As businesses continue to navigate the complexities of product development and innovation, generative AI has the potential to be a powerful new tool for market research. In their recent article for the Harvard Business Review, “Using Gen AI for Early-Stage Market Research,” Ayelet Israeli, co-founder of the Customer Intelligence Lab at the Digital Data Design (D^3) Institute at Harvard, and her co-authors James Brand and Donald Ngwe explain their research on the possibilities and pitfalls of using LLMs to create synthetic customers.
Key Insight: The Power of Synthetic Customers
“Our research shows that LLMs, used carefully, can function as synthetic focus groups, producing early insights on customer preferences in a fraction of the time and cost of human studies.” [1]
By combining LLMs with traditional research methods, companies have the opportunity to simulate consumer sentiments like willingness-to-pay (WTP) to make product innovation faster and cheaper. The authors’ research shows that for simulated tests of categories like toothpaste and tablets, LLM-created synthetic customers produced realistic and accurate preferences for many familiar attributes. What’s more, teams could explore dozens or even hundreds of ideas by using these synthetic consumers as an initial filter, overcoming traditional limitations in scope.
Key Insight: The Competitive Advantage of Proprietary Data
“[F]irms that build and fine-tune their own internal ‘customer simulators’ using LLMs and historical survey data can unlock sharper early-stage insights.” [2]
While usage of LLMs out of the box showed promising results, companies that incorporate their own historical customer data were able to achieve better results. For example, the authors noted that LLMs often rate novelty higher than actual humans, and as a result synthetic customers were initially positive about pancake-flavored toothpaste. Fine-tuning the LLM with data from an actual study helped to correct this enthusiasm and produce WTP results more in line with actual human sentiment. The researchers found similar results when testing hypothetical features, like built-in projectors for laptops.
Key Insight: Strategic Integration, Not Replacement
“For anything beyond early-stage high-level trend detection, human research remains essential.” [3]
The most successful application of this technology comes from understanding it as an augmentation tool rather than a replacement for traditional research. Given that LLMs are trained on static data, they may not reflect current market conditions without receiving frequent updates and new data. This allows companies to follow a new innovation roadmap: broaden the top of the innovation funnel by using AI, but keep the bottom narrow through sharper, more cautious analysis.
Why This Matters
Synthetic customers might not totally replace human research, but they can dramatically enhance it. For business leaders and executives, this represents a fundamental shift in the speed and scope of innovation strategy. The ability to rapidly test multiple prototypes or concepts at low cost could mean faster time-to-market, reduced development risk, and more efficient resource allocation. Organizations that build internal AI-powered customer simulation capabilities could gain a significant competitive advantage from fine-tuning models with their proprietary data, creating a virtuous cycle where better data leads to better insights. At the same time, decision makers and marketing professionals must be vigilant to recognize and respond to the shortcomings of these new technologies and tools.
Bonus
Learn more about the authors’ original research, and go a step further with the GenAI + Marketing Learning Module from D^3. You’ll learn the basics of engaging an LLM, with broadly applicable and actionable techniques to create content, automate tasks, and revolutionize workflows. Then the program will take a deep-dive to discover how AI can redefine your early-stage marketing research.
References
[1] James Brand et al., “Using Gen AI for Early-Stage Market Research,” Harvard Business Review, July 18, 2025, https://hbr.org/2025/07/using-gen-ai-for-early-stage-market-research.
[2] Brand et al., “Using Gen AI for Early-Stage Market Research.”
[3] Brand et al, “Using Gen AI for Early-Stage Market Research.”
Meet the Authors
James Brand is Principal Researcher and Economist in the Office of the Chief Economist at Microsoft.
Ayelet Israeli is the Marvin Bower Associate Professor of Business Administration at Harvard Business School and co-founder of the Customer Intelligence Lab at the Digital Data Design (D^3) Institute at Harvard. She studies omni-channel and e-commerce markets, and her research focuses on data-driven marketing, with an emphasis on how businesses can leverage their own data, customer data, and market data to improve outcomes.
Donald Ngwe is Senior Director of Economics in the Office of the Chief Economist at Microsoft.