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The way a company makes the most of existingknowledge within the business is a determining forceto its overall financial and operational success . . .

(Peter Nicol, VP, OutStart EMEA,

a learning and knowledge sharing solution company)

3

Era of Information Overload

988

161

(forecasted)

Digital data generated worldwide, in exabytes

Exa

byt

es

4

Source: USA Today, March 6, 2007

Worldwide:

• 161 exabytes of digital data were generated in 2006, says researcher IDC

Putting 161 exabytes in perspective, that's roughly the equivalent of:

− 36 billion digital movies

− 43 trillion digital songs

− 1 million digital copies of every book in the Library of Congress

Per Person in North America:

• About 213 gigabytes of information were generated for each person inNorth America in 2006 , roughly the equivalent of 100K digital books

Sources: IDC, UC Berkley, CIA World Factbook, USA TODAY research

Why The Drive to Amass Information?

Critical decisions need to be made . . .

• Information is needed to build knowledge

• Knowledge will guide “correct” decision-making

• Correct decision-making will be rewarded

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Let’s look at how this manifest in thePharmaceutical Industry . . .

• Multiple factors are acting to constrain brand potential

– Increasing number of new products (particularly “me-too’s”)

– Increasing number of generic options

– Greater access restrictions to branded products

– Greater restrictions on marketing to physicians

– Decreasing impact of personal detailing

Brand Teams are Charged w/ Maximizing Brand Potentialin an Increasingly Challenging Marketplace

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• Within this market, Brand Teams are required to make

critical decisions for maximizing share, revenue and/or

margins for their assigned brands

MR’s Role: Provide Decision-MakingSupport to Brand Teams

• Brand Teams rely on MR to provide information that

supports decision-making around strategy/tactics

End Goals:

– Maximize share capture

– Increase revenue/margins

• To that end, MRDs acquire information intended to support• To that end, MRDs acquire information intended to support

the Brand Team’s decision-making needs

7 MRDs: Market Research Departments

Information Typically Available to SupportKnowledge-Building

PharmaceuticalIndustry

Secondary Syndicated / Internal Custom / Primary

IMS NationalPrescription Audit(NPA) / Xponent or WoltersKluwers

Brokerage Firms’Industry Reports

Forecastingsupport

Versipan’s VONA(RetailDispensing)/IMS NationalSales Perspectives (NSP)

Manufacturing/ “ProductShipped” Data

ATU / A&U

Verispan’s Meetings& EventsAudit (PMEA)

Physician CommunicationsResearch

Patient Tracker Data/Report MARS/Simmons/MRI Patient CommunicationsResearch

Product / Marketing Support

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Research

IMS NationalDisease andTherapeutic Index (IMS NDTI)

Therapeutic Category Reports

IMS PlanTrak Health Strategies / HIRC MC Communications Research

MMIT FormularyCompass/Fingertip Data

MC “Status” andupcoming contracts

Formulary AcceptanceInfluencers

IMS Integrated PromotionalServices (IPS)

Call Reporting Data

Territory Alignment and SalesForce Deployment

Impact RX – Sales RepInteractions

Sales Force Structures&Strategies Reports

Sales Rep PerformanceEvaluationResearch

Rep-Msg RecallResearch

Managed Care Support

Sales Force Support

MRs Collect Information, But Information Does NotNecessarily Translate Into Knowledge

Barriers to Knowledge

• MRD’s information sources are often legacy acquisitions,

ad hoc projects, and/or “silo-ed”

“Knowledge" accrues when information is understood incontext and relative to the existing patterns of relationshipwith other information

ad hoc projects, and/or “silo-ed”

• Sources are seldom linked to provide a comprehensive view

of the market and what drives Rxing

• Rarely is information collected to provide ongoing and

“linked” reconnaissance on market factors that might,

or are known to, impact the brand’s “bottom-line”

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“Silo-ed” Information

Sales ForceOptimization

Managed CareAccess

ProductMarketing

10

Prescribing Data(IMS Xponent/ Wolter Kluwer’s)

