Case Studies and Value Propositions Telco

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Case Studies and Value PropositionsTelco
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Alchemist in Action Case Studies Telcos
Norkom AlchemistTM delivers actionable
intelligence to our business faster, putting
customer information directly where it is needed,
making our campaigns more effective Ken
Henson IT Director
Early last year we began running customer
retention campaigns across our key customer
regions, using the predictive capability of
Alchemist. This effort has allowed us to reduce
churn dramatically over the last 12 months.
Brian Curran, Director of Marketing
We have been very impressed by Norkom's
Professional Service, outstanding level of
commitment and quality, as well as the ability of
Norkom to make Alchemist quickly evolve to meet
our requests." Kurt van Kleemput, Market
Intelligence Mgr
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Case Study Norkom (Digifone)

• In Summary
• Over 70 projects delivered by Norkom using
  Alchemist our Consulting services
• Example projects include
• Campaign formulation support
• Customer Segmentation
• WAP Profiling
• Churn Management
• Credit Scoring
• ScoreCard solution
• X Sell programmes aimed at moving PrePaid to
  Contract
• Multiple Campaign Evaluation
• Customer Business Intelligence infrastructure
• Sales Marketing Intelligence Workbench
• Finance Intelligence Workbench

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Case Study Norkom (Digifone)

• Delivering Success with our Clients
• 45 market share achieved
• Digifones corporate clients represent 75 of the
  Business and Finance Top 100 Index corporates in
  Ireland
• Profitable, quality customer base
• Consistent recognition thru Customer Service
  awards
• History of increasing ARPU and now stabilised
• Bad Debt running at less than 4 (no Credit
  Bureau)
• Churn reduced substantilally to less than 18
• Significant UpSell success re WAP Useage
  campaigns (gt 15 response rate)
• SMS WAP useage continuing to grow
• Segmentation of client base completed on both a
  revenue profitability basis
• Campaign Evaluation system in place (e.g PrePaid
  customers)
• Dealer Management programmes in place
• Multiple changes to product offerings (tariffs,
  new products, etc) rolled out
• Complete Customer Market Intelligence platform
  in place

Norkom AlchemistTM delivers actionable
intelligence to our business faster, putting
customer information directly where it is needed,
making our campaigns more effective Ken
Henson IT Director
Norkoms Customer Segmentation work is really
excellent, and is already providing us with real
business benefit Roy Gillingham CRM Director
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KEY PERFORMANCE INDICATORS

• Customer Care Service Levels
• Connections
• Disconnections
• Activations
• Churn Real and Predictive
• Credit Collection Processing
• Managing Outbound
• Budgeting
• Metrics from other departments
• Scorecard
• Administration Backlogs (Customer Care)
• Customer Call Profile Analysis

• Supply Chain Management
• Performance by Sales Channels
• Customer Management
• Customer Acquisition
• Payroll
• Human Resources System
• WEB Alerts
• Network Monitoring
• System Monitoring
• Prepaid
• Promotional Analysis
• Usage Patterns

• Regulatory Info
• Interconnect Data
• Interconnect Revenue
• Roaming
• Unit Based Measurements (currently Call Minutes)
• Discounts
• Commission
• Package Migration
• Aged Debt Analysis
• Product Performance
• Asset Analysis
• Costs - Planned Vs Actual

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Credit Scoring Overview
Setting Cut-offs
Maintain current bad rate (5.0), set cut-off at
626. Accept rate will be 88.0.
Maintain the current accept rate (80.0), set
cut-off at 651. Bad Rate will be 3.9.
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How Norkom helps 02 to manage their
customers
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Introduction
We have lots of customer data
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Our Systems Main Sources of Customer Data
Network
Billing
Dealers
Our Customers
Other KnowledgeSources
DataWarehouse
Data Marts
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Customer Watching - Acquisition

• TeleSales / Dealers
• External data
• Knowledge Base
• Contact History

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Customer Watching - Understanding

• Behavioural Segmentation
• Usage Based
• Enables Operational Focus on High Value Base

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Customer Watching - Understanding

• Ongoing Promotions
• Event driven SMS Campaigns
• Upgrades

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Customer Watching - Retention

• Churn Management
• Predictive Campaigns
• Outbound Call Centre Activity
• Direct Mail Activity
• SMS
• Separate Post and Prepaid Models
• Model Performance Refinement

