiamondCluster in North America for wireless
ca...
Background on CVM

What is the Economic Foundation of CVM?
After CVM Approach

Before CVM approach
Additional potential re...
Background on CVM

How Do We Approach CVM?
DATA
all data at individual transaction level:
call data records from switch, d...
Background on CVM

Mobile Markets in the US
Industry Growth
# of subscribers
200

Penetration

160

60 mins.

177

Bear St...
Background on CVM

Relative CVM Complexity for Mobile Operators

• Limited separation of
purchaser and consumer
• Some com...
Agenda

Introduction

Background on CVM

CVM Case Studies from Bell Mobility
• Background
• Prepaid
• Postpaid

Engagement...
CVM Case Studies

Bell Mobility Overview
EOP Subscribers
000s subs

Revenue
C$M

Total subs growing at 20-25% p.a.
Prepaid...
CVM Case Studies – Background

Overview of CVM Phases at Bell Mobility
Bell Mobility

CVM Approach

• Market growth focuse...
CVM Case Studies – Background

Benefits of CVM at Bell Mobility
Impact of Successive CVM Phases
Annual EBITDA impact
(C$, ...
Agenda

Introduction

Background on CVM

CVM Case Studies from Bell Mobility
• Background
• Prepaid
• Postpaid

Engagement...
CVM Case Studies – Prepaid

Overview of Prepaid
Background & Issues

CVM Analysis

Strategy/Results

•

No lifetime profit...
CVM Case Studies – Prepaid

Overview of Customer Economics

Illustrative

Lifetime
value of $100

Shift in
MoU by 20%

Mon...
CVM Case Studies – Prepaid

Economics of Prepaid Subscriber
Customer over Lifetime
$300.00

Present Value
(C$)

Lifetime
v...
CVM Case Studies – Prepaid

Economics of Low-End Postpaid Subscriber
Customer over Lifetime

Present Value

Lifetime
value...
CVM Case Studies – Prepaid

Cannibalization Break-even
Year 2000 Revenue from New Users

Users
000s
700

100
75
50
25
0

6...
CVM Case Studies – Prepaid

Existing Base Cannibalization – BM
Daily Gross
Activations
1,000

900

January Average 514 per...
CVM Case Studies – Prepaid

Prepaid Customer Distribution
Minutes of Use

Revenue
Avg. ARPU for user group
C$

Avg. MoU fo...
CVM Case Studies – Prepaid

Prepaid Customer Profitability Segments
2,000

280

Lifetime EBITDA per user
Lifetime EBITDA p...
CVM Case Studies – Prepaid

Migration of Prepaid Subscriber to Postpaid

Monthly spend (C$)

80

Reprice at MoU of
200 is ...
CVM Case Studies – Prepaid

Value Drivers – Days of Use
Key MoU driver: number of days of use

# of
accounts

Key MoU driv...
CVM Case Studies – Prepaid

Targeted Usage Stim – Off-Peak
Before After hours feature
•
•
•

Daily
MOU
Index

After After ...
Agenda

Introduction

Background on CVM

CVM Case Studies from Bell Mobility
• Background
• Prepaid
• Postpaid

Engagement...
CVM Case Studies – Postpaid

Overview of Postpaid
Background & Issues
Data Sources
• Existing data sources are aggregates....
CVM Case Studies – Postpaid

Mobile Industry Data Source Comparison
Traditional Data
Warehouse

CVM Datamart

• Aggregated...
CVM Case Studies – Postpaid

Key Components of CVM Datamart
Usage and
Bill Data
• Individual call records
• No delay (up t...
CVM Case Studies – Postpaid

Data Foundation
Real-time Datamart
Needs

Real-time
Datamart

System Architecture
Switches

1...
CVM Case Studies – Postpaid

Existing Base Value Drivers

• High breakage users
have high churn rates
• Usage declines
pri...
CVM Case Studies – Postpaid

Usage as a Predictor of Migration and Churn
Usage before Migration

Usage before Churn

MoU I...
CVM Case Studies – Postpaid

Value Drivers – Modes of Use
Theory

No Association

80
60
40
20
0

1 min toll to 1.8 min non...
CVM Case Studies – Postpaid

Value Drivers – Mobile Browser Usage
Low freq., 1-2 weeks
Med freq., 3-5 weeks
High freq., 6-...
CVM Case Studies – Postpaid

Campaign Targeting

Percentage of Users

20%

18.4%

19.2%

25.0%

12.1%

25.3%

15%

10%

5%...
CVM Case Studies – Postpaid

Migration Importance – Value Compared to Activation/
Deactivation
Downward Migration vs. Deac...
CVM Case Studies – Postpaid

Migration Addressability – Complexity of Combinations
Ranking According to
Number of Migratio...
CVM Case Studies – Postpaid

Migration Example: Policy Recommendations
Description Key Segment Affected

Outbound

Inbound...
CVM Case Studies – Postpaid

Acquisition Reprice

• Final recommendation was to match
only on certain rate plans, limiting...
CVM Case Studies – Postpaid

Churn — Difficulty with Outbound Campaigns
Deactivation Impact
Deactivation
rate
6%
5%

ARPU ...
CVM Case Studies – Postpaid

Churn — Outbound Loyalty Funnel

Illustrative

Existing postpaid
consumer base
(1.0M users)
T...
CVM Case Studies – Postpaid

Churn — Channel Economics
Outbound

Illustrative

Inbound retention

Winback

Assumptions: C$...
CVM Case Studies – Postpaid

Test Environment — Description
Definition:

Launch inbound and outbound campaigns on a small ...
CVM Case Studies – Postpaid

Test Environment — Benefits
All Departments Realized Immediate Benefits...
• Marketing benefi...
CVM Case Studies – Postpaid

Test Environment — Improved Targeting
Daily Tracking
Take-Up Date:
April 20, 2000

Weekly Tra...
CVM Case Studies – Postpaid

Test Environment — Tracking Results
Usage

Churn

% Stim

Migration

% Churn

% Migration

Do...
Agenda

Introduction

Background on CVM

CVM Case Studies from Bell Mobility
• Background
• Prepaid
• Postpaid

Engagement...
Engagement Structure

CVM Project Phasing and Resources
Project Scope/Deliverables:
• Review and analyze sample client dat...
Engagement Structure

Project Team Structure
Project Design/Management Office
• Support
• Management

• Management
• Desig...
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Customer Value Management basics

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Transcript of "Customer Value Management basics"

