Data To Decision: Transforming Marketing Data To Enable Decision Management

Data To Decision: Transforming Marketing Data To Enable Decision Management Summer 2007

Agenda

What is Decision Management?

Improving Data Access

Analytical Model Techniques

Deploying Business Rules

In Case You Hadn’t Noticed, Things Around Us Are Changing

Mashups: combining content from multiple sources into one, integrated experience, Google, Yahoo! maps and social networking, YouTube, eBay and Amazon

If customers are building multi-company websites how can you personalize it for them?

More Mashups

More and more devices are being created, each a potential channel

These devices are increasingly converged and crossing over traditional boundaries

More Devices

All touch points keep upgrading (cell phones, ATMs, kiosks)

Massive complexity requires automation - the complexity is increasingly in decisions and so a focus on automating decisions is required

More Complexity

People want to do more for themselves and expect every interaction, regardless of how or where it takes place, to be personalized to them

More Self-Service

As more human behaviors emit trails of digital residue, the more opportunities reside for algorithms to harness human-induced data and become information intermediaries

More Data

The problem

In any moment of decision, the best thing you can do is the right thing, the next best thing is the wrong thing, and the worst thing you can do is nothing. Theodore Roosevelt

“ ”

Fair Isaac’s Perspective: Enterprise Decision Management

Management of decisions, especially front-line decisions , is key

Success requires precision, consistency and agility in making customer-facing decisions in real time

You must be able to link business execution to business strategy

Increasingly your ability to make the right decision quickly is a competitive advantage

Automating decisions frees up resources to focus on other issues

Enterprise Decision Management is a systematic approach to managing and improving operational decisions across the enterprise.

Evolution STAGES OF MARKETING SOPHISITCATION AND PERFORMANCE Broadcast your message to a specific segment STAGE 2 SEGMENT MARKETING Exploratory Analysis Actionable Analysis Predictive analysis and segment- focused communications STAGE 3 TARGET MARKETING Personalized communication based on individual behavior patterns STAGE 4 CUSTOMER DRIVEN COMMUNICATION Iterative and progressive communications across all channels STAGE 5 INTERACTIVE CONVERSATIONS Multi-channel, Customer Managed Relationships Event-Driven Customized Communication Broadcast your message and they will come STAGE 1 MASS MARKETING

Enterprise Decision Management Essentials: Data Access, Predictive Models and Business Rules Data Access Analytical Models Business Rules

EDM is About Improving Operational Decisions Rules / Strategies Models Data Access Model Development Rules Management Enterprise Data External Data Results Decision Request for Decision Analyst Tools Business User Tools Mobile POS Email Website Call Center CRM

Data Management Foundations

Why?

Lower Maintenance Costs

Less Training Across The Organization

Reuse of Business Entities = Faster Time To Market

Common Definitions

Point-to-Point vs. Hub & Spoke Reference: Forrester Research, Inc., “Best Practices: Successful Data Integration Requires Architecture,” Philip Russom, March 31, 2004 16 Systems Integrated with 120 Interfaces 16 Systems Integrated with 16 Interfaces and 1 Hub Data Access

In Practice: The Corporate Information Factory Operational Systems Data Management Metadata Management Data Delivery Exploration Warehouse Campaign Mart Data Mart Data Mart Integration/Transformation Data Warehouse ODS Finance Marketing Sales Call Center Decision Management Campaign Management Operational Decisions Tactical Decisions Strategic Decisions Data Access Dashboard, Reporting, Visualization

What It Looks Like Over Time Data Access

Expose Yourself: Sharing Data in a Service Oriented Architecture

Why?

