1. Optimize the Role of Predictive Analytics For Rep
Effectiveness Using Patient Longitudinal Data
Amp Idea, Inc.
Amplify your idea
www.ampideainc.com
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2. Historical Pipeline - New Product Launches
Drug-1
High
Drug-2
Revenue
:
$$$
Drug-N
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3. Current Pipeline - New Product Launches
Low
Drug-1 Revenue
Drug-2 $
Drastic Reduction in New Drugs Approval
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4. Problem
Reduced revenue and margin due to shrinking drug pipeline.
Current
Historical
Marketing and sales budgets are shrinking while the pressure is
mounting
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5. Sales Practices Losing Effectiveness
Traditional Call Allocations
 List of “No See” doctors is increasing,
 Average length of sales calls has been reduced by physicians.
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6. Improved Call Plans Using Patient Data
Traditional Call Allocations
Optimal Call Allocations
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7. Result
 Millions of dollars of wasted sales resources in sub optimal
sales territories
 Research shows that over 80% of sales territories have
significant design flaws and targeting issues.
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8. Traditional Marketing Analytics
Promotion Sales
Data Data
Resource Allocation Analytics
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9. Traditional Marketing Analytics
Promotion Sales
Data Data
Resource Allocation Analytics
“90% reps finds Call Plans to be inadequate”.
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10. Filling Blind Spot
Promotion Payer Patient Sales
Data Data Data Data
Resource Allocation Analytics
“90% reps finds Call Plans to be inadequate”.
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11. Patient/Payer Data
 Conventional resource optimization solutions are silo-
based, and often ignore other local market dynamics,
including:
 Patient and disease indication composition
 Formulary tier and co-pay differential
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12. Brand A Increases Marketing Budget - No Sales Impact
Brand A
Physician Market Brand A Brand B
Doctors Specialty Decile (Share
Cluster ('000) Share Share
Segment)
V-L 800 $80,000 Primary Care Very High Low 25% 75%
V-H 1,200 $120,000 Primary Care Very High High 60% 40%
Why does Brand A have low share among
Physician Cluster V-L?
 Formulary Status
 Disease Indication
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13. Prescription - Marketing Efforts
Physician Focused
Physician E-prescription
Prescription
Claim
Managed Care
Patient Pharmacy
Plan
Co-pay
Y/N Physician
Approval
Medicine
Dispensed
Claim Sent
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14. Prescription – Pharmacy Makes Switch
Physician E-prescription
Prescription
Claim
Managed Care
Patient Pharmacy
Plan
Co-pay
Alternate Drug Y/N Physician
If High Co-pay Approval
Medicine
Dispensed
Claim Sent
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15. 360 Degree View Of Data
Prescribers Market Events
Patients/Caregivers
Brand Brand
Performance Promotion
Traditional Optimization
Not Utilized in Optimization
Historical Data, Payers and Plans
Market Research, Analytic Results
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16. Integration of Payer Data with Physicians Sales Data
 Able to more effectively target the right physicians, with the right
message, at the right time.
 The time needed to communicate formulary updates to physicians can be
shortened, thus increasing revenues.
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17. 360 Degree View Of Data –
Patient Longitudinal Data
Patient Segmentation
Disease Disease
Age Gender Race
Indication Severity
Physician Segmentation
Product Managed Patient
Region Specialty Volume Share
Dominance Care Segment
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18. Traditional Forecasting Framework
Class Volume = Retail Sales of all the Competing Brands,
Brand Forecast = Projected Market Growth and Brand Share By Region/Segment
Historical
Class Volume
(Brand-A)
Rx (Brand-A) Rx (Brand-B) Rx (Brand-C)
Market Definition
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19. Traditional Segmentation Framework
Class Volume Product Group Shared Managed Care
Specialty
(Brand-A) Dominance Trend (Co-pay By Plan)
Rx (Brand-A) Rx (Brand-B)
Traditionally Class Volume is Calculated
by Adding TRx of all the Brands Defined in
the Class.
