How extensive is acquiescence bias in brand research, and how can it be mitigated?
Steph Clapham
Survey research can be subject to many types of biases. One of the most interesting in brand research is acquiescence bias. The impact of acquiescence bias on data quality can be quite profound. It is not uncommon for survey results to show that 5% of respondents claim to have purchased a product, whereas actual sales figures indicate the true share should be closer to 0.1%. This kind of overclaiming is especially problematic for questions that branch from others, as is often the case with funnel KPIs such as awareness in brand surveys.
This impact is not necessarily consistent across brands either ; it can be greatly exaggerated for lesser-known brands than those with very high awareness. Small brands are most impacted by acquiescence bias, given that even small errors in reported awareness can translate into quite drastic percentage leaps and even bigger representations of people in real population terms.
To better understand the impact of acquiescence bias on our surveys, we designed a test to capture overclaim across various countries. In this article, we’ll explore the findings and their impact further.
Acquiesence bias is the tendency of some respondents to lean towards providing a positive response to questions, and to thus, for example, falsely say that they know a brand or would consider buying it (or have already done so) because they feel that it’s socially desirable to do so.
We tested an effective way to capture over-claim behaviour using a fake brand question within our usual brand surveys. This mimics the exact design and placement of a brand awareness question and so cannot easily be detected as a test question. Respondents who claim to know the fake brand we present to them are considered to show over-claim behaviour, a clear indication of acquiescence bias.
The design of the logo used to depict the fake brand is an important element in the effectiveness of this capture. Ideally, we needed to create a logo and name that is coherent with the rest of the brands in the survey, but not so subtle as to easily be mistaken as a real brand. The sweet spot is creating a fake brand that is unique and could be real but isn’t! We developed dozens of these fake brand questions that we randomly attributed to respondents answering our surveys — this approach ensured that we minimise the effect of any one fake brand being more or less effective than another, or from any potential bias from one fake brand accidentally resembling a real brand.
To be even more sure we don’t capture only accidental awareness of these fake brands, we allowed respondents the option to provide a non-answer to the fake brand question. Including a “not sure” answer option enabled respondents who are uncertain to provide an honest answer to the question, as opposed to forcing them into providing a binary response. For the purpose of this test, we are only evaluating those respondents who have answered “yes” to knowing our fake brand, as this is the most conclusive evidence of bias.
Over two months (December 2024 to January 2025), we asked more than 860,000 respondents across different countries if they were aware of the fake brand randomly assigned to their survey.
Our testing spanned 20 markets and revealed significant variance in acquiescence bias rates:
Beyond country differences, we observed demographic and socioeconomic variations:
Gender: Males overclaim more than females by about 65% across all 20 countries, with the greatest gender gap in China, Israel, and Portugal. Some countries, like Japan and the Netherlands, show no gender difference, while Norway and Sweden have higher bias rates for women.
Income: Generally, higher income correlates with higher rates of overclaiming, notably in China, India, Germany, Belgium, and Poland. Norway and Israel show the opposite trend, with lower-income respondents more prone to overclaim.
Education: No consistent pattern emerged globally. In India, China, and Portugal, lower education levels tend to correlate with higher bias. In contrast, Germany and Switzerland show more bias among respondents with higher education, while Great Britain, the Netherlands, Belgium, and Brazil show no clear difference.
Given the wide differences in bias rates across countries, we examined its effect on brand awareness data for sports brands in India—a country with high bias levels.
Translated into real population numbers, these differences are staggering. In India, even a few percentage points translate into tens of millions of people who falsely claim to know a brand. For Puma, the corrected data represents 9.4 million fewer people aware of the brand. For Saucony, the difference could be as much as 100 million people falsely claiming familiarity.
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Given the wide differences in bias rates across countries, we examined its effect on brand awareness data for sports brands in India—a country with high bias levels.
Translated into real population numbers, these differences are staggering. In India, even a few percentage points translate into tens of millions of people who falsely claim to know a brand. For Puma, the corrected data represents 9.4 million fewer people aware of the brand. For Saucony, the difference could be as much as 100 million people falsely claiming familiarity.
The impact on the data when translated to real population numbers is staggering, especially for a market like India, where a few percentage points of difference translates into tens of millions of people who are (or aren’t) aware of a brand. Even for the minimally-impacted brands like Puma, the drop in awareness level once the acquiescence bias is removed represents 9.4 million people. For brands like Saucony, the effect could be as big as 100 million people falsely claiming to know it.
The impact on the data when translated to real population numbers is staggering, especially for a market like India, where a few percentage points of difference translates into tens of millions of people who are (or aren’t) aware of a brand. Even for the minimally impacted brands like Puma, the drop in awareness level once the acquiescence bias is removed represents 9.4 million people. For brands like Saucony, the effect could be as big as 100 million people falsely claiming to know it.
Beginning with more accurate awareness (thanks to the removal of acquiesence bias) will ensure that any following brand metrics are measured on the right people who are capable of answering questions about the brand. This eliminates the risk of sample-based skews that offset the calculations of funnel KPIs, like brand consideration or perception, or strange answer behaviour by those who don’t know the brand enough to answer follow-up questions about it.