Reduce setup time for new partners/applications

Strengthen links to partners & improve data quality

Simplify internal operations

Data Access

Add a new customer record

Update Existing customer / household information

Address change

Email address change

Permissions change

Merge two customer / household records

Split two customer / household records

Query for a list of customer’s / household’s

Query for a customer’s / household’s detail information

Query on a customer’s Touchpoint number

Service Oriented Architectures Can Be Complex: Start with Data Services Reference: Forrester Research, Inc., “Road To A Service-Based Architecture,” Ted Schadler, December 2002 Data Access

Building Your Infrastructure : How To Get There

Articulate the direction and then deploy it over time

Start small, and bolt on new components

Follow the standard…don’t allow a single provider to break the center or create a new center

Latch onto other enterprise-wide initiatives for common purpose

Retrofit older applications and products onto the environment

Data Access

Building on Internal Data

Customer data integration that can be analyzed and leveraged

An enterprise or active data warehouse that supports not just business analytics but the building of sophisticated predictive analytic models

Real-time, or right-time, data updates

Ready access for those who manage the decision process so they can close feedback loops

Data Access

The Data at Fair Isaac: Too Much To Manage Piecemeal 93+ Data Sources 28+ Products / Product Groups Data Access

Our Answer: ScoreNet™ One Connection, a World of Data

Fair Isaac is opening our EDM solutions to third party data:

180 billion decisions a year

Creating more hosted analytic solutions every year

Creating a decision marketplace for data

Large and Growing Source of Data:

58 Databases from 32 Vendors, Growing to 93 Databases

200+ end clients connected (3000+ including batch)

Launching new scores and new decision solutions off this asset:

Qualify Score, Expansion Score, Blaze Advisor, EDM tool kit

Falcon ID, LiquidCredit, ActiView

Data Access

ScoreNet TM Data that Drives Decisions

Sufficient scale that data providers want to participate in the network

Co-op databases

Compiled demo databases

Credit / fraud files (current “Agent of the Bureau” status)

A single platform across FI (every client/every product)

Competitive data rates

Analytic/R&D rights to data

Allows our clients to easily test new data sources

Our focus on Decision Management is driven in part by our compiled data capabilities Data Access National Consumer Reporting Agencies Transaction/ Fraud Data E-mail ECOA and Hygiene Suppression Processing Ethnic Data Debit Real Estate Data Employment and Income Fraud Automotive Data Geocoding Public records Collections Data IP Address Geolocation Consumer Credit Business Data Postal Processing Demographic/ Life Style Healthcare Data Phone/Address Append Consumer Contact/Locate

ScoreNet TM Access to Business & Consumer Data

Lower Barriers to Use: Use data for model development on complimentary basis

Simplified Management: Coordinate with a single provider of data

Lower Cost: Exploit Fair Isaac’s economies of scale in data purchases

Greater Selection: Source non-traditional data elements

Enhanced Data: Receive not just data, not also analytics and decisions

Expanded Applications: Leverage same data across multiple internal processes

Marketing Common Standards Ubiquitous Access Data, Analytics, and Decisions Integrated into Internal Processes Fair Isaac ScoreNet: Common Access to Multiple Data Providers Data Access

Program Framework Value Measurement Organization User Services Communication Tools & Partners Governance External Services Data Management Competency Center Methodology Architecture Knowledge Transfer

KPI Development

Performance Measurement

ROI/ROA/SVA

Class Training

OJT Shadowing

Ownership Transfer

Stakeholder ID

Executive

Steering

User Groups

Data Stewardship

SLA’s

Help Desk

Reporting

Usage Tracking

Core Team

Dynamic Staffing

Roles & Responsibilities

Systems & Network

Data & Data Integration

Information Delivery

Selection

Contracts

Support

Approach

Work Blocks

Flexibility

Methods

Web site

Interventions

Metadata

Championship

Applications

Profiles

Preferences

Security

Data Stewardship Program – Sample Stewardship Matrix Departments produce and consume data in multiple subject areas. The Matrix below will document the intersection points. Subject Area Department X = Means Key Data Producers/Consumers S = Key Data Producer/Consumer and Data Steward

