Transforming market research operations through human and machine synergy

The blend of Human Intelligence (HI) and Artificial Intelligence (AI) drives an unprecedented time for innovation and transformative insights. With PwC predicting that AI will add $15.7 trillion to the global economy by 2030, we’re steering towards a new, reworked future.

At Fairgen, we believe in the power of Human Intelligence and envision a future where AI augments HI, and does not replace it. Our select team of data scientists, mathematicians, and engineers are constantly building and optimizing reliable machine-learning models that can elevate the quality of real-world survey data. Our mission is clear: to unlock the full potential of human data with AI, delivering deeper insights and driving smarter decisions. We are proud to take part in fueling a more efficient research world still rooted in human intellect.

The rising demand for AI integration

Like many other industries, market research is experiencing significant pressure to integrate AI into its processes. Kantar’s Global Trends Report summarizes that 57% of professionals in the marketing field “now consider AI-driven insights essential for understanding consumer behavior, with 62% expecting to increase their investment in artificial intelligence technologies to stay competitive.” Racing to keep up with the fast-evolving consumer trends, preferences, and changing market dynamics, researchers are caught misaligned between what is demanded and the tangible reality. The urgency to stay current with brands clashes with the time, cost, and level of granular insights required. This creates the risk that by focusing solely on AI for operational efficiency, we may lose sight of our true goal– preserving authentic human impact.

Adopting AI responsibly

The increasing demand for low-cost market analyses and rapid turnarounds overlooks the complexity of the AI landscape; success hinges on the quality of the data and how effectively it is applied. If we miss the mark on utilizing AI responsibly, we risk making misguided business decisions based on insights that do not accurately reflect the populations we serve. So, to truly thrive, we must prioritize sustainable practices that balance AI as a catalyst for innovation, allowing us to provide insights that help retain our most valuable resource– humans. With our industry realizing AI is here and ready to be used, it must be reaffirmed that opportunity lies not in replacing humans, but in heartening human-machine dynamics for deeper insights and better decision-making.

How does Generative AI integrate into market research?

Market research aims to understand consumers, spot market trends, improve strategies, and assess competition. Brands seek not only to stay afloat, but lead in their industries by collecting insights to align products or services with identified needs better. In today’s increasingly diverse world, tracking and predicting consumer behavior has become challenging with traditional data collection methods, creating a competitive struggle for brands and researchers alike.

Exploring Generative AI’s role in market insights reveals diverse ways it has been harnessed to create opportunities in both qualitative and quantitative research efforts. Let’s explore some of the promising applications of synthetic data.

Leading AI applications to enhance real-world quantitative surveys

• Sample augmentation: Augmentation allows researchers to generate new data points from existing real data, enhancing the size, variability, and quality of a dataset while keeping a minimal risk, as the synthetic data generated is trained exclusively on each study.
  • Synthetic boosts: Synthetic respondents are generated to boost under-represented groups, allowing brands to read rare audiences at an unprecedented scale. At Fairgen, for example, our augmented synthetic respondents have been validated to be statistically equivalent to three times the amount of real respondents.
  • Data imputation: Refers to using AI to generate more data for existing respondents, usually allowing for shorter questionnaires and improving the analysis of incomplete survey responses. Broken down by Ipsos's advanced analytics team, the standard goal of data imputation is to enhance sources by making sure each respondent “has a complete set of data for each variable of interest, all whilst preserving the original structure of the data.”
  • Fusion: Fusion in market research refers to combining different datasets to create a more complete and insightful view. It works by merging information from separate sources, even if they don’t fully overlap. For example, if one dataset has information about X and Y, and another has X and Z, fusion helps reveal the hidden relationship between Y and Z while keeping the original connections between X, Y, and Z intact. Unlike imputation, which fills in missing data within a single dataset, fusion brings together multiple datasets to discover new insights.

Leading AI applications to enhance qualitative studies and research operations

• Research agents: These AI-driven systems conduct research by asking questions and tailoring interactions based on user responses. Operating at scale, in multiple languages, and 24/7, these agents adapt to interviewees’ answers, providing insights without human oversight.
• Virtual audiences: Utilizing large language models (LLMS), virtual audiences are simulated persona groups that mimic human responses. Trained on real data, this synthetic pool of “individuals” builds vast knowledge of trends, behaviors, and preferences, helping insights professionals with quick ideation and brainstorming.
• Digital twins: Digital twins simulate the behavior and performance of real-world objects or processes, allowing researchers to test various conditions and outcomes. When real-world testing is impractical or too expensive, digital twins provide optimal simulations grounded in real-world data, offering valuable insights without the need for physical experimentation

What potential risks are associated with using synthetic data in market research?

Using synthetic data in market research carries several risks:

• Inaccuracy: Synthetic data may not fully capture what real-world data would reveal, leading to flawed insights.
• Bias: Synthetic data can amplify any biases present in the original data or the algorithms used to create it, contaminating results.
• Lacking diversity: Synthetic data often lacks the richness and variation of real human data, resulting in mediocrity—missing out on the broader range of views and behaviors.
• Inadequacy: Synthetic data, when validated for one purpose, fails to perform effectively for other use cases or regions, potentially leading to incorrect or misleading conclusions.

Understanding that synthetic personas may not fully represent the diversity of the human population, we can refine our use of generative AI to implement more authentic applications and bridge the gap between the real and synthetic worlds.

How should we embrace the synthetic data revolution?

Revolutionizing the market research landscape, these innovative tools are not only streamlining processes and providing more precise recommendations, but promising a new era for data collection and analysis. Synthetic respondents are transforming the field by uncovering hidden insights accurately and flexibly that informs business decisions has become more effective than ever before. Yet, the hype around synthetic data shouldn’t come without its precautions. Service providers and brand researchers must emphasize a focus on the integrity of data and associated processes.

Jay Calavas, VP of Vertical Products at Tealium, emphasizes that “Real-time data collection is a strategy, not an afterthought.” The potential of AI to deliver time and cost efficiencies can only be fully realized when ethical and transparent practices guide its use in data collection. In support, Chief AI Scientist at Kantar, Ashok Kalidas, validates that it is “vital to start with a high-quality data source that is very specific to the problem at hand, and use that to train a synthetic data-generating algorithm.” As well, ensuring audit-ability and consent throughout the data pipeline is necessary– critical standards that should serve as the foundation of the industry. Companies must prioritize platforms built on high-quality proprietary data supported by skilled data science teams with deep expertise in analytical frameworks. Transparency in validation processes and a commitment to reliability are key to finding credible partners in synthetic data. As AI continues to transform market research, it symbolizes the convergence of human creativity and machine intelligence, calling for a future where innovation and responsibility go hand in hand.

Synthetic data is an opportunity

The future of market research lies in the synergy between AI and human expertise. At Fairgen, we believe that AI should complement human insight, not replace it. By blending the unparalleled speed and scalability of AI with the creative intuitions of humans, we can deliver more valuable, data-informed results. This collaboration ensures that innovation remains responsible while capitalizing on the strengths of both humans and machines for a greater impact.