This presentation was given at the World Economic Outlook Conference on 22 October 2009 as an introduction to decision analytics and predictive modeling and how it could be applied predicting the decisions of individuals, rather than aggregates, of people. This was before the days of Big Data and Hadoop so the possibilities are even greater today. Of particular note in this presentation are the slides illustrating the additional power of adding local economic information to an analysis rather than relaying on more aggregate economic information. Small scale, local changes in the economy will influence consumer behavior and it's important to know that when launch products and setting prices, for instance. Please share your comments.

1. Decision Analytics:
Revealing Customer Preferences and Behaviors
Presented by:
Larry Boyer
Director, Decision Analytics
IHS Global Insight
22 October 2009
Boston, MA

2. 2
• What Are Decision Analytics?
• How Are Decision Analytics Applied?
• Decision Analytics in Practice
• Why Does the Economy Matter?
• What Can IGI Decision Analytics Do for You?
• What Do You Need to Succeed?
— Comprehensive Data
— Analytical Expertise
• How Can Decision Analytics Help You?
— Selected Examples
• Case Studies
• Business Implications and Bottom Line
Agenda

3. 3
What Are Decision Analytics?
Transforming Your Information into Actionable Knowledge and Insight
• Integrating transactional, operational, and customer level data with broader
macroeconomic data to drive critical insights into the business environment
Management
Analytical Expertise
Technology
Data: In-house & External
Actionable Strategic and Tactical Decisions

4. 4
How Are Decision Analytics Applied?
• Internal Applications
— Supply Chain:
• More accurate matching of supply and demand
• Reducing stockouts and overstocks,
• Price Optimization
— Operational Efficiency:
• Distribution Site Location,
• Staff placement Optimization
• External Applications
— Enhance Customer Acquisition & Retention
— Discover Customer Preference & Behaviors

5. 5
Decision Analytics in Practice
• Signet
— Applied Data Mining Techniques
• Discovered unexpected source of profitable customers
» those who borrowed large amounts of money and paid off the
balances slowly
— Tactical Action:
• Created the first balance transfer card
• Targeted debtors as valued, not just valuable customers
— Success: Signet spun off its bank card division
• We know it now as Capital One.

6. 6
Decision Analytics in Practice
• Progressive Auto Insurance
— Explored in-house data and external data
— Discovered strong relationships between FICO scores & higher risk drivers
— Tactical Action
• Construct predictive scoring model
— Success: Progressive is the 1st
insurance company to offer real-time, on-line
insurance comparisons

7. 7
Decision Analytics in Practice
• Boston Red Sox, 2002
— Situation: 84 years without winning the World Series
— Turned to analytics
• Explored data to uncover new patterns of player performance
• Management acted on the what the information said
— In 2004 the Sox win the World Series for the first time in 86 years

8. 8
Why Does the Economy Matter?
• Economy and Demographics are constantly in motion and evolving
• You cannot control economic or demographic changes
• You can understand their relationships and impact on your business
• You can steer your company through to success

9. 9
Why Does the Economy Matter?
House Price Changes, 2008Q4 Vs 2008Q3
< -10%
-10% to -5%
-5% to 0
0 to 5%
> 5%
• Massive loss of wealth modifies consumer behaviors
• Massive loss of jobs modifies consumer preferences and behavior
— Americans lost a
cumulative US$3.3 trillion
in home equity in 2008
— Direct holdings, mutual
funds, and retirements
plans fell a combined
$12.1 trillion in 2008

10. 10
— Regions and metro
areas will experience
wide ranges of growth
— How are you planning
to operate your
business in each
area?
— Do you understand
how changes in one
region affect your
business in another
area?
Why Does the Economy Matter?
Personal Income Growth, 2009-2014 Average
Annual Growth
• Understanding our economic future and how it will modify consumer preferences and behaviors

