November 7, 2025

Understanding the “Why” Behind List Feasibility 

Imagine this common scenario: your pharma client wants to run an oncology study and provides you with a target list of 5,000 specialists. At first glance, that sounds like plenty, but when you receive the feasibility estimate back from your fieldwork partner, the projected number of completes is far lower than expected.

Naturally, your first question is why? Why doesn’t a list of 5,000 translate into 5,000 reachable respondents? Which factors shrink that number? And how can you explain this back to your client with clarity and confidence?

In this article, we explore the most common questions our partners raise about list feasibility. Feasibility is more than just a number – it’s about breaking down the variables behind the projection, including list quality, universe representation, incidence rates, and response behaviour. Our goal with this article is to equip you with the insights you need to have transparent and informed conversations with your clients.

Where the data comes from
In the United States, most client lists are built from large-scale prescription and sales data sets. These lists usually come from one or two major providers. While they are widely used, they have important nuances and limitations. For example, they are strongest in capturing retail prescribing, but hospital-based prescribing is often underrepresented.

How the data is captured in practice:

Step 1

A doctor writes a prescription for a patient

Step 2

The patient takes the prescription to a retail pharmacy, which processes it and records the fill

Step 3

Pharmacies source drugs from wholesalers, who in turn record distribution back to the manufacturer

Step 4

Data providers collect, clean, and structure these records, then break them into deciles (tiers of prescribing volume) or other prescribing levels

Step 5

Pharma companies purchase subscriptions to these data sets and may share subsets of them as target lists for market research

The way these lists are built directly affects how useful they are in research. A physician who prescribes a high volume of one drug may appear as decile 10 for that product but only decile 1 for another, which means no doctor has a single fixed decile. In addition, hospital-based specialists often look like low-decile prescribers because their prescribing activity is not fully captured until patients are discharged and prescriptions are filled in retail pharmacies. As a result, these doctors may be underrepresented in lists, and feasibility can be far lower than expected if you are trying to recruit them.

Why doesn’t 50% of 10 equal 5?

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Imagine you have a universe of 10 physicians. On paper, if your expected incidence rate is 50%, you might assume you will achieve five completes. In practice, it depends on who is on your list. If your list happens to contain only doctors who have never taken part in market research, your feasibility is zero, regardless of what the maths suggests. On the other hand, if your list includes doctors who are active participants, you may achieve four or more completes, but still not the neat five that the simple calculation implies. This is why feasibility cannot be reduced to straight percentages. It depends on which physicians are reachable and willing to engage, not just the number of names on the list.

“The Why” of Feasibility: Three main drivers
  1. List Quality: Quality considers how many of the records can be used for your project. For example, a client may send 50,000 names, but if 40,000 of them turn out to be nurse practitioners and physician assistants when the study only requires physicians, then the true usable list is only 10,000. This reduction directly affects feasibility.  
  2. Universe representation: Feasibility also depends on how representative the list is compared to the total target universe. A list of 5,000 oncologists may sound substantial, but if there are 270,000 oncologists in the wider universe, then the list only covers around two percent. When representation is too narrow, even a high-quality list cannot deliver the expected outcomes. 
  3. List distribution: Many projects require quotas by segment, such as prescribing decile or priority tier. The assumption is that the list supports all segments equally, but in reality, some segments are far smaller than others. For example, you may need completes across three priority groups, but if one group makes up only six percent of the list, hitting that quota will be extremely challenging. Without flagging distribution issues early, projects risk stalling mid-field when certain segments dry up.  
A Marriage of Two Variables
Even with a high-quality list that is representative of the universe and well distributed across segments, feasibility is still not determined by the list alone. Two other variables play a decisive role: incidence rate (IR) and response rate (RR).
  • IR is the percentage of respondents who qualify once they start the screener 
  • RR is the percentage of matched panelists who actually click through and attempt the survey 
Together, these variables dictate how many people need to be invited to achieve the target number of completes. For example, if you need 100 completes at a 70% incidence rate, you must drive about 143 respondents into the screener. If incidence drops to 40%, you now need 250 respondents.
At m360 Research, we estimate IR by reviewing your screener criteria, comparing it with past projects in the same therapy area, and drawing on any available client data. RR is calculated from our panel’s historical behaviour, looking at both recent activity and lifetime engagement. By combining these data points with list feasibility, we can forecast more accurately.
Conclusion
Feasibility is never just a single number. It reflects the story behind the list and the conditions that shape it. It reflects list quality, how representative that list is of the target universe, how segments are distributed, and the marriage of incidence and response rates. By breaking these drivers down and presenting them clearly, we help you explain the “why” to your pharma clients with confidence. Our role is to support you early in the process, highlight challenges before they become roadblocks, and present actionable options when gaps exist.
At m360 Research, we believe transparency builds trust. When you understand not just how many completes are possible, but why that number makes sense, you are better equipped to guide your clients and deliver successful projects.

Contact us at info@m360research.com to learn how we can help you navigate feasibility with clarity.

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