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Case Studies and Value Propositions Telco

Discover how Norkom AlchemistTM delivers actionable intelligence to Telco businesses, improving customer campaigns and reducing churn.

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Case Studies and Value Propositions Telco

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  1. Case Studies and Value PropositionsTelco

  2. Alchemist in Action – Case Studies Telco’s “Norkom AlchemistTMdelivers 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 capabilityof 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

  3. 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

  4. Case Study – Norkom & (Digifone) • Delivering Success with our Clients • 45% market share achieved • Digifone’s 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 (> 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 AlchemistTMdelivers actionable intelligence to our business faster, putting customer information directly where it is needed, making our campaigns more effective” – Ken Henson IT Director “Norkom’s Customer Segmentation work is really excellent, and is already providing us with real business benefit” – Roy Gillingham CRM Director

  5. 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

  6. 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%.

  7. How Norkom helps 02 to manage their customers

  8. Control risk Understand ourcustomers better Offer better services Improve our Business processes We collect and interpret it intelligently …and use it to… Introduction We have lots of customer data

  9. Our Systems Main Sources of Customer Data Network Billing Dealers Our Customers Other KnowledgeSources DataWarehouse Data Marts

  10. Credit Scoring • Scorecard Monitoring • Differentiated by Type of Customer • Used to Determine Service Offerings • Fraud Prediction • Dealer Customer Watching - Acquisition • TeleSales / Dealers • External data • Knowledge Base • Contact History

  11. Profitability Segmentation • Bottom Line Profit/ Cost Modelling • Product Profitability and Usage • Products Customers are/are not using • Identify Cross and Up Sell opportunities • Partner Product Usage Customer Watching - Understanding • Behavioural Segmentation • Usage Based • Enables Operational Focus on High Value Base

  12. Risk Management • Collections Process • Fraud • Customer Satisfaction • Quarterly Surveys / Focus Groups / Product Trials Customer Watching - Understanding • Ongoing Promotions • Event driven SMS Campaigns • Upgrades

  13. Winback • Information sent to Winback Team Customer Watching - Retention • Churn Management • Predictive Campaigns • Outbound Call Centre Activity • Direct Mail Activity • SMS • Separate Post and Prepaid Models • Model Performance Refinement

  14. Case Study Churn in Fixed Line International Telco Provider

  15. 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 Hvalue 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)

  16. Risk Value Matrix in combination with campaign cost

  17. Cost of campaign Retained Revenue Yearly Profit Full ROI calculation

  18. Case Study Churn of Business Customers in Fixed Line Telco

  19. 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

  20. 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

  21. CASE STUDY : Value Segmentation (MEA Telco)

  22. Churn/Non-Payment Model and Customer Value Segmentation

  23. Modelling – Value Segmentation 0

  24. 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

  25. 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?

  26. 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.

  27. Churn/Non-Payment Influencing Factors Invoice Amount • Interpretation • Customers with highest invoice amounts are least likely to pay • Recommendation • As previous slide

  28. 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 aren’t using mail. Encourage use of voice mail and other services.

  29. 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.

  30. 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%.

  31. Case Studies and Value PropositionsGovernment

  32. 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

  33. Example Data Model

  34. 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

  35. Case Studies and Value PropositionsBanking

  36. 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

  37. Case Study Cross-selling for a Bank Insurance Company

  38. AXA Model performance

  39. ROI estimation – The parameters

  40. ROI estimate – Fixed Campaign Budget

  41. Internet CustomerAcquisition and cross-selling

  42. Case Study : Customer acquisition insurance-bank Initial Business Objectives Improveclient’s 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)

  43. 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 mailexpansion campaign: using model prediction to choose top 4,500,000 prospects, i.e. the top 25% of households in 7 new areas (ongoing)

  44. Customer acquisition model:Contribution 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

  45. 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 $

  46. Fraud Detection

  47. 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

  48. 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

  49. BMO – Phases 2, 3…n • 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

  50. ROI Calculations

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