Oracle Bi Rtd - Crm Focus

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

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Agenda

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


Introduction to RTD

Significant Business Challenges

Inefficient Service ProcessesInefficient Service Processes

Lack of Customer KnowledgeLack of Customer Knowledge

Customer AttritionCustomer Attrition

Do Not Call ListDo Not Call List

Inconsistent Delivery ChannelsInconsistent Delivery Channels

Low Share of WalletLow Share of Wallet

Inaccurate Customer SegmentationInaccurate Customer Segmentation

Price-based CompetitionPrice-based Competition

Employee AttritionEmployee Attrition

Marketing Collateral OverloadMarketing Collateral Overload

‘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 Customer RetentionRetention

Revenue Revenue GrowthGrowth

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 responserates

Low ROI

• 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

WarehouseMarts Marts

ETL

EII

Metadata

Data Mining CM

ProductsScores

Web

Offer match

WarehouseMarts Marts

ETL

EII

Metadata

Data Mining BI CM

ProductsScores

Contact Center

Offer match lists

Campaign-Centric Approach for Inbound MarketingLimitations and Constraints

Data Mining BI CM

Interaction-Centric Approach for Inbound MarketingBenefits 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

MetricsScores OLTP

POS

Real-Time Decision Engine

Data Warehouse

Campaign Management

DataMining

Web ATM Kiosk POSIVR

BusinessIntelligence

Contact Center

TelcoTelco FinsFins RetailRetail HealthHealth TravelTravel OthersOthers

Oracle RTD Provides a Real-Time Decision EngineDelivering Decisions as a Service

GovGov

• 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


Solution Demo

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

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 intelligentcross- 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

“Appropriate”10x Success

Leveraging Inboundin Real TimeCustomer Initiated,

Relationship Driven

“Convenient”5x Success

Event DrivenCustomer Triggered

“Unexpected”1-5% Response

CampaignMarketing-initiated

CustomerEnterprise

Advanced real-time predictive analytics allow each interaction at any timeand any channel to be tailored for each customer

RTD Leverages Inbound Interactions

Source: Gareth Herschel, Gartner, ‘03

Call IVR Navigation CC Route

In-House CC Outsource CC Outsource CC

CSR Intro

Resolution? CSR Answer Cross Sell

Handoff Handoff

2nd Tier

Handoff

Yes

No

Agent / QueueRoute

End/Handoff

Skip IVR /Escalate immediately

Route to optimal CC

Route to optimal

queue / agent

Present targetedmarketing offers

List likely answers /resolutions

Escalate based on priority

Intelligent Contact Center Using RTD

Sales

Marketing

Service

RTD Drives Top & Bottom Line Benefits

• Incremental revenues through improved cross andup 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


Key Capabilities & Features

Gap Between BI and Operational Apps

OperationalApplications

BI & AnalyticsSolutions


Oracle RTD Bridges the Gap

BI & AnalyticsSolutions

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

Challenge of Business Process OptimizationImproving the Process from the “Outside”

Process ImprovementProcess Improvement

Time Lag to Implement Change

Expensive and Lengthy Projects

Limited Adaptability to Changes

Process IntelligenceProcess Intelligence

Offline Analysis, Historical Data

Aggregated siloed data

Limited by Analyst Bandwidth

Today’s Process

A gap exists between A gap exists between process intelligence and improvementprocess intelligence and improvement

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 DecisionsImproving the Process from the “Inside”

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 andnot implemented externally

Ability to arbitrate is a built-in feature of the product andnot implemented externally

Oracle RTD Decision FrameworkDriven 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 whichrules and analytics can be used to drive the decision process

RTD provides very granular control over the degree to whichrules 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

Self-Learning: A Process Perspective

SourceDatabases

Analytical

Mart

Data MiningTools

Scoresand Lists


Traditional Learning Process: models lag by weeks or months

Continuous Self-Learning Process: models are updated in real-time

feedback: days or weeks


feedback: immediate

decisions

events

Self-LearningAnalytics

input fromexternal models

and lists

Advantages:

• Automatic model creation

• Quick to react when behavior changes

• Allows broader scope of analysis

• Simple to implement and run

Tracking Multiple Outcomes Over Time

• Predicting a single outcome froma 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 decisionover time

“Free cablefor 30 days”

“Seminar onhome refinancing”

1. Interested

2. Registered

3. Attended

4. Applied forrefinance

1. Interested

2. Installed

3. Kept servicebeyond 30 days

Examples

(now)

(+10 days)

(+30 days)

(+5 mins)

(now)

(+7 days)

(+10 days)

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

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)

ContactCenter

Web

Other Products

Choices,Rules,Models,Learnings

Multi-Channel Decision App

Shared Set of: ContactCenter

Web

Decision Server

• Decision Server provides partitioned learning models, such that a single application can support true multi-channel decisions

Real-time Decision Process

EligibilityEngine

Prediction /Scoring Engine

Decision Server1. Send customer id

5. Determine eligible offers

6. Score eligible offers

7. Return ranked offers

LearningEngine

8. Send response9. Learn from response

3. Send context info2. Create session & load customer data

4. Request offers

CustomerInteraction

Touch Points

RTD Platform and Integration Points

Portals,PDAs, …

Publishers

CRMSystem

ContactData

Transaction Data

ContactData

Suppliers

AdvisorsInformants

Enterprise AppsSFA, CRM, Web Portals, …

DecisionServer

Profile Manager

Learning Engines

Decision Engines

Goal ManagerBusiness& IT UserInterface

Inline Inline ServiceService

• 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

Open and Flexible Integration Support

Oracle BIEE

Server

3rd PartyModel

Executable

ACD / IVR

XML / SOAP

.NET

HTTP

JDBCDecision Server

onJ2EE

Back-EndDatabase

Back-EndDatabase

Back-EndSystem

WebApp

Call Center App

Teller / ATMApp

Info

rma

ntA

dv

iso

r

JavaSmart Client

JSP Tags

Java / JNI

Data Mart / Warehouse

Data Mart / Warehouse

JDBCXML / SOAPJava

New Real-time Decision Paradigm

Producing scores to Managing goals

Refreshing models to Adjusting to changes

Response management to “Closed Loop” mgmt

Single channel to Multi channel analytics

“Out of context” to “In context” analytics

Resource intensive to Automated process

Replacing systems to Integrating systems

Helps companies shift their attention from …

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


Questions & Answers

Oracle Bi Rtd - Crm Focus

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