  1. 1. CVM Introduction Eric Smith July 12, 2001
  2. 2. Agenda Introduction Background on CVM CVM Case Studies from Bell Mobility • Background • Prepaid • Postpaid Engagement Structure Page 2
  3. 3. Introduction Session Goals • Goals: – Introduce CVM concepts for C-Level Clients and Prospects • Worksteps – Outline why CVM is critical for companies to meet their financial objectives – Explain components of CVM: data for analysis; understanding of customer economics, and customer behavior patterns; offer design; results from tracking and improvement cycles – Show Bell Mobility examples in the prepaid and the postpaid segments Lecture based – Work on the Sprint PCS case • Structure work – timeline, team, deliverables, initial hypotheses • Deliver findings – story line analysis, create recommendations in the areas of churn and migration - 2 teams Case workshop based The two parts – lecture and case based – will ensure that both the tools and the examples of CVM are introduced. Page 3
  4. 4. Introduction What is Customer Value Management (CVM)? • CVM helps corporations develop tailored products and services to their customers, in order to maximize profits on an individual customer level • The goal of CVM is to move towards mass customized offers and price discrimination based on: – Willingness to pay (both consumer and corporate) – Current customer value and usage profiles – Churn and migration risks • CVM enables companies to manage their firm value in the face of rapidly decreasing prices and potentially slower acquisition growth • Specifically, CVM generates or preserves value through: – Usage stimulation through micro-targeted offers – Rate plan and feature migration management through improved understanding of reprice potential and proactive offer design – Churn prevention through improved predictive modeling and targeted retention strategy – Improved acquisition strategies which consider existing base impact Page 4
  5. 5. Introduction What is the difference between CVM and CRM? Customer VALUE Management Customer RELATIONSHIP Management Focus • Improve profitability by delivering targeted offers • Retain customers by improving customer interactions Lever for Change • Product offers • Customer touchpoints Approach • Hypothesis, data-driven • Integrated, comprehensive Capabilities • Capture detailed customer data; ability to deliver micro-targeted offers • Channel integration to offer a consistent experience; “know the customer” Metric • Customer profitability • Customer retention Customer Expectations • Exceed on customer value • Exceed on customer service • Direct customer to most profitable offerings • …is acceptable for low value customers • Customer migration patterns; reasons for churn; value drivers • Streamline and improve processes Customer Service Attrition… Required Understanding • …should be reduced • Customer needs and expectations; channel usage CVM is focusing on creating profitable customer relationship. Page 5
  6. 6. Agenda Introduction Background on CVM CVM Case Studies from Bell Mobility • Background • Prepaid • Postpaid Engagement Structure Page 6
  7. 7. Background on CVM Development of CVM • The CVM practice was developed by DiamondCluster in North America for wireless carriers. Since then we have used it for LD and have developed the IC for retail banking • Successful CVM efforts bring together a wide variety of skills in the DCI consulting team, including marketing strategy, microeconomic analysis, statistical modeling, and information technology deployment • Current CVM initiatives: Bell Canada Bell Mobility Sprint PCS TIM Telesp CW Optus Telecom New Zealand Page 7
  8. 8. Background on CVM What is the Economic Foundation of CVM? After CVM Approach Before CVM approach Additional potential revenue/ consumer surplus created by micro-offers Uncaptured consumer surplus Carrier revenue Price Price Uncaptured consumer surplus Carrier revenue Consumer demand curve shifts out with tailored products Market Demand Curve Rate Plan 1 Existing Base Focus Rate Plan 2 Existing Base Focus Market Demand Curve Broad Rate Plan - New Users New Rate Plan 3 Existing Base Focus Old Quantity Micro-Offers to Existing Base The result of a successful CVM approach is the shifting out of the consumer demand curve and the capturing of consumer surplus. Page 8 Quantity
  9. 9. Background on CVM How Do We Approach CVM? DATA all data at individual transaction level: call data records from switch, daily account adjustments and transactions, daily account profile updates Customer Economics • Customer Behavior Understand drivers of individual user profitability, profits by segment • • • Offer Design How do customers behave over time? What types of behavior are linked? What actions change behavior and the corresponding economic drivers? • Target individual users with specific offers Results Tracking/ Improvement Cycle • Quantification of impact, incorporate results into future offer design FINANCIAL RESULTS Measurable financial impact such as usage stim for low users, prevented migration reprice, prevented churn Through micro-targeted offers, DiamondCluster has used subscriberlevel data to create real financial results, in usage, migration, and churn. Page 9
  10. 10. Background on CVM Mobile Markets in the US Industry Growth # of subscribers 200 Penetration 160 60 mins. 177 Bear Stearns Strategis 153 126 100 80 67 54 54 60 0.48 0.45 0.43 0.45 0.37 0.4 0.33 0.33 0.3 0.2 54 40 250 mins. 0.54 0.5 67 67 1,000 mins. 140 129 52% 116 101 120 102 114 83 86 106 43% 98 86 120 100 mins. 66% 0.6 140 Product Launches Price / minute ($) 500 mins. Merril Lynch 180 Price Declines 0.23 0.2 0.21 0.16 0.19 0.21 0.16 • VAS Services • Roaming inclusive plans • Text messaging services • WAP, browser services • Location based services • 3G services 0.16 0.12 0.1 20 0 0.10 0.09 0.12 20 02 * 20 03 * 20 00 * 20 01 * 19 99 19 98 19 97 0.0 Apr. 27, 1998 Feb. 15, 1999 Aug. 9, 1999 Apr. 17, 2000 Year Source: Merril Lynch. (*) forecasted. Source: Wireless week, Washington D.C. The mobile telecom industry is unique in its rate of growth, price declines, and changing nature of end user services, requiring dedicated thinking about its base management issues. Page 10
  11. 11. Background on CVM Relative CVM Complexity for Mobile Operators • Limited separation of purchaser and consumer • Some competition by call with override codes • Low switching cost Potential Complexity of CVM Offers High • • • • Frequent separation of purchaser and consumer Limited transferability (name and ID) Huge potential range of products (city pairs) Competition per trip, with medium switching costs Mobile carriers Airlines Long distance operators Financial services Low • No separation of purchaser and consumer • High potential transferability • Large range of products • Competition per item, low switching costs Traditional retail: movies, clothing, music, books, etc Low High Transactions per User • Frequent separation of purchaser and consumer • No transferability (unique mobile number) • No competition per call, competition by bundled services only, with high switching costs • No separation of purchaser and consumer • Limited transferability • Large range of products • Competition per transaction, low to medium switching costs The mobile sector is one of the most complex industries for CVM data analysis, given the sheer volume of customer transactions and the potential complexity of pricing each transaction. Page 11
