Market research has continuously evolved in response to technological change. From door-to-door surveys to telephone interviewing, and now to online and mobile solutions, each shift has brought both challenges and opportunities. Researchers have met this change with agility, creativity, and the ingenuity necessary to expand the reach and effectiveness of the field.
And now Generative AI (GenAI) is emerging as a transformative force; across all industries, but particularly in market research. As the methods for uncovering consumer insights and making data-driven decisions continue to evolve, it is a necessity for organizations to partner with research agencies who understand how to properly leverage and integrate GenAI to enhance their insights, not replace them with a faster, cheaper stream of data points.
The Dawn of a New Era: Transformative Applications Driving Real Results
Generative AI is reshaping market research by delivering faster, deeper insights. With projections estimating up to $4.4 trillion in annual economic value, organizations using GenAI are already seeing up to 40% faster results and 35% richer consumer understanding. Traditional methods are struggling to keep up with the scale and speed of modern data, but GenAI tools—especially advanced Natural Language Processing (NLP)—are stepping in to fill the gap. These tools can rapidly process vast amounts of qualitative data, detecting emotional tones and nuanced patterns in minutes, enabling insights that once took weeks to uncover.
The Human + AI Partnership: A Winning Combination
GenAI is empowering human researchers by automating time-consuming tasks like data cleaning, basic analysis, and report generation; freeing researchers to focus on strategic thinking and insight interpretation. This partnership between human expertise and AI capabilities is creating a new paradigm in market research, where machine learning enhances rather than replaces human judgment.
As such, Ironwood champions the “Human in the Loop” (HITL) philosophy and has developed protocols for incorporating human oversight of AI tools at key stages of the research process, i.e.: design/strategy setting, data collection and data validation, data curation, model training, and interpretation of strategic insights.
Best Practices for Integrating GenAI into your Insights Toolbox
Be it secondary, primary qualitative/quantitative research or social media, we believe that the power of GenAI is best harnessed and optimized when the HITL approach is applied. In short, we deploy AI to help scale, speed up, and deepen our analyses. Human researchers enter the process to periodically sense check AI and to bring the holistic context, critical thinking and strategic insight necessary to enable actionable and reliable business decisions.
Following are a few practical applications of this blended process that we’ve used to dramatically improve informational depth, cost efficiency and speed to insights.
With its ability to scour the breadth of the internet in minutes (if not seconds!), secondary research is a primary use of GenAI. We often use it to establish baseline market analyses, identify/monitor social and technology trends and assess regulatory and economic policy impact.
However, AI can return findings that contain recency gaps—i.e., understated past year market changes—and “hallucinations”—i.e., overgeneralized findings that lack factual grounding. To mitigate these shortcomings, our practice is to cross-check AI-generated insights with traditional human-led secondary research, such as historical data, literature reviews, database searches and published data, etc.
Using GenAI to interview “live” respondents is, admittedly, still one of the more slippery slopes left in the evolving human-AI insights partnership. While AI cannot replace the deep, nuanced understanding that comes from a real human connection, it can provide early learning and validation of human interviewers and online interviews, allowing for scalable yet conversational data collection.
In pure qualitative settings, GenAI tools can mimic some moderator interactions to shape the early-stage interviewing process,as well as asynchronous online qualitative discussions; again as a supplement (not a replacement) for a human moderator. And in surveys, virtual chatbots are increasingly deployed to facilitate probing of open-ended questions; both to enrich the level of feedback provided by respondents and detect bots and other fraudsters.
Our “secret sauce” in the SR space is grounded in the responsible and realistic construction of consumer personas or SEGMENTS, as well as the unique ability to ensure that their traits and behaviors are representative of the CURRENT CATEGORY (not data that can be years old). This ensures the validity and accuracy of our synthetic data, and in turn, the integrity of our research outcomes.
In conjunction with our strategic partner, PersonaPanels, our SR’s are modeled and validated to ensure that they accurately emulate the category-specific traits and behaviors of customized or generational consumer segments (i.e., Gen Z, Older Boomers) or other respondent personas. The validated SR’s are updated with current, category-specific news, trends and current events by seeking out internet sites and publications that are consumed in “real life” by these segments. Then, using category-focused and current characteristics, these “super SR’s” can be engaged for queries ranging from market/trend assessments to new product and communications development and optimization.
HITL is infused into this process, most importantly, through the grounding of the SR’s in the current mindset and attitudes of their “real” human segment counterparts. However, we also take the additional steps of weaving in testing with human subjects pre, peri and post the SR evaluations.
This process creates a far more time- and cost-effective research cycle than one that is rooted solely in traditional, human research.
In their most basic form AI data analysis tools are used for text analysis and to summarize key themes and sentiments from large bodies of open-ended data resulting from qualitative or large-scale quantitative research and social media.
We use GenAI for both basic text sentiment analysis and advanced detection of emotional cues in video and audio, like facial expressions, body language and vocal tone. AI scales and speeds up the analysis, leaving human researchers to focus on interpreting results and informing strategy.
As a leader in the industry-wide movement to improve online data quality, Ironwood has developed a rigorous, multi-layered process to detect and mitigate online survey fraud.
Our first line fraud defense employs seamless, AI-powered technology and complex scoring algorithms to detect and prevent mal activity at the screening stage and in real-time, throughout the survey, i.e.: duplication detection, Captcha and our proprietary “Bot Sentry” visual traps narrative open-end traps, attention testing, red herring questions, grid accuracy and pre-set thresholds to flag speeders, straight liners and cheaters.
However, given the constant and rapid evolution of survey fraud, we find that human logic and analytical power still play a vital role in bolstering fraud prevention and mitigation. Our human-led protocols begin with analysis of the in-survey flags kicked out by our automated processes and extend to an integrated review and cleaning of data sets for multiple indicators of fraud, such as: response consistency, patterning of responses and analysis of open-ended for sensibility and accuracy.
Explore the AI Revolution with Ironwood
As GenAI technology continues to evolve, Ironwood Insights Group remains committed to staying ahead of the curve. Our continuous investment in emerging technologies and researcher training ensures that our clients always have access to the most advanced research capabilities available.