When we wrapped up our recent eBook, “The Ultimate Buyer’s Guide: How to Select a Sample Supplier,” with a series of buyer tips, one of our umbrella concepts was to seek dependability in execution. This means that your supplier should be able to manage and launch a project with several key tenants in place, including feasibility of sample.

Our belief is that, in order to reach the data quality goals that we all seek as researchers, we need to start looking beyond the panel book to “real-time” feasibility. Over the past few years, the feasibility process has changed dramatically. Yet, many sample providers still manually produce feasibility calculations in spreadsheets using simplistic formulas. Automated feasibility processes can not only take into account a huge number of factors–from field parameters to individual behaviors–to provide accurate estimates of how a project will fill, but also deliver greater speed, accuracy and dependability.

One approach is to estimate maximum or “spot” feasibility. This approach gives us the most precise and practical number possible as it effectively represents a promise on the part of the supplier to deliver. Most suppliers aren’t willing to share spot feasibility outside of a direct business quote or proposal, as the number can sometimes appear quite small in comparison to the broad sweeps most panel books take when it comes to reach. Moreover, there are important questions about incidence, reliability and transparency that must be answered up front by suppliers, while buyers need to pay attention to their own metrics that affect completion rates, such as survey length, dropout rates and other factors.

If you are a sample buyer, we encourage you to look out for these things. If your vendors are still calculating based on spreadsheets or using methods that don’t take into account incidence and project days, but every other aspect of field and individual respondent profiles, you may be at risk. This is another area where you should require transparency. Ask how it is done. A good objective metric is to have the supplier provide data on how many jobs it failed to deliver on time and in full.

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