1. Enabling More Intelligent and Profitable Customer Interactions Using Oracle BI Real-Time Decisions (RTD),

2. Agenda Introduction to Real-Time Decisions (RTD) Solution Demo Key Capabilities & Features Q&A

3. Introduction to RTD

4. Significant Business Challenges Inefficient Service Processes Lack of Customer Knowledge Customer Attrition Do Not Call List Inconsistent Delivery Channels Low Share of Wallet Inaccurate Customer Segmentation Price-based Competition Employee Attrition Marketing Collateral Overload

5. ‘Back to the Basics’ “Customer retention should be your highest priority in your CRM strategy…After you have protected your customer asset through retention efforts, cross-selling is the CRM strategy for growing revenue.” Kimberly Collins, Ph.D. CRM Summit Spring 2004 Customer Retention Revenue Growth

6. Traditional Outbound Marketing Falls Short Privacy restrictions Customer opt outs Competitive clutter Growing resistance to marketing efforts High cost/low response of outbound marketing Low response rates Low ROI

7. Offline and data-driven process Discrete and often disconnected marketing efforts Offline arbitration of campaign and channel conflicts Product-level analytics Resource and time intensive Warehouse Marts ETL EII Metadata Data Mining CM ProductsScores Web Offer match Warehouse Marts ETL EII Metadata Data Mining BI CM ProductsScores Contact Center Offer match lists Campaign-Centric Approach for Inbound Marketing Limitations and Constraints

8. Data Mining BI CM Interaction-Centric Approach for Inbound Marketing Benefits of Real-Time Recommendations Real-time and KPI-driven Centralized decision logic and in-context predictive analytics Automated and integrated decision services Leverages existing BI assets and operational infrastructure Contact Center Web Decision Services MetricsScoresOLTP POS

9. Real-Time Decision Engine Data Warehouse Campaign Management Data Mining WebATMKioskPOSIVR Business Intelligence Contact Center Telco Fins RetailHealth Travel Others Oracle RTD Provides a Real-Time Decision Engine Delivering Decisions as a Service Gov

10. Pre-built application for Siebel Call Center Intelligent offers and retention treatments embedded in Call Center Data mapping to Financial Services and Communications data model Shares Siebel Marketing offers and campaigns to coordinate inbound and outbound Leverages customer analytics and offline predictive models Oracle RTD for CRM Intelligent Offer Generation and Retention Management Application

11. Solution Demo

12. Demo Scenario About National Bank Fictional financial services provider Customer base: 5 million Assets: $69 billion Revenue: $4.6 billion Large volume Siebel Call Center Business Challenges High customer turnover rate of 14% per year Associated replacement cost in millions per year Average cost of new customer acquisition: $250 Currently 2 products per customer, goal of achieving 4 per customer

13. Demo Use Cases Profile of caller (Linda Johnson): Female, 28 years old, single Holds checking and savings account at National Bank Medium-value customer Calls to change address (due to new job after grad school) Business goals Expand customer relationship through real- time intelligent cross- and up-sell offers Profile of Caller (Robert Knowles): Male, 38 years old, married, homeowner Holds several accounts at National Bank High-value customer Considers closing all accounts (unknown to National Bank) Calls to inquire about checking account fees Business goals Retain customer relationship through real- time retention treatment Use Case # 1 : Intelligent Cross- and Up-Selling Use Case # 2 : Proactive Real- Time Retention Management

14. “Appropriate” 10x Success Leveraging Inbound in Real Time Customer Initiated, Relationship Driven “Convenient” 5x Success Event Driven Customer Triggered “Unexpected” 1-5% Response Campaign Marketing-initiated CustomerEnterprise Advanced real-time predictive analytics allow each interaction at any time and any channel to be tailored for each customer RTD Leverages Inbound Interactions Source: Gareth Herschel, Gartner, ‘03

15. CallIVR NavigationCC Route In-House CCOutsource CC CSR Intro Resolution?CSR AnswerCross Sell Handoff 2 nd Tier Handoff Yes No Agent / Queue Route End/Handoff Skip IVR / Escalate immediately Route to optimal CC Route to optimal queue / agent Present targeted marketing offers List likely answers / resolutions Escalate based on priority Intelligent Contact Center Using RTD

16. Sales Marketing Service RTD Drives Top & Bottom Line Benefits Incremental revenues through improved cross and up selling Improved long-term profitability through enhanced loyalty and retention Reduced acquisition costs through improved customer retention Reduced outbound marketing spend through more effective inbound marketing and leverage of insights into actual customer response behavior for outbound Reduced operating costs through more intelligent and streamlined business processes Improved agent productivity by enabling less experienced and skilled users

17. Key Capabilities & Features

18. Gap Between BI and Operational Apps Operational Applications BI & Analytics Solutions

19. Operational Applications Oracle RTD Bridges the Gap BI & Analytics Solutions Real-Time Decision Solutions  Bridge between operational and analytical worlds  Operationalizes offline analytic insight, models and scores  Creates new behavioral & contextual insights through continuous learning  Unites channel experiences through singular decision framework  Drives process behavior of both technology and human resources

