One of the biggest sources of confusion with my clients is whether to do small N or larger N qualitative payer research versus adding in Quantitative research. While the needs of each organization are different, here is my take.
Qualitative Payer Research:
One thing to keep in mind in any United States payer study is that the clear majority of lives in the U.S. are at least influence by the ‘Big Three’ PBMs. Estimates vary, but it’s clear that >80% of lives are covered by the biggest PBMs. Thus, I’ve made the argument that increasing the number of respondents can increase project budget without providing additional incremental insights. This is a little different if you find yourself with significant behavioral or attitudinal segmentation. But as I’ll demonstrate below, qualitative methods aren’t as good as Quantitative studies for market segmentation anyway.
So, what’s the right number of respondents? I consider the specifics company confidential to Chiral Logic – and I have a surprisingly high number of readers of my blog from competitors (thanks guys!) – but the basics are, if you can get 50% of lives for an early stage asset (and that’s all the money you have for a study…) you MIGHT be alright. Following the bouncing ball – you can get a ton of marketing insights, especially if response is homogeneous, from 10-12 conversations. The trick is you need the RIGHT Payers. It helps if your moderator has years of experience and isn’t intimidated to push back.
Large N (25-30 respondents) projects are often requested by pharma companies who want to go the extra mile. And, if your guide is too long to get through in an hour, or if you expect heterogenous responses, this might make sense. We moderators hate these studies though – it’s very difficult to stay excited about a project after the 25th HOUR of asking the same questions to different payers. Beyond that, you’re retreading the same path, repeatedly…
Quantitative Payer Research:
Quant is great for market segmentation. It’s ESSENTIAL to translate your qual research into the forecast and your qual was reported by respondent. (Chiral Logic doesn’t report our findings by respondent, instead using a methodology that can be directly tied to forecasting). Unfortunately, quant is expensive, requires longer timelines, requires a different skillset & partners, and can return the same insights that were found in qual.
Typical Use Cases:
Development assets typically need a small N assessment to get a developing picture of the competitive landscape, likely restrictions both at launch and at maturity, net pricing, and what kinds of value substantiating programs will maximize access.
Before launch (using either information from FDA submission or expected equivalents), I like to complete a comprehensive payer qual study. This study should be sufficiently powered to uncover attitudinal and behavioral segmentation. One of the benefits of the Chiral Logic approach is that we can scale the study, while it’s happening, to generate the insights required.
Then, in cases where forecasting requires, a follow-on quant study can fill in the gaps and eliminate any strategic discrepancies. Also, there are some launches that are so important to the company’s future that no stone should be left unturned…and in these cases a qual/quant methodology can be optimal.
There’s always the problem of getting a ‘normal’ distribution of U.S. payers (something that we’ve been thinking about for years and have a reasonable work-around). But this, I’ll leave for another blog. As my thoughts on the inherent dangers of these developing on-line platforms where marketers can communicate ‘directly with payers…’
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