Physician Preference or StatedIntentions

Product MarketingSales Force

Optimization

“Silo-ed” Information

XXXX

Managed CareAccess

11

Linked Analysis to SupportBusiness Goals

XX

XX

XX

MR Recommendations Typically Limited

• MR agendas can be reactive and static, rarely including

comprehensive surveillance of a dynamic marketplace

• Projects tend to focus on one aspect of the marketing mix,

in isolation, not accounting for the inter-related influence of

Lack of “Big Picture” knowledge limits MR’s ability to “sitat the table” during brand strategy planning

in isolation, not accounting for the inter-related influence of

each component of the “whole”

• Rarely based on ROI; based more on respondent feedback

or the “statistical” finding

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Model misspecification leads to “wrong answers”

This leads to brand strategy/tactics that may not ultimatelyadvance the “bottom-line”(Worse: may have negative ROI)

MR’s Decision-Making Support Gains Value andCredibility Through Brand Knowledge Building

• Foundation for knowledge building is “Linkage Analysis”

• “Linkage Analysis” is a knowledge management system that

allows MRDs to:

– Know What Matters

– Avoid Wrong Answers– Avoid Wrong Answers

– Make Recommendations That Enhance the “Bottom-Line”

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Knowing What Matters

Create a Business Blueprint Example ONLY

BrandEvaluation

Business Impact Measures Business Results

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Gross Revenue

Market Share

Overall PhysicianLoyalty/Satisfaction

Detailing/Sampling

Sales RepService &Support

Managed CareAccess

Must involve key stake-holders from business supporting functions

Knowing What Matters

BrandEvaluation

Business Impact Measures Business Results

Source the Business Blueprint Example ONLY

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Gross Revenue

Market Share

Overall PhysicianLoyalty/Satisfaction

Detailing/Sampling

Sales RepService &Support

Managed CareAccess

Not accounting for the impact of animportant component of theBusiness Blueprint results in ModelMisspecification

Avoiding Wrong Answers

• The Phantom Menace: Model Misspecification

– Our theories are always a simplification of “Truth” and all ourmeasures are imperfect reflections of the “Truth”; thus allmodels are misspecified to some degree

– HOWEVER, our task is to seek models that are sufficientlyspecified, to minimize our chances of a “wrong answer”

• There are four basic types of model misspecification:• There are four basic types of model misspecification:

– measurement error

– erroneous functional form for the relationship

– inclusion of an irrelevant variable

– exclusion of a relevant variable (omitted variable bias)

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– exclusion of a relevant variable (omitted variable bias)

Avoiding Wrong Answers

$$

HomeSellingPrice

Square Footage$86,000 per 1000

MisspecifiedModel

Goal: Maximize the SellingPrice of My Home

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PriceNumber of Baths$32,000 per bathsover one

Results suggest:A. Adding an office (500 sq ft)

increases selling price by $43KB. Adding a bath will increase

selling price by $32K

Given similar cost, choose A

Omitted Variable

Not Included inMisspecified

Model

New/Used

Avoiding Wrong Answers

$$

HomeSelling

Square Footage

Number of Baths

$42,000 per 1000

$44,000 per

$86,000 per 1000

$32,000 per baths

MisspecifiedModel

True Model

Goal: Maximize the SellingPrice of My Home

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$$ SellingPrice

Number of Baths

New/Used

$44,000 perbaths over one

$56,000 for newvs used

$32,000 per bathsover one

“True Model” suggests adding a bath will better achieve goalA. Adding an office (500 sq ft) increases selling price by $21KB. Adding a bath will increase selling price by $44K