  12. 12. Agenda Introduction Background on CVM CVM Case Studies from Bell Mobility • Background • Prepaid • Postpaid Engagement Structure Page 12
  13. 13. CVM Case Studies Bell Mobility Overview EOP Subscribers 000s subs Revenue C$M Total subs growing at 20-25% p.a. Prepaid share stable at 40% Prepaid Revenues growing at 7-22% p.a. Postpaid 1036.6 857.0 126.3 509.1 1335.4 1454.9 97 98 99 863 1825.5 00 929 97 1221.0 1349.0 98 99 MoU Prepaid 95 30.1% 28.4% 27.2% 27.1% 98 99 00 01 98 99 44 42 37 Notes: Postpaid 221 195 186 165 97 01 Postpaid MoU increasing at 5-13% p.a. Due to platform error, incoming minutes are not billed for Minutes per month Market share stabilizing after entry of two, digital only competitors 32.2% 00 01 Market Share (Subs) % 1,394 1,134 981 00 01 All 2001 figures are estimates. Source: company publications. Bell Mobility is the incumbent wireless carrier in Ontario and Quebec, with C$1.4B revenue and 2.8 M subscribers. Page 13
  14. 14. CVM Case Studies – Background Overview of CVM Phases at Bell Mobility Bell Mobility CVM Approach • Market growth focused in pre-paid segment (BM had no presence, competitor launched prepaid product) • Phase I: Prepaid - Analyzed revenue impact of introducing prepaid product through estimation of cannibalization of low end post-paid revenue and growth in pre-paid subscriber base and revenue • Low churn rates (1.5% per month) compared to industry average • Phase II: Postpaid - Analyzed revenue impact of existing strategies for usage stim, customer retention, and rate plan migrations. We widely deployed successful initiatives and abandoned or modified currently unsuccessful strategies • Lagged competitors on MoU but led on average revenue per minute (ARPM) • Complex systems and offers - 1,200 separate rate plans, 300 features • No analysis of migration patterns • Phase III: Enterprise - Developed tool to calculate profitability of each customer in the segment and the impact of alternative offers in terms of value to customer and profit to BM • Sophisticated, third generation data warehouse prior to DCI presence, but no CDR level data and minimal tracking of campaign effectiveness Evaluation of the competitive positioning of Bell Mobility led to prioritization of CVM initiatives. Page 14
  15. 15. CVM Case Studies – Background Benefits of CVM at Bell Mobility Impact of Successive CVM Phases Annual EBITDA impact (C$, million) $100 18% improvement of annual EBITDA 7 $90 Achievements from Each Phase • Phase I: Prepaid - Analysis of profitability of prepaid product led to successful product launch and total revenue gains of C$10 million per annum (based on 40% cannibalization of low-end post paid) 18 $80 6 $70 $60 • Phase II: Postpaid - Postpaid analysis focusing on targeted feature sales, migration management, churn and improved acquisition strategies led to revenue savings of C$70 million per annum 42 $50 98 $40 $30 14 $20 $10 8 10 $0 Pre-paid Targeted Migration Improved Improved Enterprise revenue1 feature management3 acquisition churn5 revenue6 sales2 strategies4 Total annual benefit 1. Due to successful launch of pre-paid product, after DiamondCluster analysis showed cannibalization of low -end postpaid to be 25%, much lower than 40% breakeven. C$10M figure based on value of continuing prepaid offer and conservative 40% cannibalization assumption. 2. Assuming 5% of feature repriced revenue saved for 10 months per customer, 600,000 features on customer accounts 3. Assumes 100,000 migrations per month for 12 months. For serial migrants assumes 1,000 people per month causing C$100 reprice loss per month. Backdating 10% of migrations by 2. 5 months at C$10 reprice per month. Proactively offering alternatives to 10% of migrations thus reducing reprice by C$7 per months for ten months. 4. Prevented launch of new off -peak clock - value based on assumption that 20% of customers who would be at least 20% better off would have migrated to the new rate plan. 5. Stopped C$0.5M monthly outbound churn effort where the econom of the campaigns was negative. ics 6. Based on similar usage, migration, and acquisition strategies applied to enterprise segment, and adjusting for relative percentage of revenue for the base, including the cost of reprice and the benefit of increased account share. 7. Based on estimated 2001 EBITDA of C$534M. • Phase III: Enterprise - Strategic roll-out commencing March 2001. Estimated revenue savings of C$18 million through targeted feature sales, migration management and improved acquisition strategies (based on savings proportional to consumer segment) CVM has been extremely effective in generating new revenue streams and eliminating revenue loss resulting from poorly targeted programs. Page 15
  16. 16. Agenda Introduction Background on CVM CVM Case Studies from Bell Mobility • Background • Prepaid • Postpaid Engagement Structure Page 16
  17. 17. CVM Case Studies – Prepaid Overview of Prepaid Background & Issues CVM Analysis Strategy/Results • No lifetime profitability model to determine absolute returns for a new acquisition campaign (prepaid/postpaid) • Developed simple economic model of lifetime profits per user, gaining support for all inputs from relevant departments • Process in place to apply model to all new acquisition programs, handover to client completed • Limited understanding of relative lifetime profitability of new adds and the role of cannibalization (prepaid/postpaid) • Applied model to prepaid and lowend postpaid users, determined relative profits and breakeven cannibalization rates Case study analysis to determine how actual cannibalization rates compared to breakeven • Gained support for C$5M in prepaid marketing by showing actual cannibalization rates close to 25%, much less than breakeven rates of 40%+ Total value of segment C$10M per year, even at high cannibalization rates Applied model to each individual prepaid user, quantifying months to breakeven and total lifetime returns Reviewed scope for prepaid usage stim, prepaid to postpaid migration • • • Limited understanding of the distribution of lifetime profits across user base, role of value management • • • • Refined strategy to migrate top-end prepaid users to postpaid, avoiding expected revenue hit of 12% Gained support for general usage stim program Using CVM tools, we are able to measure lifetime profits for prepaid and postpaid users, manage cannibalization before prepaid programs were rolled out, and prioritize prepaid migration and usage stim strategies. Page 17