11. 11
What Can IGI Decision
Analytics Do For You?
Who are my most
valuable customers?
Who will be my
next customer?
Which customers are at risk
of cancellations?
We can help you link
customer level data
with economic insights
to answer critical
questions such as:

12. 12
What Can IGI Decision
Analytics Do For You?
• Economics and Demographics
— Uncover relationships between consumer behavior and
economic and demographic changes
• Forward Looking
— Prospective not Retrospective:
• Insights beyond just what happened
• Why it happened
— Proactive not Reactive:
• Fact-based, data driven insights to simulate or
perceive outcomes prior to decision implementation

13. 13
What Do You Need to Succeed?
Available Data Array Integrated Data Array
• Product Level Data
— Type of product sold
— Number of units sold
— Price
Product
Completeness of Data — Supply Side
— Reliability
— Frequency

14. 14
What Do You Need to Succeed?
• Customer Level Data
— Who was the customer?
— Duration and frequency of customer interactions
— Customer demographic characteristics
Customer
Completeness of Data — Demand Side
Available Data Array Integrated Data Array

15. 15
What Do You Need to Succeed?
• Economic Level Data (state, MSA, county, zip code)
— Unemployment
— Wages
— Disposable income
— Fuel prices
— Home Values
Macroeconomic
Completeness of Data — The Economy
Available Data Array Integrated Data Array

16. 16
• Many companies competing on analytics can become insular in their outlook
relying on their internal data to drive analysis and solutions sets
— Leave out other important factors
— Leads to imprecise relationships
• Example: Risk of Nonpayment
Comprehensive Data — In Action
Internal Data is Not Enough!
εχβχβα +++= 2211y
εχβχβχβα ++++= 332211y
equation 1:
equation 2:
Current
balance
Payment
History
Error Term
Unemployment Rate
for Zip Code of
Customer
Variation in Rate of Nonpayment
Without Economic Drivers
With Economic Drivers

17. 17
Analytical Expertise
Success relies upon implementation of analytical tools and techniques by highly
qualified quantitative experts
• Right analytical tools
• Appropriate application of the tools necessitates deep expertise
Affinity Analysis
Market Basket Analysis
Events Analysis
Market Segmentation
Profiling
Defect Analysis
Fraud Detection
Risk Analysis
Portfolio Selection
Forecasting
Credit Approval
Direct Mail
Campaign Management
Link Analysis
Frequency
Analysis
Clustering Classification
Value
Prediction
Associations Sequential
Patterns
Similar
Sequences
Demographic
Clustering
Neural
Networks
Decision
Trees
Radial Based
Functions

18. 18
Identification of “At Risk” Customers
• Problem: A weakening economy is resulting in higher than anticipated
customer cancelations. Can we identify which customers are “at risk” of
canceling given the economic outlook?
• Solution: Develop a model that predicts customer cancelation based on both
historical experience and economic conditions.
— Model critical drivers contribute to “at risk” status
— Create a “Risk” Index for all customers
— Customize risk ranking
• Customer characteristics
• Economic conditions
— Forecast future risk
• Action: targeted action to mitigate cancellation risks

19. 19
Identification of Potential New Customers
• Problem: How do I use information about my current customer base to target
and identify potential new customers as the economy recovers?
• Solution: Model current customer base and economic condition
— Link customer characteristic and economic
data to estimate likelihood of being a
customer in a given region, state, or MSA
— Leverage 3rd
party data source to define
total market
— Predict likelihood of being a customer
within total
• Action: Target consumers with the highest
likelihood of becoming customers
Predicted
Penetration
Actual
Penetration
Opportunity for
Market Share Gain
Total Market

20. 20
Target High Value Customers
for Sales Efforts
• Problem: With so many potential customers to direct sales efforts towards, how
do we focus our efforts on the most valuable customers for our company?
• Solution: Develop an expected value index for potential customer
— Time series of data on historical customers, economic conditions, spending
patterns allows development of a tool to:
• Predict the likelihood of any individual customer’s response to sales
efforts
• Estimate the annual or lifetime value of a customer based on historical
purchasing patters and economic conditions for other customers
• Derive an Expected Value Index
» EV Index = Probability * Annual Value
• Action: Target sales efforts at customers with highest EV Index scores

