• Representation – Consumers are losing trust and patience in brands. Typical consumer survey response rates are chronically low, at between 5% and 30%. Audiences are increasingly reluctant to give up their time and evermore suspicious about surrendering their personal data: 80% of consumers in a recent Norton study say they are concerned about their privacy, with 40% worrying about how businesses use their data. Self-selection bias favors those with strong opinions or more free time who are far more likely to respond. Smaller sample sizes heighten the potential for bias, further under-representing diverse and niche audiences, who are already scarce in research studies. High survey frequency drives down participation rates and damages the quality of responses. Bored audiences rush answers, declining to provide in-depth qualitative feedback, and their inattention to detail fuels incomplete and incorrect responses.
• Lack of Depth – Without the ability to ask follow-up questions or hone in on detail to clarify answers, poorly designed surveys are only a one-way conversation without nuance or context. Standardized questionnaires can oversimplify or ignore complex consumer emotions, attitudes and behaviors, exacerbating underrepresentation and bias challenges.
• Consumer Attitude-Behavior Gap – Surveys capture attitudes and opinions, rather than real-life purchasing behaviors. An individual’s self-reported values can conflict with or even directly oppose their actual buying decisions. Recent reports showed that 78% of shoppers believe sustainability is an important factor in buying choice, with 55% claiming that they are willing to spend more on eco-friendly brands. However, sustainable products only represent one fifth of the consumer-packaged goods market as a whole. Buyers aren’t necessarily liars, but they are often unaware of their own behavior patterns, and how closely their beliefs and intentions match their decisions. The attitude/behavior gap is even wider where consumers face a conflict between their values; often cost, availability, or speed of delivery represent higher stakes than personal ethics. Purely assessing attitudes and opinions may not accurately reflect how consumers actually interact with a product and may miss business-critical insights into key purchase drivers.
• Cost & Time – Consumer and Market Insights teams are spending between a quarter and nearly half of their working time fixing data quality issues. 59% of CMOs report that they lack sufficient budget to execute their strategy in 2025, and Gartner expects that marketing budgets will remain flat as a percentage of company revenue this year. Lower available marketing spend is forcing enterprises to do more with less, further pressurizing under-resourced CMI teams for tangible results.

Poorly executed research studies are delivering incomplete, low quality data that informs inaccurate insights. However, the survey is far from dead: the best consumer insights will always come directly from consumers themselves. Research studies that are effectively designed, delivered and managed provide the vital first-party data that organizations need to understand consumers and adapt campaigns for business impact.
Consumer-centric innovation does still require survey data – but through the right approach.

Better Survey Design – Writing Effective Questions

JT reveals, “Most consumer research quality issues arise from the design and experience of a survey. Interactive survey methods, such as gamification, ensure relevance to your ideal type of respondent, and create more realistic conversational experiences. An interesting and enjoyable survey experience delivers more in-depth, relevant survey data that can power consumer-centric innovation.”

Sustaining Engagement – Communicating with Research Participants

Wilson advises: “When first speaking with potential participants, communicate your needs up front, including the estimated completion time so your participants know what they’re committing to. Assign turning points early in the study to avoid wasting participant time.”

Insights leaders are communicating effectively with research participants, and encouraging more detailed complete responses, through:

• Building Trust – Specifying how respondents’ data will be used and protected at the very beginning of the survey assuages any privacy fears. Introducing your organization and transparently outlining the purpose of the survey further establishes the study’s credibility and reinforces the value of the research.
• Valuing Respondents – Pruning questions to avoid repetition both increases the quality of unique, detailed answers and demonstrates that researchers respect the time and effort of respondents. Clear instructions help participants complete the survey without getting frustrated, reducing probability of disengagement and abandonment. Including a progress bar keeps respondents engaged, provided that questions are of similar length and complexity, to avoid misleading audiences of the time and effort involved.
• Consumer Focus – Wilson continues, “Try to think about all users when designing your study. Doing so will not just improve the respondent experience, but also help your performance metrics for the respective study.”