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20. Data Quality Management using
Managed Care Plan Data
Market Potential
Brand A
Physician Market Brand A Brand B
Doctors Specialty Decile (Share
Cluster ('000) Share Share
Segment)
V-L 800 $80,000 Primary Care Very High Low 25% 75%
V-H 1,200 $120,000 Primary Care Very High High 60% 40%
Data Pre-Processing
And Staging Payer Data
(Business Rule)
Market Potential By Access
Brand A
Physician Market Brand A Brand B
Doctors Specialty Decile (Share
Cluster ('000) Share Share
Segment)
L-L 800 $24,000 Primary Care Low Low 25% 75%
V-H 1,200 $96,000 Primary Care Very High High 60% 40%
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21. Focus on Disease Indications
Brands - Disease Indications
Indication - 1 Indication - 2 Indication - 3 Indication - 4
Brand-A Yes Yes
Brand-B Yes Yes Yes Yes
Market Rx By Disease Indication
Share
Physician Cluster Indication - 1 Indication - 2 Indication - 3 Indication - 4 Doctors Decile
Segment
V-L 5% 5% 50% 40% 800 Very High Low
V-H 40% 60% 0% 0% 1,200 Very High High
Conclusion: Based on Patient’s Disease Indication, Brand-A should receive
significantly less Details for Physician Cluster V-L
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22. Data Quality Management using
Drug Indication
Market Potential
Brand A
Physician Market Brand A Brand B
Doctors Specialty Decile (Share
Cluster ('000) Share Share
Segment)
V-L 800 $80,000 Primary Care Very High Low 25% 75%
V-H 1,200 $120,000 Primary Care Very High High 60% 40%
Data Pre-Processing
Patient Data And Staging Drug Indication
(Business Rule)
Market Potential By Indication
Brand A
Physician Market Brand A Brand B
Doctors Specialty Decile (Share
Cluster ('000) Share Share
Segment)
L-L 800 $8,000 Primary Care Low Low 25% 75%
V-H 1,200 $120,000 Primary Care Very High High 60% 40%
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23. Market Definition : Traditional
Approach – Treated Market
Traditional approach defines the Hypertension
market based on treatment option. Market
Angiotensin II
Calcium Channel
Receptor Beta Blocker ACE Inhibitor
Blocker
Antagonists
Azor Azor Lopressor Lotensin
Tribenzor Benicar Toporol - XL Capoten
Cardizem Benicar HCT Bystolic Zestril
Exforge Diovan Coreg Prinivil
Lotrel Lasix Inderal Accupril
Norvasc Micardis Metoprolol Altace
Tarka Edarbi Tarka
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24. Market Definition: By Disease
Indication – Market Potential
Hypertension
Market
Identify product attributes that will
maximize actual prescribing
High Blood Pressure
Abnormal Heart Rhythm High Blood Pressure
Heart Failure Heart Failure
Angina Renal Disease
High Blood Pressure Tremor Stroke
High Blood Pressure Diabetic Nephropathy
Abnormal Heart Rhythm Pheochromocytoma
Angina Heart Failure Left-Ventricular Hypertrophy
Migraines.
Angiotensin II
Calcium Channel
Receptor Beta Blocker ACE Inhibitor
Blocker
Antagonists
Azor Azor Lopressor Lotensin
Tribenzor Benicar Toporol - XL Capoten
Cardizem Benicar HCT Bystolic Zestril
Exforge Diovan Coreg Prinivil
Lotrel Lasix Inderal Accupril
Norvasc Micardis Metoprolol Altace
Tarka Edarbi Tarka
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25. Realistic Physician Decile
Number of Physicians By Segment
(Traditional Approach)
Very High High Medium Low
Benicar 4,000 8,000 12,000 80,000
Number of Physicians By Segment
Total
(Disease Indication and Access)
Very High 1,600 200 100 100 2,000
High 1,100 3,000 1,000 900 6,000
Medium 800 2,200 8,000 5,000 16,000
Low 500 2,600 2,900 74,000 80,000
After applying access and disease indication filter,
 1,600 very high, 3,000 high and 8,000 medium volume
prescribers retained their respective decile segmentation.
 200 high volume, 100 medium and 100 low volume prescribers
moved to very high segment.
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26. The Net Result: Improved Call Plan
Prioritizes Targets Based on Traditional Call Allocations
Sales Data AND Local Market
Demographics of the Patients,
Plans and Physicians:
 “No See” doctors Based On:
 Communication Preferences
 Formulary Status Remove “No See” Doctors New Called
 Disease Indication On Doctors
Optimal Call Allocations
Better Targeting, Call Allocations &
Territory Alignments Compared to
Traditional Call Allocation
Methodology
In this example, dropping lower-value doctors from the customer target list and
adding higher potential doctors would significantly improve market coverage
and incremental sales.