  18. 18. CVM Case Studies – Prepaid Overview of Customer Economics Illustrative Lifetime value of $100 Shift in MoU by 20% Month 1 Month 2 Month 3 Month 4 Month 5 Breakeven in 5 months Month 6 Month 7 Month 8 Customer migrates from $60 plan to $40 plan Month 9 Customer churns in month 9 Cumulative customer EBITDA Usage charges Access charges Cost of acquisition Cost of maintenance Key economic factor fixed for existing base Key economic factor which can be influenced Customer Acquisition cost Our modelling of customer economics is the foundation of our CVM analysis. Page 18
  19. 19. CVM Case Studies – Prepaid Economics of Prepaid Subscriber Customer over Lifetime $300.00 Present Value (C$) Lifetime value $183 $500 100 $400 (C $) $200.00 Cumulative EBITDA Breakeven in 10.5 months Lifetime margin = 53% 68 $300 68 454 $100.00 EBITDA per month 35 $200 $100 183 34 31 28 25 22 19 16 13 10 7 4 1 $0.00 $0 Lifetime Direct Cost Revenues of acquisition (without advertising overheads) ($100.00) Commissions on top-ups Network costs Customer service costs / Bad debt EBITDA Notes: Assumes no pre-to-post upsell. Lifetime revenues based on ARPU of $17.00 / month (includes $50 increase in package price from $99 to $149) Direct COA costs include: $13 dealer bonus, $6 coop, $40 dealer margin, $10 activation costs, $15 packaging costs, and $16 handset subsidy ($115 phone cost - $99 revenue before $50 package price increase) Commissions on top-up at 15%. CS costs at $1.25 / month, bad debt at 0.25%. Lifetime churn at 3%, discount rate of 15%. Using actuals, our model showed that the lifetime value of a new prepaid user was $183, with a breakeven time of 10.5 months. Page 19
  20. 20. CVM Case Studies – Prepaid Economics of Low-End Postpaid Subscriber Customer over Lifetime Present Value Lifetime value $406 $500.00 $400.00 Breakeven in 23 months $300.00 (C$) $1,600 $1,400 Cumulative EBITDA 279 $1,200 (C $) 103 $200.00 EBITDA per month $100.00 515 1469 $400 66 61 56 51 46 41 36 31 26 21 16 11 167 6 1 $800 $600 $0.00 ($100.00) Lifetime margin = 28% $1,000 405 $200 ($200.00) $0 ($300.00) ($400.00) Lifetime Revenues Direct Cost Residuals of acquisition (without advertising overheads) Network costs Customer service costs / Billing / Bad debt EBITDA Notes: Assumes no 2nd headset subsidy over customer life. Lifetime revenues based on $25 access revenue + LD charges (10% of traffic at $20/minute) Usage at 150 minutes out of 200 min bundle each month $50 bad credit, Residuals at 7% Direct COA costs include: $13 dealer bonus, $15 coop, $60 dealer commission, $15 activation costs, $0 packaging costs, and $176 phone subsidy ($295 cost -$119 revenue) CS costs at $2.50 / month, bad debt at 1.5%. Billing at $0.63 / month. Lifetime churn at 3%, discount rate of 15%. While entry level postpaid users have roughly twice the lifetime values of prepaid users, their breakeven times are also twice as long. Page 20
  21. 21. CVM Case Studies – Prepaid Cannibalization Break-even Year 2000 Revenue from New Users Users 000s 700 100 75 50 25 0 600 500 400 74 43 47 31 With Prepaid Case 56 64 73 20% 30% 40% 50% Cannibalisation rate without Prepaid Case Lifetime Revenues for New Users 425 $M 300 600 240 283 325 368 236 200 365 235 With Prepaid Case 0 20% 430 494 559 0 155 With Prepaid Case 36% 471 400 200 100 52% breakeven cannibalisation rate, revenue $M Prepaid Postpaid 30% 40% 50% 20% 30% 40% 50% Cannibalisation rate without Prepaid Case Lifetime EBITDA Value of New Users Added Cannibalization rate Without Prepaid Case $M Notes: 425,000 target prepaid users and 155,000 mobility postpaid users from year 2000 plan In year revenues from prepaid= $102/users ($17.00 ARPU x 6 months), lifetime revenue value $554 In year revenues from postpaid user =$197.40/user ($32.90 ARPU x 6 months), lifetime revenue value $1519 Lifetime value per user: $239 prepaid, $565 mobility postpaid 200 43% breakeven cannibalisation rate, subscriber value 190 102 100 88 136 160 184 208 0 With Prepaid Case 20% 30% 40% 50% Cannibalisation rate without Prepaid Case Even at a 40% cannibalization rate, prepaid was a net positive contributor to both BM’s year 2000 revenue (C$10M per year) and the lifetime EBITDA value from new users (C$6M per year). Page 21
  22. 22. CVM Case Studies – Prepaid Existing Base Cannibalization – BM Daily Gross Activations 1,000 900 January Average 514 per day 800 Launch of low end postpaid plan February projected 632 average per day 700 26% GAP 600 500 400 300 February actual average 468 per day 200 100 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 Note: Reporting difficulties resulted in zeros for Jan 6 and 7, those subs added in days following Jan 7. Feb data through Feb 27. The early impact of the low end postpaid plan suggested internal BM prepaid cannibalization of postpaid of around 26%. While substantial, this result represented the upper limit, given the postpaid advertising campaign and dealer incentive structures and training. Page 22
  23. 23. CVM Case Studies – Prepaid Prepaid Customer Distribution Minutes of Use Revenue Avg. ARPU for user group C$ Avg. MoU for user group Total Monthly Revenue C$ M 7 140 300 Cumulative Net ARPU Cumulative Net MoU 6 120 250 Total Cumulative Revenue Avg net MoU 5 100 200 4 80 150 Top 25% of base has an MoU of 85 100 60 Bottom 25% has an MoU of less than 2 50 3 2 1 20 0 0 50 100 150 200 250 300 # of subscribers (‘000s)- sorted by descending Net MoU Top 18% are responsible for 70% of total revenue 40 Top 50% are responsible for 96% of total revenue 350 0 0 sub #s 400 0 50 100 150 200 250 300 350 400 # of subscribers (‘000s) - sorted by descending Net ARPU Note: Net revenue includes all contra elements. Very few customers represent the majority of prepaid minutes and revenue, requiring targeted, segment specific action. Page 23
  24. 24. CVM Case Studies – Prepaid Prepaid Customer Profitability Segments 2,000 280 Lifetime EBITDA per user Lifetime EBITDA per user Average MOU per user 1,000 210 140 1,688 65 500 70 25 0 9 Average MOU per user 228 1,500 301 0 0 (251) (175) (39) (500) (70) Zero users Low users (<20 min) Medium users (2059 min) High users (60-200) Very high users (200+) On average, only High and Very high users have a positive EBITDA... Page 24
  25. 25. CVM Case Studies – Prepaid Migration of Prepaid Subscriber to Postpaid Monthly spend (C$) 80 Reprice at MoU of 200 is C$29.50 60 aid Prep Revenue gain if upselling from MoU of 60 is C$7.00 40 RealTime 150 20 0 0 40 No upsell too big of a stretch % of users % of minutes MoU 0-60 87.3% 39.5% 60 80 Upsell target 120 160 “Upsell” only to avoid churn MoU 60-80 4.5% 10.6% 200 240 Minutes of Use MoU 80 + 8.2% 49.9% …As a result migrating high users to postpaid is expensive, representing an average reprice of 36% for users over 80 MoU. Page 25