Avoiding Wrong Answers

$42,000 per 1000

$44,000 per

$86,000 per 1000

$32,000 per baths

MisspecifiedModel

True Model

$$HomeSelling

Square Footage

Number of Baths

Goal: Maximize the SellingPrice of My Home

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$44,000 perbaths over one

$56,000 for newover used

$32,000 per bathsover one $$ Selling

Price

Number of Baths

New/Used

Action Plan flawed due to misspecified model

• A component that directly impacts the selling price was omitted

Avoiding Wrong Answers

Goal: Maximize BrandShare / Revenue

$$MarketShare /

Sales Force

Product Quality

Hypothetical Example in Pharma

$$ Share /Revenue

Product Quality

Managed CareSF Analytic Results based onmisspecified model suggests:A. Increasing sample increase share

by 1.5% shareB. Increasing category knowledge will

yield 1.2% share

Recommendations implemented, but results don’t accrue.Why? Action plan was flawed because a component known to impactbusiness result was omitted

Avoiding Wrong Answers

Avoid Model Misspecification

• Spend the requisite time needed to build a consensus

“Business Blueprint”

– Proper model building is based as much on experience andmarket knowledge as research “learnings”

• No statistical test will reveal a specification error due to anomitted variable biasomitted variable bias

• Always explore hypotheses about the relationship of

“impact” variables, as well as what might be missing

– Remember the Phantom Menace

Linkage Analysis

Number ofDetails

“Valued” Rep

Manage CareAccess

New Product - Diabetes

.38.12.32

22

PhysicianPreference

ProductValue

Share ofTRxs

BLINDED CASE STUDY(Illustrates “point,” not actual findings)

Company Image/Presence

.05

.35 .56

Linkage Analysis

Mature Product – Enzyme Replacement

Number ofDetails

“Valued” Rep

Manage CareAccess

.10.18.32

23

PhysicianPreference

ProductValue

Share ofTRxs

BLINDED CASE STUDY(Illustrates “point,” not actual findings)

Company Image/Presence

.35

.08 .56

The Validity of Linkage Analysis

• There is a relationship between using linkage analysis and

improved business performance

– Linkage analysis is synonymous with building causal modelslinked to business outcomes

23% of Companies: Extensive linkage and validation

Those 23%, on average, had 3% higher ROA

77% of Company: Minimal linkage and validation

Those 23%, on average, had 3% higher ROAand 5% higher ROE than companies thatdidn't use linkage (i.e., causal models)

Christopher Ittner and David Larcker, “Coming Up Short on Nonfinancial Performance Measurement”Christopher Ittner and David Larcker, “Coming Up Short on Nonfinancial Performance Measurement”

Harvard Business ReviewHarvard Business Review (November, 2003)(November, 2003)24

Making Recommendations That Enhancethe “Bottom-line”

• Avoid the temptation to provide recommendations based

on “statistics” or respondent-preference

• Focus, instead, on the “cost-benefit” or ROI of the

considered improvement initiative

Model of Impacts on Rxing Statistical Effect

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Model of Impacts on Rxing

• Product Efficacy

• MC

• Sales Force “Perceived Value”

• Detailing

Statistical Effect

.56

.32

.32

.32

Sampling

Category Knowledge.35

.25

Making Recommendations That Enhancethe “Bottom-line”

• Ongoing reconnaissance tracks:– The changing market– Progress on share building initiatives– Brand Team assumptions about what impacts the market

Listen(Obtain Feedback)

Implement Change

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(Obtain Feedback)

Analyzethe Data

Measurement

Define Action Paths

Management

Continuous Processfor Monitoring

Drivers of Rxingand Profitability

Brand Knowledge Building

• . . . through “Linkage Analysis ” – a system for managinginformation that allows MR to:

– Know What Matters

• Blueprinting , together with continuous reconnaissance of themarketplace, keeps the Team updated on factors shown to impactthe “bottom-line”

– Avoid Wrong Answers

• “Linked” analysis of all factors known/thought to impact the“bottom-line”

– Make Recommendations That Enhance the “Bottom-line”

• Recommendations, while they do not discount respondentpreference and stated intentions, are based on a cost/benefitanalysis of impact on the “bottom-line”

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For more information, please contact . . .

Linkage Analysis Help SupportsDecision-Making for Strategic Advantage

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Susan Lee SimpsonVP, Senior Account Executive

513-564-8382

susan.simpson@burke.com

Thomas MillsVP, Statistical Consultant

513-564-8381

thomas.mills@burke.com