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Case Study

• Churn in Fixed Line International Telco Provider

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ROI on High Risk / High Value Customers

• Model Type 1 suggests to target HRHV group in
  March using Feb data
• 10,858 customers in HRHV /106,009 in High Value
  group
• 526 churners in HRHV /1,495 in High Value group
  are captured in 5.5 months 35 H value
  churners identified by model
• Random selection for capturing the same number
  (35) of churners within 106,009 H value
  customers
• 37,298 customers
• Gain in using model prediction compared to random
  selection on High Value ONLY
• 37,298 10,858 26,440 customers every 5.5
  months
• 28,844 (estimation) per every 6 months (1 per
  action-customer)
• Total Gain of Using the Model by Year 57,688
  (estimation)

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Risk Value Matrix in combination with campaign
cost
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Full ROI calculation
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Case Study

• Churn of Business Customers in Fixed Line Telco

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Issues at stake

• Annual lost revenue 3.844m
• Commissions to replace lost customers 70.00 per
  customer 1,656 115,000.00
• Set up fees 30.00 per customer 1,656
  50,000.00
• Disconnection processing 30.00 per customer
  1,656 50,000.00
• Average marketing acquisition spend 350.00 per
  customer 1,656 580,000.00
• Total Annual Cost Churn 4.639m

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Potential savings

• Churn has been reduced by by 10 in first
  exercise (further exercises reduced it to up to
  40.)
•  
• This has reduced the churn from 138 per month to
  124 per month, i.e. by 14 customers per month or
  168 per year.
•  
• This has a bottom line impact of
•  
• Revenue lost savings 3.844m - 3.454
  390,000.00 per year.
• Commission 70.00 per customer 168 12,000.00
• Set-up fees 30.00 per customer 168
  5,000.00
• Disconnection processing 30.00 per customer
  168 5,000.00
• Average marketing acquisition spend 350.00 per
  customer 168 59,000.00
•  
• Total Annual Potential Savings 471,000.00
• Total Potential Savings over 3 years 3
  471,000.00 1.413m

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CASE STUDY Value Segmentation (MEA Telco)
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Churn/Non-Payment Model and Customer Value
Segmentation
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Modelling Value Segmentation
0
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Value Segmentation Influencing Factors
Hotline Usage

• Interpretation
• If hotline is used, customers likely to be high
  value

• Recommendation
• Further discussion of this service should be
  considered

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Value Segmentation Influencing Factors
Disconnection Reason

• Interpretation
• High value customers disconnected due to
  non-payment. Fraud? Call Selling?

• Recommendation
• Further discussion. Re-assess credit management
  policy?

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Value Segmentation Influencing Factors
Last Bill Sequence Number

• Interpretation
• Older customers are more likely to be high value

• Recommendation
• Discuss campaigns to lock-in customers. Work
  towards true lifetime value measures.

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Churn/Non-Payment Influencing Factors
Invoice Amount

• Interpretation
• Customers with highest invoice amounts are least
  likely to pay

• Recommendation
• As previous slide

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Churn/Non-Payment Influencing Factors
Voice Mail Access Calls

• Interpretation
• Customers accessing voice mail are more likely
  to pay/stay with Click

• Recommendation
• Examine why people arent using mail. Encourage
  use of voice mail and other services.

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Churn/Non-Payment Influencing Factors
Customer Service Calls

• Interpretation
• One of the keys to increased customer loyalty is
  increased customer interaction

• Recommendation
• Discussion around what types of customer service
  calls these are. Increase customer interaction at
  all points of contact. Invest in automating and
  monitoring.

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Lift curve for Churn/Non-Payment Model
Reading from the blue line, the lowest 10 of
scored customers identifies 85 of the
non-payers a lift factor of 850.
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Case Studies and Value PropositionsGovernment
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Analytical Opportunities for the Inland Revenue

• Exploit the richness of the Transaction and
  Portal data
• Understand the mechanics of behaviour
• Detect abnormal behaviour using robust techniques
• Profiling the behaviour of builders,
  subcontractors
• Understanding links between contractors, builders
• Look for hidden voucher dealing rings
• Investigate Watchlist behaviour
• Perform more complex analyses than is possible if
  done manually
• Feeds into rules process and KYT/KYS (Know Your
  Taxpayer/Subcontractor)
• Automate process of discovery
• Introduce common practices
• Reduce cost of manual exploration

