It’s easy to understand that new products must meet customer needs to be successful. Identifying unmet needs is often the first step in determining what products should be developed. As a next step, it is imperative to prioritize which product attributes will ultimately lead to adoption. In this webinar, we will demonstrate the use of discrete choice modeling to optimize clinical trial design by prioritizing which aspects of product attributes are most impactful to customers. We will present two case studies showing how choice-based conjoint techniques can be used in both pharmaceutical and medical device markets.
Heart disease is the leading cause of death in the US and the third leading cause of years of potential life lost (YPLL). With an increasing focus on population health, chronic disease management and migration to value based care to promote improved outcomes, patient satisfaction and cost reductions, it is critically important to understand the provider, payer, system and patient perspectives on adopting new technologies and products. In this webinar, we will highlight issues to consider when conducting cardiovascular research within the changing cardiac care landscape.
Have you ever had difficulty capturing feedback from your target population? This webinar will explore the role of online ethnography in creating a mutually beneficial research experience for both you and your target audience. KJT Group has experience conducting online ethnographies over the past eight years with a range of hard to reach populations. Join us to learn when this unique methodology can be used to answer critical business objectives.
Join us for this quarterly update where we discuss the ever changing US healthcare delivery and reimbursement system. We will examine issues impacting providers, patients, payers and health systems. We will report on the migration from fee for service to value based payment mechanisms and progress toward developing population health and coordinated care delivery models.
The advent of online research, in particular, online panels, promised to make very large samples affordable. Alas—while online panels have driven down CPI, small samples are commonplace, especially for B2B and healthcare research. In this webinar we share the results of an experiment we conducted to explore the impact of small samples on our ability to make inferences that guide decision-making. We show what happens to the stability of a discrete choice model as sample size decreases from about 400 to as low as 25, and we show how applying Bayes’ rule can help us make better decisions using data from small samples.