  26. 26. CVM Case Studies – Prepaid Value Drivers – Days of Use Key MoU driver: number of days of use # of accounts Key MoU driver: usage per day # of accounts 1 2 3 4 5 6 7 8 9 MOU per day Daily MoU 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Days of use For low to medium prepaid users, MoU / day is surprisingly constant. The main driver of usage is the number of days the phone was used. For high users, MoU / day is the main revenue driver. Page 26
  27. 27. CVM Case Studies – Prepaid Targeted Usage Stim – Off-Peak Before After hours feature • • • Daily MOU Index After After hours feature Phone used 17% of days Daily ARPU $0.17 Daily MoU 0.45 • • • Day proxy dMoU -17 -14 -11 -8 -5 -2 1 4 7 10 13 16 19 22 25 Free proxy dMoU 28 31 34 37 Phone used 32% of days Daily ARPU$0.39 (excluding $25 subscription fee) Daily MoU 2.29 Nights proxy dMoU 40 43 46 49 52 55 58 61 64 67 70 Day Relative to Take-up (low users) A usage stim initiative targeted to the prepaid segment showed that low users could be drastically stimulated with an off-peak offer. Page 27
  28. 28. Agenda Introduction Background on CVM CVM Case Studies from Bell Mobility • Background • Prepaid • Postpaid Engagement Structure Page 28
  29. 29. CVM Case Studies – Postpaid Overview of Postpaid Background & Issues Data Sources • Existing data sources are aggregates. Most requests are not lifecycle based Low usage • MoU is low compared to industry average and drives revenue negative events. • Commissions paid on usage features no matter what pre-existing usage streams were Declining ARPU/Migrations • Downward migrations accounted for 52% of lost access revenue (48% loss from churn) • Upward migrations accounted for 37% of gain in access revenue gain (63% from new acquisitions) Churn • Relatively low churn rates (1.5%) • Most resources devoted to outbound retention campaigns Test Environment • Lack of clean, controlled environment makes product development slower, riskier, and lower impact • No proper understanding of offer value vs. return CVM Analysis Strategy/Results • Crated new transaction level (CDR) data sources, linked them with existing profile data bases • Reduced time to track impact of initiatives, greatly increased targeting precision • Analyze psychology effects of alternative stim offers and effects of training on multiple usage streams • Implement targeted offers based on observed stim in trial offers • Reviewed profitability of feature sales, targeted accordingly • Reprice reduced on feature sales by C$8M per year • Calculate revenue gain from alternative offers that replace downward migrations Analyze migration impact resulting from new acquisition offers • Generate recommendations for CSRs to avoid downward migrations where possible. Savings of C$14M per year Revise outbound acquisition strategies, avoided reprice of C$42M per year • • Enhance churn prediction model Calculate relative returns from outbound retention campaigns based on model predictions and inbound save offers • • Shift resources to inbound save efforts Saving of C$6 million per annum • Created cross functional team to launch and support small scale initiatives very quickly across all inbound and outbound channels • Executed 8 campaigns in short time frame Trained customer management resources on product development cycle, including feedback from CS and tracking results. • • • CVM activities in the postpaid segment focused on stimming low MoU customers, managing upward and downward migrations, improving customer retention and creation of ongoing test environment. Page 29
  30. 30. CVM Case Studies – Postpaid Mobile Industry Data Source Comparison Traditional Data Warehouse CVM Datamart • Aggregated over time • Processed / billed data • Individual call records, account profile changes • Highest possible level of granularity Frequency of update • Weekly, monthly • Delayed by bill cycle • Shifted across users depending on bill cycle • Twice a day • Date is absolute (not shifted in time) across users Ease of support and use • Accessible by any user through a simplified graphical user interface • Limited flexibility in creating new variables • Mostly used for reporting • 10+ times more data • Used by technically and statistically more advanced analysts • Very flexible • Mostly used for strategy definition Data Level To achieve the full potential CVM in the mobile telecoms market, near real time datasets at the individual transaction level need to be constructed and maintained. Page 30
  31. 31. CVM Case Studies – Postpaid Key Components of CVM Datamart Usage and Bill Data • Individual call records • No delay (up to 1 day) • Roaming usually not included • Prerated CDR (includes call type definitions, distance) Account Change Data • Individual account / user profile transactions - Activation - Deactivation - Migration between RPs, features • Creates near real time customer profile and historical profile by day Historical Data • Usually available from DWH • Up to 24-48 months of observations • Bill (usage & revenue) aggregates • Profile (Activation, rate plan, features activation/deactivation) • Information is delayed but 100% accurate and rich in history Lifecycle View Cluster has developed for its clients a CVM Datamart, which incorporates all customer transactions in a near real time format. Page 31
  32. 32. CVM Case Studies – Postpaid Data Foundation Real-time Datamart Needs Real-time Datamart System Architecture Switches 1 Real-time usage variables (for usage database) Postpaid User Profile Change Assign User Info Customer Service Prepaid 2 Each account transaction (for profile database) Voicemail Split M2M/ Remove Duplicate Pre-rating Daily Activity Log 1 2 Browser Feeds captured twice daily before billing Billing 3 User information for entire lifecycle Roaming Data warehouse (monthly summary of bill cycles) 3 • Usage database - 3-6 months of CDRs - 6-12 month of daily aggregates • Profile database - 12 months of real-time profile • Other data as needed - Irate calls to CS - External agency data (demographics) Update once per month DiamondCluster initially constructed the CVM datamart as proof of concept, then productionalized it later. Our CVM analysis also relied on historical data from bill line item based datawarehouse. Page 32
  33. 33. CVM Case Studies – Postpaid Existing Base Value Drivers • High breakage users have high churn rates • Usage declines prior to churn USAGE • Usage trends precede migration both upward and downward • Out of bundle revenues • LD • Roaming EXISTING CUSTOMER VALUE CHURN MIGRATION • Total value loss • Partial value loss As a result of our modelling of customer economics, we have centered our CVM analyses on usage, rate plan and feature migration, and churn. Page 33
