Kantar's Profiles Blog

Sampling & Sourcing Pitfalls: Quota Problems?

Posted by Austen Lear on Aug 27, 2018

We’ve all been there. You spend endless hours perfecting a survey and imagining your ideal audience fallout. Might as well set some quotas to ensure that desired outcome, right? But now, as you take a step back, those quotas are starting to look a bit unrealistic and a little overwhelming. Take a deep breath, because I’m here to help provide solutions for some common pitfalls when it comes to setting quotas.

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Do you currently have quotas set for age, gender, income, ethnicity, education, region, kids in the household and your client segments? This amount of quotas (or even just four or five of these) can cause a number of issues: feasibility impact, sampling overload, over quotas, the list goes on. Not only can they be challenging to manage, the side effects from these issues cause harm to the panel and panelist engagement.  

To simplify your quotas without sacrificing data, try combining quota groups. If you initially proposed six quota buckets for age, reduce them to three. This can still provide an accurate subset of the larger audience. The same approach can be applied to income and other demographics as well. 

Why do “too many quotas” cause feasibility problems?

Having too many quotas can cause unfavorable combinations to arise as quotas begin to close (and we all know the easy ones go first). For example, your United States survey could be left needing Hispanic males in the Midwest to close a project. In this scenario, you would be looking to fill an ethnicity quota in a region where that ethnicity doesn’t have a large representation. Or in another project, you could be left with age 65+ early adopters with a master’s degree; a challenging ask.


Have you considered the goal of the research when you designed national representative (nat rep) quotas with a specific audience?  For example, if you’re screening for respondents that go skiing or snowboarding five times or more a year, and there are quotas in place to include national representation in the United States. This could pose a problem regarding incidence rates for quota groups, and therefore impact feasibility. In this scenario, we can assume that older respondents (60+) may not go skiing/snowboarding as frequently as younger respondents, and respondents from Florida most likely won’t be hitting the slopes as frequently as those from Colorado. Instead of forcing unrealistic quotas, try one of these solutions:

  1. Set the quotas to match the Skiing/Snowboarding audience, rather than forcing nat rep where it may not fit.
  2. Use a national representation outgo, but allow age or region completes to fall in naturally. This ensures you expose a nat rep audience, but you’re collecting a representation of your audience. If you feel this method provides unfavorable results to how the population looks, you can always weight the data for the quotas that are underrepresented.

These are just two situations where too many quotas can create inefficiencies for you, your studies and panels. Discuss any quota concerns with your project manager prior to fielding. There may not always be an opportunity to combine quota groups, and sometimes national representative quotas are the way to go, but recommendations from a trusted sampling solutions partner may help you avoid some of these pitfalls.

Topics: Online Sampling, Data Quality

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