Evaluating Group Practice Level Response Modeling – Should Large Account Management be the New Pharmaceuticals Selling Model?
Submitted October 2007 to the PMSA 2008 Abstract Review Committee
Presented April 29th, 2007 at PMSA
Katherine Cooley - Principal, MAG
Submitted under the topic of: Best Practices for a Pharmaceuticals Business Analytics Department Area: Response Modeling Addressing: New Dynamics in the Pharma Industry
Unique Because: Challenges the Status Quo Design for Sales Activity Planning and Suggests a Possible Approach to Reduce Sales Force Expense – Critical to the Success of Tomorrow’s Pharmaceutical Landscape.
The advent of physician level data gave way to a new paradigm in the pharmaceuticals sales model. Marketing scientists reveled in the ability to model response and segment physicians for appropriate call and sampling activity based on their individual volume, share, trend and other markers for adoption or prospective use. But, this granularized approach and corresponding criteria for segmentation may have obscured some of the practical advice such data may have to offer and resulted in an unsustainable swell of sales reps and over sampling.
We explore, via a Cardiovascular case study example with actual data and analysis, the pros and cons of group practice level response modeling to determine appropriate sales call and sampling activity. We further investigate some of the possibilities presented by a “Large Account” (aka Group Practice) approach to developing overall sales and marketing strategy. Specific topics discussed will include:
Group Practice Data Sources (no commercial spin)
Best Practices in Customer Groups Data Collection
Method of Analysis Employed in Case Study
Implied Findings of Analysis
Difference in Optimals for Individual Physician vs. Group Approach
Resulting Economics
Suggested Construct for Employing Group Level Analysis
Implications for Overall Marketing and Sales Strategy
Employs the following data types:
Web MD Blue Book Group Practice Audit Data
IMS Xponent Physician Level Data
Wolter’s Kluwer National Rx Data
Company Call Activity Data
Reconciling Stated Preference Share with Actual Prescribing Behavior – Generating Realistic Pharmaceuticals Sales Forecasts with Primary Quantitative Instruments
Submitted October 2007 to the PMSA 2008 Abstract Review Committee
Fatma Aybegum Yavuz - Principal, MAG
Submitted under the topic of: Best Practices for a Pharmaceuticals Business Analytics Department Area: Methods, Research Design and Forecasting Addressing: Marriage of Market Research with Secondary Analytics in Practical Forecasting Applications Unique Because: Draws on best practices as well as exploits first hand exposure to a volume of studies allowing for empirical review and insight from those who are developing and implementing late breaking methods. Interesting Because: This presentation will leave a documented “do-it-yourself” road map for industry professionals to calibrate their own research.
Often the most costly component of a well designed forecasting study is the primary quantitative market research element. Large samples of sometimes multiple audiences are used to generate preference shares for products in development or undergoing some alteration in-market. Such studies yield raw figures which - for a host of reasons detailed in this presentation - can not be directly applied to forecasting models.
Specific Topics:
• Establish how primary instruments are used in forecasting
• Articulate why raw figures from quantitative primary studies are faulty
• Establish product and market attributes which assist in defining the appropriate approach to reconciling study outputs (e.g. small share or large share product, time in market, market maturity…)
• Front end design practices that yield clear paths to reconciliation
• External validation methods (triangulation).
• Smash open industry black boxes by clearly detailing practices used to calibrate primary quantitative studies
• Add to the compendium of knowledge on the topic by revealing empirical findings and methodologies developed by the authors after executing a large number of such studies.
• External validation methods
Case Study Review:
This presentation will incorporate case study examples which marry primary research with secondary data and models to create realistic, defensible forecasts.
Case study examples include:
Neurologic Disorders Case Study (Basic Application)
• Reviews calibration process including front end design of a quantitative discrete choice study for ultimate plug in into new product demand forecast
Case Study (Advanced Application) - Respiratory Diseases
• Chronicles a three part study which marries and reconciles physician, patient and payor primary studies with secondary data to yield forecast
Employs the Following Data Types, Tools and Techniques:
Quantitative Primary Data Collection
Audited Market Share Data (IMS/WK or the like)
Juster Scale and like instruments
Curve fitting techniques
Analogs
Triangulation
Forecast Analogs as Predictors of Share for New Products – Still Appropriate in Tomorrow’s Rx Landscape?
Submitted October 2006 to the PMSA 2007 Abstract Review Committee
Presented May 1st, 2007 at PMSA
Fatma Aybegum Yavuz - Vice President, MAG
Eduardo Sabo - Managing Consultant, MAG
Submitted under the topic of: Best Practices for a Pharmaceuticals Business Analytics Department Area: Forecasting Addressing: New Dynamics in the Pharma Industry Unique Because: Eduardo and Fatma have collectively conducted over one hundred pharmaceutical product forecasts developing unique insights into the nuances of predicting Rx market dynamics. Such exposure has made them exceptionally aware of changes in the landscape that will affect future forecasting approaches. This presentation leverages this vast experience and insight to lay the groundwork for future thinking surrounding approaches to forecasting.
One common approach for estimating potential product share for a new product is to use analog classes and products as benchmark values. We seek classes and products that share some of the same features or circumstances that the new product will bring to market.
In some cases we look for similarities with respect to the product profile; better efficacy, improved safety or dosing. In others cases we look for similarities in the market dynamics, e.g. order of entry, share of voice and DTC spend level.