21. 21
Selected Examples
Major Pharmaceutical Company
• Problem: How does the current economic downturn impact customer consumption behavior
by therapeutic areas of treatment and how to better position sales and marketing efforts in
light of future economic changes?
• Solution: Development a model to forecast customer behavior
— Linked Medicare, Medicaid, MSA level economic, IMS prescription, and internal client
prescription and marketing data
• Results:
— Asymptomatic customers exhibited nearly
2 times greater sensitivity to economic
conditions than symptomatic
— Wealth measures such as home price
indexes, stocks values, and real income
where critical drivers in customer behavior
related to prescriptions
— Different marketing efforts had alternative
impacts on mitigating the loss of sales
volumes in asymptomatic patients by MSA
-14.00
-12.00
-10.00
-8.00
-6.00
-4.00
-2.00
0.00
MSA 1 MSA 2 MSA 3 MSA 4 MSA 5
Assistance Program
Coupon
Reduction in Asymptomatic RRx 2010
(Top 5 MSA)

22. 22
Selected Examples
Major Healthcare Provider
• Problem: Monitor eligibility and enrollment in a healthcare assistance program. Annual review
of nearly 1.2 million individuals. Two methods of customer outreach: a direct mailer approach
or a in person interview.
• Solution: Develop a model to forecast customer behavior
— Linked transactional level healthcare treatment trends, demographic and enrollment data,
and economic conditions of eligibility
— Linked medium home price and income with customer level data provided critical insights
into eligibility and enrollment behaviors
• Results:
— Reduced requirements for in-person
interviews by 1/3
— Net administrative cost savings of
approximately $30 million per year
13%
24%
27%
36%
16%
9%
8%
67%
Original Approach Enhanced Approach
In-Person
Interviews
Direct Mailer

23. 23
Selected Examples
Major Resort Destination
• Problem: How does the economic down turn and shrinking business and leisure travel were
impacting tourism arrivals? How might alternative economic outlooks change travel arrival
numbers?
• Solution: Develop a model of consumer travel behavior
— Linked country level macroeconomic data, customer level survey data, and enplanement data
— Created a scenario tool test alternative economic scenarios
• Results: Civilian arrivals dependent on consumer preferences and origination market economics
— Resort Destination identified key drivers (package deals and other marketing incentives) to
modify customer behavior to mitigate forecasted economic effects
Total Civilian Arrivals
Total Civilian Arrivals

24. 24
Selected Examples
Major Pharmaceutical Company
• Problem: How are economic conditions impacting customer consumption behavior, and how
to better position sales and marketing efforts in light of future economic changes?
• Solution: Develop a model to forecast customer behavioral responses to economic
conditions
— Linked Medicare, Medicaid, MSA level economic, IMS prescription, and internal client
prescription and marketing data
• Results: Losses occurring due to branded to
generic switching
— Generics grew disproportionately and were
forecast to continue to grow as
• Medicaid enrollment increase
• Changing customer preferences
— Direct to consumer coupons and patient
assistance programs had the most pronounced
impact on mitigate this switching
Branded to Generic Growth Rate
Branded Growth Rate
Generic Growth Rate

25. 25
Decision Analytics Implications
for the Bottom Line
Implications for Your Business
• Improve competitiveness and profitability
• Leverage 3rd
party data to surface additional critical information
• Gain insight to evaluate business decisions before you act

26. 26
Decision Analytics Implications
for the Bottom Line
• With the right data and analytics, you can
find opportunity in any environment.

27. Thank You for Your Participation!
Presented by:
Larry Boyer
Director, Decision Analytics
IHS Global Insight