  34. 34. CVM Case Studies – Postpaid Usage as a Predictor of Migration and Churn Usage before Migration Usage before Churn MoU Index (100) 130 100% Migrations Up Migrations Down 27% 120 75% 48% 48% 55% 55% 72% 110 50% 73% 100 25% 52% 52% 45% 45% 28% 90 0% 80 Months prior to migration Rate group 1 Months after migration Rate group 2 Rate group 3 Rate group 4 Rate group 5 Rate group 6 70 Usage drop in month 1 - 6 prior to churn 60 Usage in month 1-6 prior to churn compared to month 7-12 prior Month of Migration Notes: 100%is the average usage through month 7 - 12 prior to churn. Usage changes precede customer transitions. As observed at client, migrants up have usage stim of 13%, migrants down usage loss of 10%, and churners usage loss averaging 50% in the 6 months prior to status change. Page 34
  35. 35. CVM Case Studies – Postpaid Value Drivers – Modes of Use Theory No Association 80 60 40 20 0 1 min toll to 1.8 min non-toll - 3 6 120 100 80 60 40 20 0 150 DIGITAL Non-Toll Non-Toll Long Distance 9 12 15 18 21 24 27 30 33 36 39 No Association 1 min toll to 1.7 min non-toll - 3 Toll Minutes 80 1 min incoming to 1.2 min outgoing 40 20 1 min incoming to 3.2 min outgoing Outgoing Incoming 80 60 40 1 min incoming to 3.6 min outgoing 20 0 0 - 3 6 9 12 15 18 21 24 27 - Incoming Minutes 80 70 60 50 40 30 20 10 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 Incoming Minutes 1 min off-peak to 0.7 min peak 100 80 1 min off-peak to 0.8 min peak 1 min off-peak to 3.3 min peak Peak Off-Peak 9 12 15 18 21 24 27 30 33 36 39 1 min incoming to 1 min outgoing 100 60 6 Toll Minutes 100 Outgoing • Shift consumer psychology in two phases - Deeply discount usage features to encourage new modes of use - Customer gets in habit of making more calls, break association of expense with each call 120 100 Peak • Psychology is main hurdle to usage/ revenue stim - Mobile for safety only - Price perception vs. actual price Observations 150 ANALOG 60 40 1 min off-peak to 3.6 min peak 20 0 - 2 4 6 8 10 12 14 16 18 20 22 24 26 28 Off-Peak Minutes - 2 4 6 8 10 12 14 16 18 20 22 24 26 28 Off-Peak Minutes Changing the number of modes of use dramatically increases total usage, as customers begin to think of their mobile like their home phone. Page 35
  36. 36. CVM Case Studies – Postpaid Value Drivers – Mobile Browser Usage Low freq., 1-2 weeks Med freq., 3-5 weeks High freq., 6-10 weeks Browser MoU Before they started using the browser, high frequency users had declining MoU. After using the browser, they had the highest MoU stim. MoU/User 40 32 9 350 307 312 3 3 267 323 0.2 338 0.1 272 284 255 261 239 3 319 330 277 26 363 268 239 227 -3 -2 -1 1 2 3 Relative Month Notes: User base: 473 browser users started to use the browser in June - July cycles and who did not have ESN# change or migration within ±3 months from the time when first used the browser and has more than one browser call. MoU adjusted for seasonality. User base for seasonality indexes users who activated before Nov. 1999 and were active as of Sept. 2000, did not have and ESN change and did not activate the browser. All users who started using the mobile browser experienced voice stim in addition to the other, direct benefits. Furthermore this voice stim has proved to be stickier than the data minutes themselves for all data users. Page 36
  37. 37. CVM Case Studies – Postpaid Campaign Targeting Percentage of Users 20% 18.4% 19.2% 25.0% 12.1% 25.3% 15% 10% 5% 0% 0 Usage Pattern 10 20 30 40 50 60 70 80 90 100 110 120 Bundle Utilization Percentage 130 140 150 160 170 180 190 200+ High breakage Low breakage Action Sell subsidized/free usage features Sell full price/discounted VAS features Offer stretch features, other VAS (2) Upward migrate/Offer stretch features, other VAS Priority High Medium Low Low Expected Benefit • Reduced churn and downward migration • No risk of reprice • Some LD stim • Out of bundle usage revenue • Some LD stim Overage • Keep out of bundle usage revenue • Reduce churn of high value users • Keep out of bundle usage revenue • Secure higher access fee All campaigns have been carefully targeted on customer behavior, such as bundle utilization, to maximize effectiveness while avoiding reprice. Estimated in year EBITDA savings of C$8M per year. Page 37
  38. 38. CVM Case Studies – Postpaid Migration Importance – Value Compared to Activation/ Deactivation Downward Migration vs. Deactivation Access Value Number of Users Migrations Down Churn $-622,985 $-581,034 Upward Migration vs. Activation Number % of Total Value Change 44,600 52% 20,001 48% Access Value Number of Users Migrations Up New Users $987,384 $1,707,074 Number % of Total Value Change 35,732 37% 72,991 63% 45,000 35,000 Churn 40,000 Migrations 30,000 35,000 25,000 New Users Migrations 30,000 20,000 25,000 15,000 20,000 15,000 10,000 10,000 5,000 5,000 0 0 0 -10 -20 -30 -40 -50 -100 0 <-100 Drop in Access Charges Notes: 10 20 30 40 50 100 <100 Increase in Access Charges Data taken from CLUSTER migration model, based on May usage and access revenues. Includes prepaid rate plans. Migration direction defined by an increase/decrease in access revenue after the migration. Migration activity is a large value driver previously untracked. It represents 52% of all gains and 37% of all losses in access revenues. Page 38
  39. 39. CVM Case Studies – Postpaid Migration Addressability – Complexity of Combinations Ranking According to Number of Migration Events 38 rate plan combinations represent 80% of migrations 39% of expected revenue impact 120% Ranking According to Revenue Impact Last 1,234 rate group combinations contribute 24% of revenue impact, but are too small to analyze (less than 20 migrants per month 120% Must examine 186 rate plan combinations to include 80% of migrations and 80% of revenue impact 100% 80% % of Total Revenue Change % of Total Migrations 100% 1,272 Total Combinations for February 60% 12 rate plan combinations represent 61% of migrations 12% of revenue impact 40% 5 rate plan combinations represent 42% of migrations 9% of revenue impact 20% 1,272 Total Combinations for February 80% 60% 26 rate plan combinations represent 41% of migrations and 40% of revenue impact 40% 7 rate plan combinations represent 17% of migrations and 20% of revenue impact 20% 0% 0% 0 Note: 38 rate plan combinations represent 48% of migrations and 47% of revenue impact 500 1000 1500 Rate Group Combinations Sorted by Number of Migration Events 0 500 1000 1500 Rate Group Combinations Sorted by Revenue Impact Expected revenue combination based on differences on average ARPU per plan. While total migration activity is complex the distribution of effects is highly skewed. Approximately 3% of migration combinations could provide 80% of the migration events or 39% of total access revenue created and lost. Page 39
