Decision Analytics:Revealing Customer Preferences and Behaviors

Presented by:

Larry BoyerDirector, Decision AnalyticsIHS Global Insight

22 October 2009Boston, MA

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• 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

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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

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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

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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.

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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

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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

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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

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Why Does the Economy Matter?

House Price Changes, 2008Q4 Vs 2008Q3

< -10%-10% to -5%-5% to 00 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

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— 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

Percent 2.9 to 3.7 3.7 to 4.2 4.2 to 4.7 4.7 to 5.3 5.3 to 6.3

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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:

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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

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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

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What Do You Need to Succeed?

• Customer Level Data— Who was the customer?— Duration and frequency of customer interactions— Customer demographic characteristics

Cu

sto

mer

Completeness of Data — Demand Side

Available Data Array Integrated Data Array

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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

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• 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!

2211y

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

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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 AnalysisMarket Basket AnalysisEvents Analysis

Market SegmentationProfilingDefect AnalysisFraud Detection

Risk AnalysisPortfolio SelectionForecastingCredit Approval

Direct MailCampaign Management

Link AnalysisFrequencyAnalysis

Clustering ClassificationValue

Prediction

Associations SequentialPatterns

SimilarSequences

DemographicClustering

NeuralNetworks

DecisionTrees

Radial BasedFunctions

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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

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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 customersPredicted Penetration

Actual Penetration

Opportunity for Market Share Gain

Total Market

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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

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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

0.00MSA 1 MSA 2 MSA 3 MSA 4 MSA 5

Assistance Program

Coupon

Reduction in Asymptomatic RRx 2010

(Top 5 MSA)

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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

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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 ArrivalsTotal Civilian Arrivals

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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

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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

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Decision Analytics Implications for the Bottom Line

• With the right data and analytics, you can find opportunity in any environment.

Thank You for Your Participation!

Presented by:

Larry BoyerDirector, Decision AnalyticsIHS Global Insight