Combining Human & AI Expertise – Harnessing Synthetic Data

Synthetic data and audience data have a 90–95% correlation rate. Whereas proxy audiences don’t get bored with surveys, errors and inconsistencies from human respondents are commonplace thanks to distraction, disengagement and signaling bias. Synthetic data allows researchers to test endless combinations of questions with proxy audiences automatically, at far lower cost and at much greater scalability than real live human studies.

JT explores how businesses are incorporating synthetic data to scale their existing research methodologies: “We’re quickly moving from traditional research processes to always-on data to synthetic predictive models built on survey data. The future of innovative research involves an experimental approach that fuses natural and synthetic. OpenAI and more publicly or easily accessible models are providing capabilities off the shelf. Businesses are incorporating synthetic data at a macro level and layering it on top of their focus group methodologies, in-depth interviews and quantitative research surveys. Insights leaders are augmenting their existing data sets with AI, refining their knowledge lakes for a broader consumer understanding.”

Gartner estimated that by the end of 2024, 60% of all data used to train AI would be synthetic, rising from just 1% in 2021. JT continues, “The quarterly wave of brand tracking and the pre- and post-campaign testing aren’t going to cut it in a world that needs a continuous and consistent training feed. Businesses are evolving from purely their own self-delivery, bringing tracking studies and other continuous data sets together to train AI models to generate the insights they’ve always wanted.

“Natural data from real people provides the quality training data for synthetic models, although training data from a research pool can become out of date very quickly. We deliver daily tracking feeds continuously, with up-to-date data to guarantee recency. I see the fusion of AI and traditional research models as an accelerant to solving many of our industry’s persistent problems. AI isn’t a replacement for quality surveys; consumer-centric innovation will come from the union of CMI teams and artificial intelligence enhancing the quality and accuracy of research.”

Aligning surveys directly to insights team KPIs and business-critical objectives guarantees the responses required for consumer-centric innovation that drives organizational impact. Starting survey design from scratch helps align every question with the answers insights teams and stakeholders really need, to prioritize the most important questions that deliver the most valuable insights. Beginning with your hypothesis and taking a step back to honestly review whether every question is both relevant and necessary to it, will improve the respondent experience and keep the survey on track with the research purpose.

Choice is at the heart of consumer centricity. Neutral, objective questions guard against leading respondents towards answers that don’t match their true beliefs, empowering participants to control the narrative of their responses and truly reflect their views. Open-ended questions have an important place in allowing participants to elaborate on answers and provide context for richer qualitative data. Offering a range of answer selections, including options for uncertainty and inapplicable information, avoids forced responses and ensures consumers can accurately represent their personal attitudes and opinions.
Whilst errors and bad actors can never be completely eradicated from human research, only real-life survey participants can provide the real-life consumer view needed for consumer-centric innovation.

JT reinforces the importance of raw consumer opinions in a wide world of available data sources: “Ultimately, research tools are designed to answer your hypotheses. Artificial Intelligence is programmed to deliver answers no matter what. AI doesn’t care whether these answers are useful or even correct – and often, they’re neither.

Even if consumers don’t have anything to say, well-designed research with real live human participants gives more diverse and realistic results than any technology. Accurate, relevant research is better with real people.”

JT & Wilson will be sharing proven strategies for consumer-centric research at the Quirks Event for Marketing Research & Insights Professionals in New York this July.

Meet us at Quirks to hear the accurate, actionable insights that are powering consumer-centric innovation for real business impact.

Delineate’s always-on tracking helps global CPG brands access live, accurate consumer data to power faster insights and more effective campaigns. Our approach delivers:

• Representation – Specific quota control covers all your target segments, and daily methodology allows for sensitivity to fast-changing market and political dynamics
• Depth – Tailored qualifying questions ensure accuracy and detail, providing granular data with AI-enhanced analysis and organization
• Optimization – A fair price for a fair interview guarantees high quality exact audience matches, making for an engaged participant pool who provide the most valuable insights
• Maximum Value – Data science-ready survey questions deliver the answers your business needs to the most urgent questions, for the greatest impact.

Uncover game-changing insights into your consumers’ preferences and behaviors.