Join us as we present at PRMG Connect in Orlando, FL!
In this workshop, you will learn how to design and build models quickly and efficiently while managing the statistical error inherent in any forward-looking exercise. The workshop itself will combine didactic learning with group exercises to provide a sense of critical success factors in building models under uncertainty and offer a couple of specific tricks you can use in organizing the process.
Complex Diseases: Challenges in Forecasting Physician Treatment Decisions under Multiple Market Scenarios
In this session, we will focus on some of the common assumptions marketers and researchers make that can lead to models, strategies and product positions that are simply wrong, and how to avoid the assumptions that lead to these errors. The presenters will build on RG+A’s extensive history in market simulation and modeling, including errors we have seen, to provide some simple benchmarks for building better forecasts and support tools.
In this session, Ms. Park will discuss some of the commercial imperatives that led Quest to make improvement and tracking of customer experience and Mr. Martin will describe some of the methodological and practical challenges that RG+A faced when creating a set of marketing research tools and studies to support Quest’s efforts. In the process, the two will address issues such as identifying the proper study design and core measures, challenges inherent in tying customer experience to financial metrics in a complex, multi-stakeholder environment, and how companies looking to upgrade their customer experience activities might proceed to do so in a systematic way.
Small sample research, once the way to “do it on the cheap,” has now become a pivotal tool for all kinds of research exercises. In this workshop, you’ll learn that small sample size doesn’t have to mean sacrificing the quality of the research. Using recent studies as a backdrop, we’ll show how some new methods – and new ways of looking at old ones – can help studies with small sample sizes produce projectable and actionable insights.