Financial Impact of Business Intelligence Investments

Transcription

1 Financial Impact of Business Intelligence Investments Simon Marland Retail Chief Information Officer Nedbank SA Retail SAS Forum International Lisbon June 2005

2 Index Background & Introduction Fundamentals Model Enablers Some Key Metrics The Customer Centric Data Model Example of integration and owning the value chain

3 Background One of the four largest banks in South Africa Branded Businesses: Nedbank, Old Mutual Bank, Pick n Pay Go Banking, Nedbank Corporate, American Express in South Africa 2004 Balance Sheet ~ US$ 45 billion 2001 Reported Profits ~ US$ 0.5billion 2002 ~ 12% of profits 2003 a complicated story, but closer to 20% of the core Approx 3.1 million customers for Nedbank Retail 3

4 Where I Fit in to the Nedcor Group Centralised IT models fail for the following reasons: Not close to business solutions Are treated as projects, The business benefit is not understood and generally gets lost in the IT-isms! A federal model can work as: It adheres to IT standards, but business benefit come first. It is allowed to work outside IT governance while fitting into certain standards. It is important to be a mature adult! 4

5 Index Background & Introduction Fundamentals Model Enablers Some Key Metrics The Customer Centric Data Model Example of integration and owning the value chain

6 Business Intelligence

7 Analytics 2002 Analytics 2003 Analytics 2004 Business Intelligence Evolution Consolidation of MIS Purchase of Tools Split of BIU into CA & PA Transactional / Customer Compentency Centres created Data Discovery Upskill of People Employment of Bus. Consultants Profitability / Enablers Marts Built. Pricing HL / ABF added Analytics / Insight Focus MIS moved to Warehouse Foundations Finished Bis Models Built Value-Chain Leadership Business Intelligence Value Chain Data MIS Analysis Insight Decision Decision -Tracking Warehouse 2002 Non-Delivery of BI from T&O Purchase of SAS DQ & WA Employment of W/H team Corner stone Client View Mart Dedupe Key CRM Warehouse 2003 BIZ Tools Bought 5 4 5! Infrastructure Investment Credit Mart delivered Middleware R100m Balanced Scorecard Warehouse 2004 Delivery Tools Built 28 Marts World class Design Financial Delivery Sweat the tools

8 Ownership & the Value Chain Analytics Back Office/ Call Centres Relationship Manager 8

9 The No. 1 Enabler LEVEL 3 Value Integration Analytics Solution Marts B.I. Mgt / Vision / Strategy LEVEL 2 Biz Analytics, Mart Production Integration / Solutions LEVEL 1 Information Data Competency / Skills need to continuously evolve as software / Hardware evolve and we move up the Value Chain! Value Value add add = Profit Profit Software needs to be the latest and greatest as to enable productivity and stress levels to be better for the people. Otherwise you increase the number of people needed. Hardware is a major factor in motivation and productivity. Hardware can become extremely expensive if foundations and understanding of what and where we are going to be in the future is not understood. People Software Hardware

10 High-level architecture infrastructure

11 The Data Warehouse (User Enablement) Data marts supporting web applications 6000 users 200 users 3000 users 400 users 200 users All emps 375 users 200 users 3000 users 30 users Aprimo Enact CMS Finance & FTP AML DQ Client Info Unit HL Originator s CAN BSC RDW Production Development

12 Index Background & Introduction Fundamentals Model Enablers Some Key Metrics The Customer Centric Data Model Example of integration and owning the value chain

13 Value Extraction = $$$$ s Analytics Model 33% CRM / Value Prop 33% Integration 33%

14 Enabled CRM Environment - Connecting the Dots Web Services Enabled Financial Experience Alpha TP Systems Portal with Embedding to Customer Experience Legacy TP Systems ODS StaffWare ecrm Sales/Customer Management Portal Web Services Integration Aprimo Messaging Systems SMS FAX Mail Merge etc (Operational Data Store) Information Centre(IC) SAS Warehouse (Note 2) 14

15 Churn Analysis Retail Optimum, N5000 and Ordinary CA Closures Churn & Attrition Modelling 3.00% % Active Accounts Closed 3.50% 2.50% 2.00% 1.50% 1.00% Uncontrollable Controllable 0.50% 0.00% Model 30% Jul-03 Aug-03 Sep-03 Oct-03 Nov-03 Dec-03 Jan-04 Feb-04 Mar-04 Apr-04 May-04 Jun-04 Jul-04 Aug-04 Month CRM / Value Prop 50% Integration 20% Predictive Attrition Modelling Closed Current Accounts Analytics M1 M2 M3 M4 M5 M6 Account Closed in M7 Benchmark Period Comparison Period Ordinary, Optimum, N5000 Uncontrollable Controllable Closures Closures Low and Training Data Set No Transactors MMI Accounts then categorised into 5 different transactional behaviours based on statistics and behavioural pattern: Stable Erratic Step Down Declining Predicted Closed Actual Closed Not Closed % 2, % 26, % 399,338 Not Closed 93.9% Step Up

16 Model Product Propensity Model SEMMA Construction* Assess Modify Explore Model Sample Model 40% CRM / Value Prop 40% Integration 20% MODEL MODEL SELECTION SELECTION Model Validation* SELECTION Validation*MODEL Analytics Optimal Model: Regression Base Line Gini co-efficient: 94.2% Response Rate Rank By Score