Data Stewardship Program – Responsibility Business Steering Committee (BSC) – Oversees organization & accountability, standards & procedures, audit & control across systems. Decision makers on high impact issues and improvements. Data Stewards – Accountable for their subject area’s data quality. Data Stewards communicate with the BSC and coordinate/lead the working team stakeholders. ( need to discuss in detail ) Working Teams – Working Teams consists of non-steward stakeholders that have been identified as key data producers and consumers in a subject area. These teams help define issues, recommend solutions, support ongoing data assessment, business rules definition, and support data stewards with the data quality process. IS Facilitation and Support – This IS Team works to facilitate, execute and support the Data Stewards and Working Teams with issues and improvement tasks. Data Access Working Teams Business Steering Committee Data Stewards IS Facilitation

Strategic Framework for Business Intelligence A Complete Analytic Portfolio Provides Three Levels of Information How am I doing? What opportunities exist to improve performance in the future? Why am I doing well or poorly? Enable Effective Business Decisions Understand Performance Measure Performance Relative to Objectives KPIs Performance Diagnosis Supporting Info The portfolio provides each user with a comprehensive view of key information to drive action and measure performance Data Access

Business Intelligence Must Support Diverse Needs Performance Excellence Execute Focus Business Attention Insight Knowledge Information Summarization Data Standard Reports Ad-Hoc Reporting Analysis + - Level of Decision-Maker - + Amount of Detailed Data Dashboards A Complete Data Management Solution Provides Information To Support Multiple Communities with Varied Needs Data Access

Business Intelligence Tools (BI)

Business Intelligence Dashboard

Development of dashboard reporting to support multiple subject areas (Response, Product, and Web analysis)

Response Summary shows brand performance against a pre-defined goal

Interactive graphic allows user to see progress at any given point during the month

Standard Reporting

Develop set of standard reports

Standard reporting package identified during design step

Ad Hoc Query & Analysis

Configuration of BI tool to answer ad hoc questions

No advanced knowledge of underlying data source required

Custom Variables

Advanced Filtering and Graphing

Data Access

EDM is not BI

Strategic decisions have broad business scope but occur less frequently

Tactical decisions determine how the enterprise will manage processes and customers

Operational decisions have the highest volume and deal with individual transactions

BI has the most value for Strategic decisions

EDM the most value for Operational decisions

Tactical decisions require a blended approach

Strategic Decisions Tactical Decisions Operational Decisions

Different Types of Analytics are Needed They can be used together and often are. Analytical Models 3 2 1 Cluster model Classify or categorize individuals or other entities Descriptive Analytics Strategy optimization Credit score Example Develop superior ruleset or strategy Decision Analytics Predict future behavior of individual Predictive Analytics Used to: Type

Descriptive Models Identify Relations Use: Find the relationships between customers Example : Sort customers into groups with different buying profiles Operation : Analysis is generally done offline, but the results can be used in automated decisions – such as offering a given product to a specific customer Descriptive models can be used to “categorize” customers – which can be useful in setting strategies and targeting treatment. Analytical Models

Product Relationships with PeaCoCk: Pairwise Co-occurrence Consistency

The PeaCoCk framework allows modelers to present complex relationships (using information theory) between entities (e.g.. words, phrases, products, customers, stores, etc.) in various contexts.

These pair-wise co-occurrence relationships result in a PeaCoCk graph which can be "mined' for patterns of interest (using graph theory and machine learning algorithms).

Medium Consistency Bought together in 5.2% of purchases of both products Low Consistency Bought together in 0.2% of purchases of both products High Consistency Bought together in 21.3% of purchases of both products Dryer Washer Side-by-Side Washer Blenders Phones Analytical Models 284 64,326 76,625 32,593 79,505 8,173 39,097 79,506 52,743

Insight-Driven Customer Decisioning Analytical Models Cross-sell via web/e-mail/POS If a customer purchased “sufficient” products in a bundle, promote the remaining products to “ bring closure to the purchase event ” Optimize Store/Web/Catalog Layout Strategically place the “neighboring products” based on co-purchase affinity to “ maximize coat-tail influence of the product ” Cross-department traffic If a customer purchased “sufficient” products on one side of a bridge, and nothing on the other, then promote the bridge product to “ migrate customer into higher value area ”