  40. 40. CVM Case Studies – Postpaid Migration Example: Policy Recommendations Description Key Segment Affected Outbound Inbound Prevent Certain Migrations • Impose fees or future date all downward migrations to prevent abuse through multiple migrations • Do not call • • CS policy Systems issues on validity of future dated transactions Substitute Certain Migrations • Instead of allowing customers to downward migrate, give them a free feature and secure the higher access fee Example: instead of 400 to 200, 400 to 400 with free feature • Do not call • Recommendation engine for targeting Systems issues: free feature — Rate Package lock Recommend customers a rate plan which is more beneficial to the company and to customer Example: move customers from old rate package to new rate package • Instead of contacting customers individually — slow and expensive — move them to a new rate plan automatically Flashcut those users on Flex with long term average less than 50 • Changing the migration policy would cause too high churn risk Example: Digital North America / Real Time Canada where migration reprice is significant, but churn risk is even higher • • • Shift Customers to Certain RPs • • Flashcut • • Leave Intact • • • Target certain outbound migrations based on feature sales • Only accomplished where in year revenue constraints are met Recommendation engine for finding ideal plan or targeting • Recommendation engine for targeting and offer design Do not call • Fulfill Requests • Recommendation engine for targeting and offer design Stretch features for upsell None of these tactics are universally applicable, but on a targeted basis they can address the majority of migration reprice, saving C$14M per year. Page 40
  41. 41. CVM Case Studies – Postpaid Acquisition Reprice • Final recommendation was to match only on certain rate plans, limiting reprice • Result was an expected savings of $26M annual EBITDA $50 $40.28 $25 Reprice ($M) $15 $10 $5 $0 ($5) ($10) ($15) ($20) ($25) ($30) 80 60 40 20 0 -20 -40 -80 • By analyzing actual reprice on the existing base, calculated that it would require a 3% increase in market share to compensate for the expected reprice -100 • Initial reaction was to match competitor clock across entire base 16% 14% 12% 10% 8% 6% 4% 2% 0% In year rev. impact (reprice) $M) • Competitor changed off-peak clock, beginning off-peak at 6 PM instead of 8 PM % of Subs In year revenue reprice (annual) % -60 Background % better offer new plan (assuming 20% of users with 10% or more better off switch) $11.94 $12.91 $16.40 $0 Original idea matching across the base Alternative A (match on OP plans) Alternative B Alternative C (match on OP (match on OP, + RT plans) RT and flashed old) By analyzing the expected reprice using CDRs, saved Bell Mobility an expected $26M from avoided reprice. Page 41
  42. 42. CVM Case Studies – Postpaid Churn — Difficulty with Outbound Campaigns Deactivation Impact Deactivation rate 6% 5% ARPU Impact During the period between pull and mailing 13% of both the target and control group deactivated implying late action on save attempt 5.5% ARPU $155 $153 Target Control $150 5.2% 3.7% 4% 3.6% 3.6% 3.4% $152 3.2% 1.9% 2% 1.7% 1% 3.4% $146 $143 $145 3.2% $144 2.9% 3% Target Control 3.5% 2.6% $140 2.5% $130 0% $137 $142 $141 $142 $137 $135 Campaign launch $142 $140 $140 3.0% Peak in deactivation rate 2 months prior to campaign suggests outdated data $143 $141 Reduction in ARPU indicates that high value users churned at higher rate. $135 Campaign launch $125 Mar Apr May Jun Jul Aug Sep Oct Mar Apr May Jun Jul Aug Sep Oct The targeting difficulties on outbound churn campaigns have driven poor actual results, contrary to carrier’s previous perception. Page 42
  43. 43. CVM Case Studies – Postpaid Churn — Outbound Loyalty Funnel Illustrative Existing postpaid consumer base (1.0M users) Targeted users based on predictive churn model score calls (96,000 users per month) Nonchurners 910,000 93% of users taking up the offer, however, are non-churners over next six months Contacted users taking up offer (25,920 users) 70,080 18,921 Users remaining on network after 6 months (21,566 users) 18,921 1089 1,400 Churners or potential churners over next six months 6,999 25,920 90,000 Targeting Process Contact Offer Process • Predictive churn model • Call center support ~100,000 users/month • 1.5% monthly churn in base • 4.5% monthly churn in list • RPC rate of ~30% • Uptake rate of ~90% • Assumes equal RPC and Uptake for churners and non-churners 5,599 Realized Save Rate Customers who churn despite loyalty offer • Save rate of 20% for churners In almost all outbound loyalty programs, the majority of users taking up a retention offer are not actually churners, limiting total returns. Page 43
  44. 44. CVM Case Studies – Postpaid Churn — Channel Economics Outbound Illustrative Inbound retention Winback Assumptions: C$45 ARPU, saved users remain on network for 12 months, C$5.00 per contact outbound, C$4.00 inbound • Targeting: - 27% churners over 6 months in lists • Offer Uptake Rate: - 90% • Save Rate: - 20% • Cost of Contact: - $6.70 per contact • Targeting: - 60% of callers are churners • Offer Uptake and Save Rate: - 100% for non-churners - 25% for churners • Cost of Contact: - $0.00 per contact • Targeting: - 100% of those called are churners • Offer Uptake and Save Rate: - 5% • Cost of Contact: - $6.70 per contact • Give away revenue breakeven: $2.31, 5% of ARPU • Give away revenue breakeven: $12.50, 28% of ARPU • Give away revenue breakeven: $33.50, 74% of ARPU Move away from migration offers to feature offers Room to enrich offers depending on results Increase investment depending on observed results for targeted winback segments Notes: Monthly revenue saved is multiplied by 9 months (since churners would leave in an average of 3 months); Give away cost lasts for 12 months, 3 months for churners who accept the offer. Inbound and winback efforts, however, can show substantialy higher returns due to their inherent targeting benefits. By shifting resources to the inbound channel, we improved in year EBITDA by C$6M. Page 44