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27. Next Gen Forecasting Framework
Class Volume = Retail Sales of all the Competing Brands By Disease Indication,
Brand Forecast = Projected Market Growth and Brand Share By Region/Segment
+ Disease Indication + Managed Care Impact
Historical
Class Volume
(Brand-A)
Indication-1 Indication-2
Rx (Brand-A) Rx (Brand-B) Rx (Brand-A) Rx (Brand-C)
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28. Next Gen Segmentation Framework
Product Group Shared Managed Care
Specialty Class Volume
Dominance Trend (Co-pay By Plan)
Amp Idea, Inc.
Patient
Rx By Diagnosis
Diagnosis proposes that
Class Volume
should be
Interim Adjusted Calculated after
Class Volume
Filtering TRx from
the Competing
Business Rule Co-Pay By Brands Based On
(Co-pay) Plan Disease Indication
and Managed Care
Final Adjusted
Plan.
Class Volume
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29. Resource Allocation Process –
Optimal Reach and Frequency
Market
Segmentation
Specialty Historical
Promotion
Rx/Sales Data Evaluation
Changes in market
segmentation will reflect
Call Data Product/Portfolio in territory
Resource
Samples Optimization
alignment/incentive
compensation and
Patient Data
territory alignment in the
Call Planning
Disease Indication marketing resource
Payer Data
optimization process.
Incentive
Compensation
Territory
Alignment
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30. Excel Based Analytic Information Hub - Provide
Better Insights into Brand Performance
Sales/Marketing Data Analytic Engine
(CRM) (SAS)
Provider/Patient
Data Excel Based
Key Performance Extraction, Management
Transformation, Dashboard
Indicators (KPIs) (Data Integration &
Loading
Business Intelligence)
(Excel/SAS)
Payer
Market Research/ Data Warehouse Report
Analytics Results (SAS, DB) Generation
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31. Benefits
 Better physician targets, less wasted marketing
resources.
 Appropriate adjustment in marketing resources for
targeted physicians based on:
 No see doctors
 Patient diagnosis information
 Managed care – Formulary status and co-pay
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32. Approach - Pareto Principle
 The Pareto principle states that, for many events,
roughly 80% of the effects come from 20% of the
causes.
 In other words, 80% of sales come from 20% of sales
reps.
 Review low performing territories using sales,
promotion, patient and managed care data.
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33. Opportunity
 By deploying a more robust patient-based analytical platform, and integrating a
broader set of local market intelligence metrics, we are better positioned to:
 Save millions of dollars per field force in annual sales team expenditures, and
potentially increase revenue in each territory.
 Provide grounded, empirically-based decisions around the ROI of patient-based
marketing initiatives.
 Based on proprietary research, moving beyond conventional resource
optimization techniques and incorporating a more robust localized solution,
brand sales performance can increase.
 By increasing productivity and improving customer targeting, Pharmaceutical
companies will improve the sales in non-performing territories
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34. Critical Business Questions To Explore?
 What does the complete competitive landscape for my product look like?
 How can we maximize the impact of our product launch tactics?
 How effective are our sales reps in accessing, engaging, and promoting to
targeted physicians?
 How well does our detailing activity drive brand choice compared with
competitors?
 Which core messages are most effective?
 Is it time to change our messaging strategy?
 Are sampling volumes driving share—or supplanting written prescriptions?
 Is our marketing campaign driving share from the targeted source of
business?
 How effective are our new sales strategies and tactics?
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35. Questions
www.ampideainc.com
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36. Author’s Bios
Praveen Mathur is Vice President of Business Analytics at Amp Idea. He works with clients
in establishing best practices in the area of sales optimization through combination of
customer and market information, advanced analytical processes and software solutions.
His areas of expertise include: maximizing ROI on marketing investment; customer
segmentation; promotion response modeling; field force portfolio optimization; and market
mix modeling using non-linear regression techniques.
Prior to joining Amp Idea, Praveen held various positions at inVentiv Health, leading cross-
functional teams of business analysts and software engineers in the development of
analytical tools to evaluate the effectiveness of sales and marketing initiatives.
Ram Bala is an Assistant Professor of Operations Management & Information Systems at
the Leavey School of Business, Santa Clara University. He holds a Ph.D. in Management
Science from the UCLA Anderson School of Management. His main research areas are
product line design, promotional effort allocation, global product development and pricing
and contracting strategies for services. His specific interest is the deployment of cutting
edge technologies to increase the “intelligence” of business decisions.
Prior to joining academia, he has consulted for several firms in the area of pharmaceutical
marketing analytics.
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37. Contact
 Praveen Mathur,
 Vice President - Analytics, Amp Idea, Inc.
 pmathur@ampideainc.com
 www. ampideainc.com
 732-619-3899
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