Quotas help you to collect representative, accurate data from your consumer research. They also allow you to keep track of how many respondents meet a condition outlined in your survey.
Let’s take a look at the types of demographic quotas available on the aytm platform, learn how to use them in your next survey, and explore a few use cases for quotas.
Types of Demographic Quotas
At aytm, we offer two ways to implement demographic quotas in your survey. The first is by using Quota Balancing (or US Census balancing). This allows you to balance your survey by three US demographic traits individually – gender, age, and location – in non-nested quota groups (for other custom options, please reach out to your account manager or chat with us). This feature gives DIY users more autonomy in the platform, removing the need to contact us for assistance setting up your quotas.
We also offer Nested Quota Groups. This feature allows you to create custom demographic quota groups. It even includes a list of US Census presets to quickly add quota groups based on US Census breakouts.
Nested Quota Groups are not limited to the United States. You can create custom quota groups for any of the countries offered on the Target Market page. And you can also change the percentages of your nested quota groups with a click of your mouse.
Keep in mind that cleaning your data AFTER your survey has finished fielding is highly recommended to best balance your quotas.
And for best results using quota balancing, we also recommend a minimum of 400 to 1000 respondents with no screening criteria.
When Not to Use Quotas
As great as quotas are, there are specific times when they may not be right for your study. You wouldn’t want to balance by Census if your target market is not representative of the US population, for example, if you’re targeting gamers – an audience that skews male – Census balancing could skew your sample in an undesirable way.
Sometimes it’s not about whether or not you should add any quotas, but being careful not to add too many quotas. Too many granular nested quotas could greatly extend the time required to field, or worse, skew your sample.
Using Demographic Quotas in Representative Research
Quotas let you determine how representative of your audience your survey respondents will be. You know just how critical this information is if you’ve ever found yourself meticulously picking through the results of a survey, only to realize that 75% of your respondents are female, while they only make up around 51% of the population. A discrepancy like this can invalidate any insights you may glean from the data, resulting in lost time and money.
You wouldn’t want to make strategic business recommendations based on the views of a minority if this group isn’t representative of the entire population you require buy-in from. Using quotas helps you get one step closer to unbiased and actionable insights.
Using Demographic Quotas in Tracking Studies
Another good reason to establish quotas is for the sake of consistency when conducting a tracking study. Let’s take a look at aytm’s COVID-19 tracker to illustrate this point:
We surveyed 1,000 US adults and weighted to be reflective of the US population, with the goal of understanding the changing US consumer landscape during the Covid-19 global pandemic. This is an ongoing survey that has completed more than a dozen waves to date.
Now, imagine how the data might look if we paid no attention to the consistency of the sample. If in wave 1, we used census balancing by age and in wave 2, we didn’t, we may have seen wildly different numbers from week to week.
Our research shows that concern for the pandemic is very different among older and younger adults. The data would lack consistency if one week had 50% of respondents under the age of 30, and another week was made up of 60% of respondents over the age of 60. When it comes to this particular pandemic, age is a huge factor in someone’s level of concern about the virus.
This same idea applies in any research project, whether you’re doing a product sentiment tracker and your target audience is 75% female or you’re tracking changing NPS scores for a local brand with 55% of your customer base located in Fulton County, Georgia. Use quotas to ensure your sample remains constant.
Using Demographic Quotas in Comparison Research
Whenever your audience is narrow, only encompassing one gender, or age bracket, for example, it’s not necessary to conduct representative research. But you may still be interested in uncovering insights about other genders or age brackets.
You can use demographic quotas to guide the sample towards the audience you’re most interested in, while also looking at alternative audiences whose data could be used for comparison purposes.
For example, if you were selling beard oil, you might want 80% male and 20% female respondents, understanding that the primary decision-maker is highly likely to be male, while also obtaining insight into how women could play into the purchase decision.
For many consumer research studies, one of the goals is to provide data that mirrors the population(s) of interest. That’s where demographic quotas come in. Aytm makes it incredibly easy to program quotas into your survey, offering reliable results every time.
Read more about adding demographic quotas to your surveys.