20. Challenge of Business Process Optimization Improving the Process from the “Outside” Process Improvement Time Lag to Implement Change Expensive and Lengthy Projects Limited Adaptability to Changes Process Intelligence Offline Analysis, Historical Data Aggregated siloed data Limited by Analyst Bandwidth Today’s Process A gap exists between process intelligence and improvement

21. Performance Goal driven Continuous control Detects changes over time Decision service Continuous learning Transaction level data Operationalize traditional analytics Process-oriented data model Today’s Process RTD Decision Server Process ImprovementProcess Intelligence Process Optimization via Real-Time Decisions Improving the Process from the “Inside”

22. Performance Goals as First Class Citizens Influence operational business process to optimize multiple competing performance goals, such as: Minimize Service Costs Maximize Revenue Expedite Customer Service Minimize Attrition Risks Ability to arbitrate is a built-in feature of the product and not implemented externally

23. Oracle RTD Decision Framework Driven by Domain Knowledge and Empirical data Predictive Approach: Automation Prediction inferred from data vs. explicitly defined Predictions [automatically] evolve based on response patterns and changes Requires a lot of data RTD provides very granular control over the degree to which rules and analytics can be used to drive the decision process Rule-Driven Decisions When decisions are driven by declarative logic expressed by business users Model-Driven Decisions When decisions are driven by logic learned by models from empirical data Rules Approach: Control Existing knowledge and logic can be leveraged Convenient when decision needs to be constrained Does not scale with volume / interaction complexity

24. Self-Learning: A Process Perspective Source Databases Analytical Mart Data Mining Tools Scores and Lists Operational Applications Traditional Learning Process: models lag by weeks or months Continuous Self-Learning Process: models are updated in real-time feedback: days or weeks Operational Applications feedback: immediate decisions events Self-Learning Analytics input from external models and lists Advantages: Automatic model creation Quick to react when behavior changes Allows broader scope of analysis Simple to implement and run

25. Tracking Multiple Outcomes Over Time Predicting a single outcome from a decision does not model real buying processes, which have multiple steps over time Learning is limited as decisions are based on very limited criteria Decision Server tracks multiple outcomes from each decision over time “Free cable for 30 days” “Seminar on home refinancing” 1. Interested 2. Registered 3. Attended 4. Applied for refinance 1. Interested 2. Installed 3. Kept service beyond 30 days Examples (now) (+10 days) (+30 days) (+5 mins) (now) (+7 days) (+10 days)

26. Adapting to Changes in Behavior The Problem with Current Solutions: Other products treat old response data as if it is as relevant as newer data; this is a huge mistake How Businesses Try to Cope: Two choices: either run forever with undifferentiated data, or flush all of the data periodically No useful way to look at what has happened within and across time periods Decision Server Approach: Automatically track, weight, and report on response data over time via user-controlled criteria

27. Enabling True Multi-Channel Solutions Channels have varying response characteristics, so models that naively “pool” channel data are less effective Businesses should not build separate analytic solution “silos” for each channel Choices, Rules, Models, Learnings CC Decision App (silo) Choices, Rules, Models, Learnings Web Decision App (silo) Contact Center Web Other Products Choices, Rules, Models, Learnings Multi-Channel Decision App Shared Set of: Contact Center Web Decision Server Decision Server provides partitioned learning models, such that a single application can support true multi- channel decisions

28. Real-time Decision Process Eligibility Engine Prediction / Scoring Engine Decision Server 1. Send customer id 5. Determine eligible offers 6. Score eligible offers 7. Return ranked offers Learning Engine 8. Send response 9. Learn from response 3. Send context info 2. Create session & load customer data 4. Request offers Customer Interaction Touch Points

29. RTD Platform and Integration Points Portals, PDAs, … Publishers CRM System Contact Data Transaction Data Contact Data Suppliers AdvisorsInformants Enterprise Apps SFA, CRM, Web Portals, … Decision Server Profile Manager Learning Engines Decision Engines Goal Manager Business & IT User Interface InlineService Informants & Advisors Handle information events and requests for decisions from enterprise applications Publishers Deliver KPIs, alerts, learnings, and other information to portals & external apps Suppliers Deliver profile data on demand User Interfaces Analyze data, create offers, configure system

30. Open and Flexible Integration Support Oracle BI EE Server 3 rd Party Model Executable ACD / IVR XML / SOAP.NET HTTP JDBC Decision Server on J2EE Back-End Database Back-End Database Back-End System Web App Call Center App Teller / ATM App Informant Advisor Java Smart Client JSP Tags Java / JNI Data Mart / Warehouse JDBC XML / SOAP Java

31. New Real-time Decision Paradigm Producing scores to Managing goals Refreshing modelsto Adjusting to changes Response management to “Closed Loop” mgmt Single channelto Multi channel analytics “Out of context” to “In context” analytics Resource intensive to Automated process Replacing systemstoIntegrating systems Helps companies shift their attention from …

32. Key Features of Oracle RTD General Purpose Real-Time Decision Platform and Framework Granular control over mix of rules and analytics, including built-in self-learning predictive models, to provide decision services Enterprise Alignment / Multiple KPI Prioritization Every decision is measured against and arbitrated upon multiple competing performance goals Event-driven / SOA architecture Decisions are provided as a service in real-time in the context of an interaction workflow / operational process

33. Questions & Answers