17 lowest level Depending on the question, the Profitability model can provide different answers based on marginal or fully absorbed costs at different levels. Level 5 Full Abs Grouping Level 4 Marginal Grouping Model 80% CRM / Value Prop 10% Customer,Market, Branch,Dcar Integration 10% Level 3 Full Abs Product A/C Level 2 Analytics CA HL Etc Marginal Product A/C Level 1 Marginal Channel / Tran Contribution T T T T T T T T T T T T

18 Better Cross-/ Up-Selling More efficient customer retention Up-Selling E A E A Client Life-Time Value Model CLV (LTV) = E A + + (1 + i ) (1 + i ) More efficient acquisition of new customers Do the new customers have the same target profile as the very valuable customers? Do the customers have a high propability for cross-selling? Loss CLV Value Present Value Profitability Present Value Profit Do the customers have a high potential? Future Value Do the customers have a low potential? Faster termination of potentially less valuable customer relationships Recovery of potentially valuable customer relationships Analytics Model 80% Potential Potential Value time Period of prediction Potential Value CLV k E t A t k t (t=0) T i CRM / Value Prop 10% Integration 10% Present t T k k k t t = = Value t t = 0 t Future Value k k k k k k k k T ( ) 1 1 E 2 A2 E 0 0 T A T 1 1 = Customer Lifetime Value of a customer k = revenue from a customer k = expenses for a customer k = customer k = time period (t=0, 1, 2, ) = today = predicted duration of a customers relationship = interest rate (1 + i 2 2 ) (1 + i ) Feedback-Loop High Keep Cross-/Up-Selling Potential + Customer Enhance Retention Profitable Customers Low Keep Repeat Purchase Potential + Loyalty Customer Enhance Base High Enhance Cross-/Up-Selling Potential + Customer Keep Retention Unprofitable Customers Low Limit Service cancel Potential Termination of the today End of relationship prediction period T

19 Data Qualitiy Before Industry debate CRM Tangible Value Generation Target Clients 20% Scrubbed Leads P oor data quality (addresses +phone no.) D on t m arket to indicator C redit scoring restrictions 47% Or After 14% Analytics Funds Actioned Leads Model 30% CRM / Value Prop 20% Integration 50% Contacted Clients P oor data (old data) Supports DIRECT MARKETING TELEBANKER CAMPAIGNS 18% Interested Clients (where they can t fulfill) Poor leads Poor message 0.6% 100% 80% 33% 19% 0.5% 0.4% 1.7% of contacted clients result in a sale (8.2% USA stats) Sales Data Quality Poor value p ro po sitio n NOT TO SCALE Intangible Value Generation

20 Index Background & Introduction Fundamentals Model Enablers Some Key Metrics The Customer Centric Data Model Example of integration and owning the value chain

21 i.e. 71% of Private banking clients hold Current Accounts Private 100% 80% Current Account 71% Cross-Sell Ratios Savings Card Home Loans NVF Investment Cross Sell Ratio is the lowest possible cross sell ratio 60% 46% Personal 40% 20% 0% 100% 80% 60% 67% 13 % 47% 27% 11% 8% The 5 average 6 has risen from from last 2.06 month. 40% 20% 0% 19 % 21% 12 % 21% Average: Markets SME Staff 100% 80% 60% 40% 20% 0% 100% 80% 60% 40% 20% 0% 100% 80% 26% 87% 70% 39% 7% 34% 21% 9% 4% 17% 8% 10 % 8% Cross sell ratios for all CGs have increased Staff banking 1.47 has the highest cross sell ratio this is illustrated by 5higher 6 percentages per product overall % 40% 20% 44% 37% 41% 20% 8% %

22 International Cross-Sell Ratio Benchmarks Success of France quoted as: Packages - Success of Bancassurance & sales of embedded products (e.g. X-Sell Credit with Protection) France Sweden Germany Ireland Europe Avg Italy UK Spain USA Standard Bank Nedcor - Many tax-exempt products *Source: Lafferty 2004 **USA figure as per Council on Financial Competition, 2002 Measured across product groupings as per Nedcor measurement. Max = 7 A bank with a cross-sell ratio of 2.3 suggests that typical consumers make a purchase once every 5 years or every 40 to 50 visits Council on Financial Competition, 2002

23 Index Background & Introduction Fundamentals Model Enablers Some Key Metrics The Customer Centric Data Model Example of integration and owning the value chain

24 A complete Customer View (by institution!) Relationship Manager Banking Products with us Financial Products with Other Non Financial Products 24

25 Index Background & Introduction Fundamentals Model Enablers Some Key Metrics The Customer Centric Data Model Example of integration and owning the value chain

26 Integration Leads Management Leads Cleanup Leads Gateway Leads Monitoring Management Measurement Analytical Models Relationship Manager Business Rules Back Office/ Call Centres Scheduled Customer Interactions

27 New Rules of the Game (not really!) SENSE OF URGENCY Improved Performance Return on Capital Employed Merger Tax Credits Revenue Divestments Net Cash Flow Operating Costs Personnel Reductions Net Income FLEXIBILITY 27

28 THANK YOU