  45. 45. CVM Case Studies – Postpaid Test Environment — Description Definition: Launch inbound and outbound campaigns on a small scale in a clean, single offer environment with precisely controlled execution across multiple channels, using CDR level data for rapid return tracking for each variation tested. Typical Process • Due to large scale approval and production process is lengthy • Reading results from bills delays campaign performance evaluation by 2 - 3 months • Easy to hit extremes of either rich offer with high risk of reprice, or less attractive offer with high marketing cost per take-up • Usually not at all or not properly measured. • Lack of hypothesis testing at offer design usually results in neutral or negative return • • • • Overlapping campaigns Improperly defined control groups Improper return calculation Limited feedback from tracking or CS into new offer hypotheses Area of Impact Time to Market Risk of Reprice Expected Returns Customer Management Process Test Environment • Due to small scale and cross functional team offers are launched very quickly • Due to single offer environment and access to CDR level data results are available in 2 3 weeks • As a result of the small scale and the testing of various offers the reprice risk is limited and is known in advance • Hypotheses driven design improves returns • Correctly measured returns are available very quickly • Sensitivity and elasticity information is also available • Complete cycle of hypothesis generation, testing, tracking, feedback prior to broad based launch • Knowledge handover from DiamondCluster through on the job training The test environment is operated by a cross-functional team to ensure that test initiatives can be launched on a small scale with short turn around and proper return tracking. Page 45
  46. 46. CVM Case Studies – Postpaid Test Environment — Benefits All Departments Realized Immediate Benefits... • Marketing benefits from increased creativity and stronger business cases in low risk environment • Finance benefits from selecting only the most profitable campaigns from those tested, and avoiding any netnegative campaigns • Database marketing benefits from easier environment to track results • Customer care benefits from fewer marketing initiatives for non-test customer care advocates, and an opportunity to provide feedback on what works and what does not …and in the Long Term, the Product Development Process Flow Was Improved GENERATE HYPOTHESES • Develop detailed hypotheses on how specific products offered through specific channels to targeted subscriber groups will impact profitability - How channel of communication affects take-up rate - How certain offers impact postcampaign behavior (churn, migration, usage) TEST HYPOTHESES • Design specific test to confirm initial hypotheses - Vary offer and channel as needed to gain significant results - Establish a control group of statistically significant size, and isolate target and control group from all other campaigns ANALYZE RESULTS • Track churn, migration, and usage impacts to determine overall impact on profitability By creating a test environment, DiamondCluster built a testing mentality within the organization which improved the product development process. Page 46
  47. 47. CVM Case Studies – Postpaid Test Environment — Improved Targeting Daily Tracking Take-Up Date: April 20, 2000 Weekly Tracking 342 users taking Afterhours at Free 342 users taking Afterhours at Free Seonds / user / day (indexed based on before avg.) 700 seconds / user / day 600 500 400 300 200 100 peak 300 250 evening peak 200 weekend 150 100 50 0 0 1 2 3 4 5 6 Week Relative to Take-Up 7 8 9 weekend Weekend 183% Evening Date relative to take-up date evening 350 -3 -2 -1 66 60 54 48 42 36 24 30 18 6 12 0 -6 -1 2 -2 4 -18 0 400 168% Peak Notes: Graph shows daily variation of 342 users who took AH free All users shifted to same relative take-up day (day zero) Graph shows usage in terms of seconds -4% Notes: Graph shows daily variation of 342 users who took AH free All users shifted to same relative take-up day (day zero) Graph shows usage indexed to before avg. (i.e. avg. of weeks -3 to -1 = 100) In this example, a tested free off-peak product, targeted at high breakage users, led to weekly usage stim of greater than 100% with no reprice. Page 47
  48. 48. CVM Case Studies – Postpaid Test Environment — Tracking Results Usage Churn % Stim Migration % Churn % Migration Downsell Control Down Upsell Control Up Attempts 30% Attempts Control 2.0% Control 8.0% 7.0% 22% 6.0% 20% 5.0% 1.0% 4.0% 3.0% 10% 2.0% 2% 0% 1.0% 0.0% Hardware Upgrade # Notes: 439 (62 take-up) 3008 0.0% Hardware Upgrade # 439 3008 Hardware Upgrade # SAME AS CHURN Usage stim is avg. of 29 days after take-up compared to 29 days before take-up. Includes all usage. Both churn and migration compare all attempted contacts to a control group. Migration chart includes migration events past the CS induced migration. In order to establish complete and accurate metrics, tracking incorporates usage, churn, and migration impacts. Page 48
  49. 49. Agenda Introduction Background on CVM CVM Case Studies from Bell Mobility • Background • Prepaid • Postpaid Engagement Structure Page 49
  50. 50. Engagement Structure CVM Project Phasing and Resources Project Scope/Deliverables: • Review and analyze sample client data feeds • Illustrate key existing base trends based on sample • Provide detailed assessment of time/budget to build productionalized Cluster analysis tool, provide ongoing base analysis and marketing support • Relevant examples of analysis tool output from other projects Phase 1A Initial Diagnostic & Phase 1B Testing 3 Month Engagement Phase 2: Proof of Concept for Tool Project Scope/Deliverables: • Develop analysis engine using client real time feeds • Use engine to create new finance revenue/profitability reports • Customer analysis to understand user behavior, micro offer opportunities with expected benefits for implementation • Test offer implementation • Productionalized analysis engine Phase 3: Implementation Project Scope/Deliverables: • Integrate Cluster analysis engine with campaign management/tracking tools, rules based recommendation engine • Implement series of targeted offers previously identified • Track results and refine offers • Provide detailed financial reports on value created Resources: • Approximately 4-6 persons (DCI) • Approximately 2 client resources from department/division under study Resources: • 4-6 persons (DCI) • Approximately 4 client team mambers from marketing, sales, finance, IT and CS 3-5 Month Engagement Resources: • 4-6 persons • 6+ fully dedicated internal resources from marketing, sales, finance, IT and CS 6-12 Month Engagement A pilot consists of 3 months to construct an initial diagnostic and testing. Page 50
  51. 51. Engagement Structure Project Team Structure Project Design/Management Office • Support • Management • Management • Design • Coordination • Education Work Steps IT Data Feeds/ Construction of Variables IT/CS/Systems Functions Using Variables/Reports Finance Offer Design/Implementation CS/Systems Tracking • Data modelling Other Functions/ Departments Infrastructure Marketing Team CS/Finance • Database marketing • Customer loyalty • Turnover prevention • Other functions Internal project dependencies Feedback and improvement loops CVM can only be successful through cross-department planning and collaboration, with marketing in the coordinating role. Page 51
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