Most surveys ask for marital status, but does it actually predict purchasing behavior? The answer is no, and the ESOMAR Demographic Committee, working on behalf of the Global Data Quality Initiative (GDQ), has spent two years proving it. The real problem goes deeper: we’re asking unnecessary demographic questions in surveys, and thanks to programmatic sampling, we’re asking them over and over again.
Take ten surveys today and you’ll answer ten different versions of the same irrelevant questions. It’s a redundancy loop that’s undermining data quality and driving away thoughtful respondents.
“This didn’t used to be a problem before the advent of programmatic,” explains Frank Kelly, Market Research Practice Lead at Virtual Incentives, and member of the committee. “Now the whole process is just basically a lot of samples being sent blind to surveys. Instead of targeting, it’s a scatter shot.”
Led by Judith Passingham (recently retired Chair of ESOMAR’s Professional Standards Committee) and Ipsos Chief Methodologist Jon Puleston, the committee has completed the heavy lifting.
The ESOMAR committee has standardized how to ask demographic questions across major countries. They’re identifying which questions actually matter versus which we ask from habit. The recommendations are ready. What’s left? Industry adoption.
Standardizing Demographic Questions in Surveys
The ESOMAR committee set out to solve what seems like a simple problem: how to ask demographic questions consistently across the world. But standardizing “simple” concepts like education, employment, or income quickly revealed how culturally specific those ideas are.
For example, Germans don’t describe higher education in terms of “universities” the way Americans do. Romanians report net income after taxes, while most Americans think in gross annual figures. Even within Europe, social class systems like the UK’s “C2” or “B2” categories don’t translate easily elsewhere. The first wave of research painstakingly mapped these cultural differences and created global standards that respect local context while enabling cross-country comparison.
Now, the second wave, currently being analyzed with findings expected by year-end, tackles a far more provocative question: which demographic questions actually matter? The committee is challenging decades of research convention, asking whether we’re collecting information that truly predicts behavior or just repeating questions out of habit.
Early insights are already challenging long-held assumptions. Marital status, a fixture in nearly every survey, shows virtually no correlation with consumer behavior. Regional data, critical in a geographically diverse country like the U.S., proves largely irrelevant in smaller markets. Meanwhile, life stage categories, like SINKs (single income, no kids), DINKs (dual income, no kids), and empty nesters, appear far more predictive.
The Globalization of Research Demands Consistency
This work addresses a growing challenge. While its original focus was on improving the quality of demographic questions overall, the rise of global research has made that mission even more critical.
Thanks to programmatic sampling and accessible tools, even small research teams can now field studies across multiple countries, a capability once reserved for large multinational firms. But that accessibility comes with risk: when every team defines basic demographics differently, global data becomes impossible to compare.
A standardized framework helps close that gap. By mapping how key concepts like income, education, and social class differ across countries, it gives researchers a shared foundation for global consistency, without erasing local nuance.
For smaller companies now running international surveys, that foundation is game-changing. It turns cross-border research from a patchwork of conflicting definitions into a system built on clarity, accuracy, and trust.
The Path To Better Survey Data Quality
The first wave of research is complete, and the second wave nearing release. The next step is clear: bringing these standards to life.
The work has shown that standardizing demographic questions in surveys is possible, and powerful. Now the opportunity lies in using those standards to rebuild how surveys are structured and how respondents are treated.
Programmatic sampling systems already have the technical capability to pass standardized demographic data between platforms. If those systems adopt these recommendations, respondents could skip redundant screening questions and move directly to relevant qualifiers, questions that actually determine eligibility.
The potential benefits ripple throughout the ecosystem:
- For respondents: Less wasted time, more compensated participation, and fewer repetitive questions
- For researchers: Cleaner data, faster analysis, and stronger predictive power
- For panel companies: Better pre-qualification, improved targeting, and higher satisfaction among panelists
It’s a shift from treating surveys as traffic to treating respondents as participants. Adopting these standards won’t just make research more efficient, it will restore the element of trust that’s been lost in the rush toward automation.
The Impact of Better Demographic Questions in Surveys
Unnecessary demographic questions in surveys waste respondent time, degrade data quality, and drive participants away. Repeated, meaningless questions erode trust in the research process itself.
The ESOMAR Demographic Committee offers a solution: a standardized framework that prioritizes questions with real predictive value, factors like life stage and household composition, instead of outdated boxes like marital status.
Adopting these practices will improve survey data quality, reduce respondent fatigue, and make research more credible and actionable. The next wave of research, with specific recommendations on which questions to eliminate, arrives by year-end 2025.
Two years of research have identified which demographic questions actually matter. Visit ESOMAR to access the standardized formats and join the industry shift toward more efficient, respectful research.