How Gen AI Is Transforming Market Research

Recently the business world has started paying attention to the impact generative AI could have on marketing activities. The most exciting of these is market research, the processes by which firms gather data and generate insights about customers and competitors. In their research, the authors have identified four distinct classes of opportunities. 

1. Supporting Current Practices.  Firms often are frustrated by the relatively high cost and long timelines of collecting customer and market insights. So how might gen AI address both?  Gen AI’s ability to synthesize information, for example, could be leveraged to summarize literature and previous research in the first stage, to extract findings from interviews and new data in the second stage, and articulate takeaways in the third stage. And gen AI could do all those activities far more rapidly than humans could.  In the survey from more than 170 market research practitioners and users it emerged that 45% of them were already employing gen AI in their current data and insights activities; another 45% told us they were planning to do so in the future.  More generally, it will increase the quality, accuracy, and customization of their work.
2. Replacing Current Practices.  One of the most innovative applications of gen AI in marketing is producing and analyzing what’s known as “synthetic data”—artificially generated data that mimics real people’s behaviors and preferences. Firms can do this with any of the widely available gen AI programs, but they can also develop and train their own specialized models using the aggregate data that they’ve already collected from traditional research, syndicated data, CRM systems, and transactional information. The synthetic data can then be used to simulate various customer or competitor responses, highlighting potential pain points and the benefits consumers seek at different stages of their interactions with a product or service. A full 81% of the respondents in the survey told they already use or plan to use gen AI to create synthetic data.  Again, there are positives and negatives to think about: Small gen AI models are mostly limited to structured or semistructured data (data that’s numerical or categorical) and don’t benefit from the public models’ broad training sets, whereas public models can also work with less-structured qualitative data.
3. Filling Existing Gaps.  Even in organizations that profess to be data-driven, practitioners often report that most decisions are made without a formal empirical analysis. There’s simply not enough time or money to do one. But gen AI promises to be an always-on intelligent engine for customer and market insights—one that can offer market researchers instant access to empirical evidence when data isn’t available or is too costly to acquire. Gen AI can be used to test assumptions, pilot concepts and execution strategies, and provide a sounding board for managerial decisions. Firms can even develop “labs” that make customized AI models available to employees in a safe and convenient way to support decision-making throughout the organization.  In the survey 30% of respondents said that their company had used gen AI to guide decision-making that previously wouldn’t have leveraged external data and insights. Overall, 81% of respondents reported using or planning to use gen AI to “listen to the market” and keep their organizations informed about the competitive environment.
4. Creating New Kinds of Data and Insights.  A mantra in content marketing and sales is that you get only one chance to make a first impression. But maybe that’s no longer true.  We say that because content marketers and salespeople are starting to use gen AI to create “digital twins”—virtual replicas of individual customers that are constructed using publicly available information or proprietary data—to test and refine their materials and pitches before presenting them to real people. This approach allows for the meticulous calibration of marketing efforts because digital twins, unlike actual people, never get tired, irritated, or bored when interacting with marketers and their questions.  More than 40% of our respondents said that they’re already experimenting with digital twins.  Another 42% said that they planned to experiment with digital twins in the future.

Gen AI offers marketers a lot, but it still has plenty of limitations, which are important to acknowledge. As we noted earlier, one of the main concerns that our survey highlighted was the potential for biased results, which was cited by 77% of respondents.  Further, given that gen AI models are trained on existing data and insights, it’s not yet clear how good they’ll be at predicting dramatic changes in consumer behavior or anticipating discontinuous product innovations. Gen AI models are also known to be sensitive to prompt architecture.  Concerns have also been raised about gen AI’s ability to simulate responses from a representative sample of the population.

The authors conclude on an optimistic note, by bringing up an idea that Cannon of Outset.ai proposed to them. If gen AI can talk to thousands of people around the world in hundreds of languages every hour, and it can instantly draw all sorts of unique, high-fidelity insights from the data those conversations generate, then our understanding of one another should deepen—and the products, services, and experiences we build will be infused with more humanity, not less.