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Example Data Model
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Regulatory Body Financial Watchdog

• Leading Regulatory Body in Europe
• Focused on International banking, Domestic
  Banking, Independent Financial Advisors, On Line
  Brokering and Credit Unions
• Alchemist focus areas are Market Abuse and Fraud
• Engagement is subject to Confidentiality

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Case Studies and Value PropositionsBanking
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Challenges for the Financial Services Industry

• Drastic measures to improve results include
• Only initiate projects that improve efficiencies
• Reduce costs
• Improve competitive position
• Norkom adresses these issues
• Considerable improvements of results of marketing
  campaigns, doubling of conversion rates
• Automate marketing processes
• Reduce marketing efforts by better targeting
• Use intelligent customer interaction as a
  competitve weapon
• Easy step in, ROI-based approach, flexible
  solution

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Case Study

• Cross-selling for a
• Bank Insurance
• Company

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AXA Model performance
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ROI estimation The parameters
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ROI estimate Fixed Campaign Budget
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Internet CustomerAcquisition and cross-selling
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Case Study Customer acquisition insurance-bank

• Initial Business Objectives
• Improve clients new customer acquisition efforts
  by way of better-focused and targeted campaigns
  through the use of predictive models.
• Identify potential households who were likely to
  open an Orange Savings Account
• Actions in the first two areas of expansion into
  the U.S (NY city and Philadelphia)

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Customer acquisition insurance-bank (II)

• Objective achievement

• Model building based on combination of
• Customer database (account households), only two
  main areas (21,326)
• External demographic and lifestyle data for
  completing info on customers and prospects
  (103,236)
• ?Direct mail campaign Model applied to a
  population of 9.3 million and chose top 5, i.e.
  450,000 prospects in NY and Philadelphia
  (completed)
• ? Direct mail expansion campaign using model
  prediction to choose top 4,500,000 prospects,
  i.e. the top 25 of households in 7 new areas
  (ongoing)

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Customer acquisition modelContribution of top
10 variables

• Credit card ranking
• Number of credit cards
• Transaction type of first mortgage
• Purchase year of home
• Mail order donor
• Number of adults
• First mortgage type
• Tenure of first mortgage
• Projected home insurance purchase amount
• Dwelling unit size

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Test Campaign performance

• ?Target population
• Control group, random selection 50,000
  prospects
• Model selection, Top 5, 450,000 prospects
• ?Campaign result
• Control group response rate 0.78
• Model top scores response rate 1
• An increase in response rate of 28
• Model acquired customers 4500
• of mailings with random selection 577,000
• Additional cost with 3.5 455.000

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Fraud Detection
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Fraud Management Client Example

• Operations in USA Canada
• Assets of over 250 Billion
• Rolling out Alchemist Project started in 2002
  and runs through until 2005
• Volumes exceeding 12 Million transactions daily
• Alchemist is the backbone infrastructure for
  corporate wide Fraud Management solution
• Engagement is subject to confidentiality

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BMO Phase 1 Scope

• Initial portfolio areas which are covered include
• AML
• Debit Cards
• Credit Cards
• Skimming
• Kiting
• Devices
• Branch e-Banking addressed
• Combination of batch and near Real Time operation
• First area LIVE in 2002

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BMO Phases 2, 3n

• Extended Harvesting areas
• Identity Management
• Access Behaviour
• Device Analytics
• Enhanced Notification techniques
• Action Tools
• Client Impact Management
• Case Book Management
• Liasion Tools
• Scenario Management
• Risk Tracking
• Performance Management
• Management Tracking

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ROI Calculations
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Increase Revenue, ARPU Margins
Customer Case Study
BUSINESS CHALLENGE
Increase volume of profitable business with same
marketing budget
AT A GLANCE

• 2,500 call center agents
• One million calls a month
• 50,000 emails per month
• Significant cost savings expected due to
  decreased agent time per call

• Revenue generated per campaign increases between
  2,5 and 3,5 times (lift curves of models between
  4 and 5 times better than random)
• Increase conversion rates of campaigns through
  correct segmentation and personalised messaging
  by 50 to 70
• Hugely successful SMS, roaming and WAP up-sell
  campaign
• Estimated impact on bottom line 3m pa
• Estimated ROI on initiative 300 pa

