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Blog: It Took Twenty Years to Become an Overnight Sensation by Roger Green
Published 05/06/2016

At a Spring PMRG meeting, the highest rated presentation, given before a standing-room-only audience, debuted a new mode of marketing research called patient simulation. The presentation focused on how this method provided more reliable data than traditional allocation and estimation methods and improved response and interview completion rates dramatically.

If you were at PMRG CONNECT 2016 earlier this week, you may assume you know the presentation to which I am referring.  However, you would be wrong.  This highly rated presentation about this intricate, effective methodology too place nearly a decade ago at the 2007 PMRG Annual National Conference in Las Vegas.  Boehringer Ingelheim and RG+A presented an approach to deploy patient simulation in support of 5-to-7-year lifecycle management for in-line brands.  Before 2007, RG+A had given presentations on patient simulation in 2000 at PMSA, with subsequent podium time in 2008 at PMRG, 2011 at both PMRG and EPhMRA, and 2015 at the PBIRG Fall Workshops.

Paul Simon once wrote, “Every generation throws a hero up the pop chart.” For those of you who learned last week that The Biebs is the real deal, here are five facts about simulation:

  • At a basic level, simulation is a choice-based platform for superior data collection. Simulation enables you to do is to collect data by having physicians “treat” realistic simulated patients. Critically, all other elements – sample size and structure, converting patient variables into realistic patients, instrument design and analytics – are entirely dependent on the skill and knowledge of the researcher.
  • The key to making simulation work depends on understanding how to structure the methodology to achieve your specific goal. RG+A has eightdifferent core templates for simulation studies. Depending on available sample, budget and timeline, researchers can conduct quantitative simulation studies and qualitative studies enhancing simulation results using small sample analytics and integrate simulation with several different forms of choice exercises. The results can project 5-7 years into the future, or compare different strategic and/or tactical options. Each approach involves differences in sample structure, instrument design, and core analysis.
  • Used properly, simulation can boost the insight power of studies addressing a diverse range of issues. Initially, RG+A developed simulation to support drug-pricing studies. Today, drug, device and diagnostic marketers utilize simulation studies also for positioning, value driver identification, competitive response, clinical trial design, stage gate assessment for products in development, and lifecycle management.
  • Simulation’s greatest utility is in forecasting. While researchers and marketers utilize the core simulation method to address a range of strategic issues, most simulation studies support forecasting at a more granular level than typical allocation or estimate-based marketing research. The most effective way to forecast how prescribers will treat patients starts with having prescribers treat simulated patients. It is critical to understand that the Devil is in the details.
  • Simulation is less suited to DIY than other choice methods. Sawtooth Software created a DIY conjoint package and has spent years creating a library teaching researchers how to use their tools to create robust conjoint exercises. Even then, most client-side researchers confronted with a complex choice issue will hire an agency to conduct conjoint studies rather than trying to bake their own. Simulation is far more complex than conjoint, offering greater opportunities and risks of failure or misinterpretation.

James Joyce once said about the first page of Finnegan’s Wake, “It took me 17 years to write it. It should take you a lifetime to read it.” Simulation is not literature, but there are more ways than one can count to do something that seems intuitive or logical but makes your highly precise results strategically wrong or operationally unusable. The biggest risk is that the researcher will never know what the study has wrong until study recommendations fail in the market a few years later.

Over the next few months, Bruce DuncanTim Deckman and I will be publishing a series of short pieces discussing some ways you can deploy simulation techniques to address specific market challenges.  We welcome the opportunity to help you learn more about how to use this 20 year-old but amazingly dynamic and vibrant platform.

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