Insights in product affinity graphs Analytical Models Product affinity graph Neighborhood Bundle Bridge Phrase

PeaCoCk Product Space Browser: Discovery Workbench A customer insight tool that allows a retailer’s research or customer insight team to graphically mine their data to understand bridges, bundles, purchase phrases based on consistency measures across purchases and time . Analytical Models

Store Design & Layout Identify Logical Relationship Among Products Multilevel Constrained Co-occurrence Optimization DEPARTMENT a b c d e a b c d e CATEGORY 1 2 3 4 5 2 1 4 3 5 PALLET/SKU Store Planogram Analytical Models

Shelf Space Allocation Modeling Analytical Models

Predictive Models Calculate Risk Or Opportunity Use: Identify the odds that a customer will take a specified action Example : Will the customer pay me back on time? Will the customer respond to this offer? Operation : Models are called by a business rules engine to “score” an individual or transaction, often in real time Predictive models often rank-order individuals. For example, credit scores rank-order borrowers by their credit risk – the higher the score, the more “good” borrowers for every “bad” one. Analytical Models

Decision Models Design More Effective Strategies Use: Design a ruleset that will deliver the right decisions to reach goals Example : Identify how much money to spend on each marketing channel to maximize sales in a given timeframe and budget Operation : Decision models are used offline to develop rules, which can then be deployed to operate in real time A decision model maps the relationships between the data available, the decision itself, the outcomes of the decision and the business objective. It is ideal for balancing multiple objectives and constraints. Analytical Models

More Sophisticated Analytics Improve Results Decision Optimization Predictive Modeling Descriptive Analytics How do I use data to learn about my customers? Who are my best/worst customers? How are those customers likely to behave in the future? How do they react to the myriad ways I can “touch” them? How do I leverage that knowledge to extract maximum value from my marketing investments? Knowledge - Description Action - Prescription Analytical Models X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X

Enterprise Decision Management Essentials: Data Access, Predictive Models and Business Rules Data Access Predictive Models Business Rules

What Are Business Rules? … The Way You Conduct Business

Logical statements of what to do (what actions to take) in different distinct situations

All companies have rules about how to do business

In lines of computer code

In manuals and memos

In the employees’ heads

In external regulations

If (vehicle’s age is between 0 years and 8 years) and (policyholder’s age is between 21 years and 60 years) and (policyholder’s number_of_claims does not exceed 3) Then set policyholder’s case to “STANDARD” If order’s purchaseDate is earlier than January 1, 2004 then print(“Your purchase is no longer eligible for return”). If customer's debt exceeds customer’s assets then set the approval_status of customer’s application to Declined Business Rules

Rules Come in All Shapes and Sizes Decision Trees Decision Tables Scorecards Templates Business Rules

EDM is About Improving Operational Decisions Rules / Strategies Models Data Access Model Development Rules Management Enterprise Data External Data Results Decision Request for Decision Analyst Tools Business User Tools Mobile POS Email Website Call Center CRM

Where to Apply EDM

Identify opportunities to automate decisions

The best customer decisions to automate are those that:

Leverage the customer data you have or can get

Need to change frequently for competitive or product—mix reasons

Can be delivered across many channels

Should be controlled by your business people

Require more complex business solutions

Are regulated

Deliver strategic differentiation

Decision Intensity Low Small customer base Few channels Few brands Few SKUs Few locations Few pricing variations Few interactions per time period Long time period High value purchase High volume purchase Large customer base Many channels Many brands Many SKUs Many locations Many pricing variations Many interactions per time period Short time period Low value purchase Low volume purchase High 0 1 2 3 4 5 6 7 8 9 10 Strong EDM candidate Weak EDM candidate 0 1 2 3 4 5 6 7 8 9 10

The ROI from EDM