Resulting share assumptions are supported using valid historical examples…
“Product X share will be like Analog 1 because our product will be second in class, with better safety and improved dosing…”
The future, however, is not as clear; companies are using more targeted approaches for drug discovery and mechanisms of action are becoming increasingly specific. In a given condition there will be multiple drug launches, each with a potentially different MOA. Examples include:
CV: ACE, ARB
Respiratory: LABA, GABA
Schizophrenia: Typical AP, Atypical AP
Depression: TCA, SSRI, SNRI
Cholesterol: Fibrates, Statins
The future, however, is not as clear; companies are using more targeted approaches for drug discovery and mechanisms of action are becoming increasingly specific. In a given condition there will be multiple drug launches, each with a potentially different MOA. Examples include:
Alzheimers Disease
Current: AChEi, NMDA
Future Disase Modifiers, Beta Amyloids
Epilepsy:
Current: 1st Generation and 2nd Generation AED
Future: GABA, AMPA, Sodium Channel Blockers
Oncology:
Current: Taxanes, Alkaloids, Alkylating Agents
Future: MAb, RTK, other
Depression:
Current: TCA, SSRI, SNRI
Future: TRI, 5HT2s
Such landscapes threaten to muddy the analog process rendering criteria such as “order of entry” inappropriate.
This presentation explores answers to the resulting questions:
• Under future market conditions, is the model of class and product analog still valid?
• What is the right set of questions to ask in order to find a proper product analog in
future therapy landscapes?
Historical analog data will be presented and case study pipelines in the aforementioned disease states will be shared.
Evaluating Group Practice Level Response Modeling – Should Large Account Management be the New Pharmaceuticals Selling Model?
Submitted November 2006 to the PMSA 2007 Abstract Review Committee
Katherine M. Cooley - President, MAG
Fatma Aybegum Yavuz - Vice President, MAG
Submitted under the topic of: Best Practices for a Pharmaceuticals Business Analytics Department Area: Response Modeling Addressing: New Dynamics in the Pharma Industry Unique Because: Challenges the Status Quo Design for Sales Activity Planning and Suggests a Possible Approach to Reduce Sales Force Expense – Critical to the Success of Tomorrow’s Pharmaceutical Landscape.
The advent of physician level data gave way to a new paradigm in the pharmaceuticals sales model. Marketing scientists reveled in the ability to model response and segment physicians for appropriate call and sampling activity based on their individual volume, share, trend and other markers for adoption or prospective use. But, this granularized approach and corresponding criteria for segmentation may have obscured some of the practical advice such data may have to offer and resulted in an unsustainable swell of sales reps and over sampling.
We explore, via a Cardiovascular case study example with actual data and analysis, the pros and cons of group practice level response modeling to determine appropriate sales call and sampling activity. We further investigate some of the possibilities presented by a “Large Account” (aka Group Practice) approach to developing overall sales and marketing strategy. Specific topics discussed will include:
Group Practice Data Sources
Best Practices in Customer Groups Data Collection
Method of Analysis Employed in Case Study
Implied Findings of Analysis
Difference in Optimals for Individual Physician vs. Group Approach
Resulting Economics
Suggested Construct for Employing Group Level Analysis
Implications for Overall Marketing and Sales Strategy
Employs the following data types:
Web MD Blue Book Group Practice Audit Data
IMS Xponent Physician Level Data
Wolter’s Kluwer National Rx Data
Bridging the Gap – A Renaissance Approach
Submitted October 2003 to the PMSA 2004 Abstract Review Committee
Katherine M. Cooley - President, MAG
Diane Ray - Vice President, Innovation Focus, Inc
Hedi Nesturuk - Principal, Life Sciences Consulting
Topic: To some degree, nearly every pharmaceuticals company suffers from an established culture that promotes a communication barrier between Marketing Science, Market Research and Marketing departments. This is often further exacerbated by the gap in terminology and skill sets between Marketing Scientists and Brand Teams. The result is that insights held by the data, information and research are not being fully leveraged when developing positioning/branding and core strategies – a.k.a. less than optimized brand profitability. This presentation will provide a toolbox to help both the Marketing Science and Marketing professional apply Renaissance thinking – a blend of science, art , business and psychology - to make the contributing departments move together in symphony. Sounds impossible? Let us show you how others are doing it and help you take the first step in building your toolbox!
Bridging the Gap between Science and Marketing: Prepare, Position, Profit
Many bio-techs pursue new science then have difficulty both approaching a marketing firm and clinching the deal. This discussion will focus on creative strategic and tactical preparations to strengthen the foundation of your bridge, how to develop and present a desirable positioning for your science, and how to come to the negotiating table prepared to profit. Hear real-life examples of what a venture capitalist (or other funding source) is looking for in order to support science development and discuss considerations in structuring the science development to begin to build the bridge to marketing. Get an overview of analytical and preparatory tools that no biotech firm should be without - creativity skills, analysis and pre-positioning work, buyer scenario planning, in-depth positioning and financial modeling, etc. Hear perspectives from biotechs, investment sources, marketers and pharmaceutical decision makers around how to build a strong bridge from the science to the end market.
Proposed Speakers
Chair: Diane Ray, Vice President, Innovation Focus, Inc.
Co-Chair: Hedi Nesteruk, Principal, Life Sciences Consulting Group
Speaker 1: Frank Maresca, Principal, FMA - Saratoga Equity
Speaker 2: Bill Ponce, VP, Business Development & Sales, Pharmion Corporation
Speaker 3: Henric Bjarke, Sr. Director Marketing, Eye Tech Pharmaceuticals
Speaker 4: Katherine Cooley, President, Market Analytics Group, Inc.
Copyright 2008 Market Analytics Group