• Click to edit Master text styles
• Second level

SOLUTION
Alchemist Customer Intelligence Campaign
Management
BENEFIT

• Use modeling lift curves to increase campaign
  effectiveness substantially
• Use insight to personalise campaigns
• Ultimately increase lifetime value of the
  customer
• Designed up sell campaigns, executed through the
  call centre
• Event based campaigns

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Credit Scoring
Customer Case Study
BUSINESS CHALLENGE
Increase acceptance rate by 10 and reduce bad
debts among contract client customer base
AT A GLANCE

• 2,500 call center agents
• One million calls a month
• 50,000 emails per month
• Significant cost savings expected due to
  decreased agent time per call

• Initial project delivered results within 8 weeks
• Automated system implemented with 12 weeks
• Bad debt rate at 4. this is among the lowest in
  Europe, and is lower than its peers in the home
  market
• Acceptance rate is 88
• Estimated impact on bottom line 2m pa
• Estimated ROI on initiative 320 (in first 12
  months alone)

• Click to edit Master text styles
• Second level

SOLUTION
Alchemist Customer Intelligence - Scorecard
BENEFIT

• Improve understanding of behavioral
  characteristics which lead to bad debts
• Use insight to change application process
• Ultimately increase acceptance rate (and revenue)
  and decrease the financial impact of bad debts
• Self updating system, automatically rescoring
  every three months

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Revenue Assurance
Customer Case Study
BUSINESS CHALLENGE
Increase billable volume and reduce financial
losses through more effective revenue assurance
AT A GLANCE

• 2,500 call center agents
• One million calls a month
• 50,000 emails per month
• Significant cost savings expected due to
  decreased agent time per call

• Iterative solution deployed, phase one completed
  within 8 weeks
• Actionable results within 8 weeks, with further
  phases adding to the wealth of information
  available to Finance Revenue Assurance Personnel
• Reduced losses through Revenue Assurance
  considerably
• Estimated impact on bottom line 6.2m
• Estimated ROI on initiative 480

• Click to edit Master text styles
• Second level

SOLUTION
Business Intelligence
BENEFIT

• Delivered Revenue Assurance KPI information on a
  next business day basis
• Delivered key Interconnect billing summary
  information (both for revenue and expenditure)
• Ultimately increased revenue and decrease
  financial losses to operator

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Customer Segmentation
Customer Case Study
BUSINESS CHALLENGE
Increase effectiveness of above the line
advertising and campaigns
AT A GLANCE

• 2,500 call center agents
• One million calls a month
• 50,000 emails per month
• Significant cost savings expected due to
  decreased agent time per call

• Click to edit Master text styles
• Second level

• 1m records analyzed across Contract, SME and
  Prepaid Customer Base
• Actionable results within 6 weeks
• Results presented to group Marketing board
• Project completed within the previous 8 weeks,
  campaign qualification figures indicate increase
  of conversation rates of 2.8, annual savings up
  to 15 Mio

SOLUTION
Alchemist Customer Intelligence (segmentation)
BENEFIT

• Improve understanding of customers, their
  behavior and their value
• Use insight change business process to focus on
  key segments across Contract, SME and Prepaid
• Designed campaigns across all mediums
• Knowledge transfer to client

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Business Intelligence
Customer Case Study
BUSINESS CHALLENGE
Decision makers unable to access key business
information in a timeline or reliable manner
AT A GLANCE

• 2,500 call center agents
• One million calls a month
• 50,000 emails per month
• Significant cost savings expected due to
  decreased agent time per call

• Click to edit Master text styles
• Second level

• Complete Business Intelligence system built with
  20 weeks
• System loads over 15m records per day, and
  retains 3 months of detailed history
• New data sources continually being added
• Existing data mart systems being decommissioned,
  reducing total cost of ownership
• Allowed client to initiate cross sell and channel
  migration campaigns
• Estimated ROI on initiative 100 (after 6 months)

SOLUTION
Business Intelligence Implementation
BENEFIT

• Improve understanding of customers, their
  behavior and their value
• Allow key decisions to be taken using up to date
  and reliable information, from a central
  repository
• Facilitate business planning, campaigns and what
  if analysis
• Reduce overall information cost of ownership