AI & Market Research - Can AI take surveys for us? I haven't posted about too many uses of AI with Market Research to date, however I think this case study should get a mention. A recent study by PyMC Labs and Colgate‑Palmolive finds that large language models (LLMs) can closely simulate human responses in consumer product surveys—hitting ~90% of human test–retest reliability across 57 surveys (9.3k human responses). How it worked: Instead of asking a bot for a number, the team asked for a short written answer with a why. That text was then compared to five Likert “anchor” statements using embedding similarity (their Semantic Similarity Rating / SSR method). Result: 90% of human test-retest reliability. They mirrored distributions of responses across demographic segments such as age and income. Researchers emphasise that this doesn’t mean LLMs think like people, but their outputs can align with human behavioural patterns under defined conditions Want to try this? 1) Start with a low-risk, high-volume concept test you already run. 2) Write clear Likert anchors (1–5) as plain sentences. 3) Prompt an LLM for a short rationale per concept and then map that text to anchors using embedding similarity. 4) Create a small human holdout to benchmark; only proceed if your synthetic vs human gap is tight. 5) Be transparent: label synthetic vs human, document prompts, and keep humans in the loop for final calls. Imagine being able to test new tourism or retail products or ad concepts instantly across synthetic “audiences”, before spending on full panels. Huge potential for insight and speed — as long as we keep the human layer where it matters most: judgement, context, and creativity. This isn’t about replacing people; it’s more about "simulate → iterate → validate" so teams can test more ideas, faster, and spend human budget where judgement matters most. Full story below 👇 #AI #MarketResearch #Insights #DigitalTransformation #SyntheticData #Tourism #Innovation https://lnkd.in/eRBWtRcs
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Week #4 of Stanford Synthetic Personas Seminar The seminar suggests retiring the question of whether AI synthetic personas can simulate humans in social research. The answer is: “Yes, they can (emphasis on ‘can’).” There’s ample evidence (see our syllabus bibliography) that AI personas can accurately simulate data that would be expected from humans given the same instructions and measures. So…case closed on AI personas vs. humans? No. There is no blanket evaluation applicable to ALL uses of AI personas. What matters most is differentiating good research from bad research – for humans and AI. Here’s a list of some best practices (so far) that are making AI persona projects better: 1️⃣ CONTEXT. Tell the AI personas a lot about the context in which they should answer questions (e.g., are they sitting in front of a screen at home, shopping in a retail store, in a university laboratory). 2️⃣ MESSAGE SAMPLING. Allow AI personas to evaluate multiple versions of media messages. Stimulus sampling is rare but critical in human research, but it’s often not done because we fear that people won’t pay attention to lengthy presentations. That’s not a problem with AI personas. 3️⃣ SAMPLE SIZES AND SPECIFICTY. The same considerations that make for good sampling in human studies likely apply to AI personas. Conduct power analyses to determine best sample sizes. Specify exact groups that should be sampled. 4️⃣ HOMOGENEITY. Tell the AI personas that they should represent their unique point of view and not try to estimate the average of what all humans think (i.e., the goal is to have the AI personas contribute to realistic variance). 5️⃣ STUDY DESIGN. Allow each AI persona to see all of the different messages of interest rather than dividing personas into groups that each see only one message. This allows each AI persona to compare messages, which increases sensitivity to differences. 6️⃣ MULTIPLE MEASURES. Ask the AI personas about the messages using different measures and then compare the answers. Use both quantitative and qualitative questions. Experiment with different wording for survey questions. 7️⃣ IMPLICIT BACKGROUNDS. Prompting AI personas to represent explicit demographics (e.g., gender, race, income) can unintentionally increase model bias in favor of the highlighted demographics. Instead, prompt SPs with implicit information (e.g., psychographics, household composition, neighborhood characteristics) that better represent social biases that are important to include in research. Our draft conclusion about AI personas is this: Of the ~$100B spent annually on commercial and academic research, a substantial portion of that work can probably be done with AI personas at great advantages in cost, time and research quality. But it’s not as simple as typing in a question to an LLM. Concentrate on techniques (and software) that allow AI personas to be the best human simulations possible.
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If you're a #researcher, this report should be on your Must-Read list! Elsevier last week shared the results of its global Researcher of the Future survey, offering fresh insights into how researchers view the rapidly evolving research landscape. Drawing on insights from more than 3,200 academic and corporate researchers across 113 countries, the report highlights widening regional differences in researcher attitudes, evolving views on mobility, and a shift in how researchers see their own role in a changing world. #Research #Researchers #Elsevier #AI
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Elsevier last week shared the results of its global Researcher of the Future survey, offering fresh insights into how researchers view the rapidly evolving research landscape. Drawing on insights from more than 3,200 academic and corporate researchers across 113 countries, the report highlights widening regional differences in researcher attitudes, evolving views on mobility, and a shift in how researchers see their own role in a changing world. #Research #Researchers #Elsevier #AI
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Elsevier last week shared the results of its global Researcher of the Future survey, offering fresh insights into how researchers view the rapidly evolving research landscape. Drawing on insights from more than 3,200 academic and corporate researchers across 113 countries, the report highlights widening regional differences in researcher attitudes, evolving views on mobility, and a shift in how researchers see their own role in a changing world. #Research #Researchers #Elsevier #AI
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Elsevier last week shared the results of its global Researcher of the Future survey, offering fresh insights into how researchers view the rapidly evolving research landscape. Drawing on insights from more than 3,200 academic and corporate researchers across 113 countries, the report highlights widening regional differences in researcher attitudes, evolving views on mobility, and a shift in how researchers see their own role in a changing world. #Research #Researchers #Elsevier #AI
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Elsevier last week shared the results of its global Researcher of the Future survey, offering fresh insights into how researchers view the rapidly evolving research landscape. Drawing on insights from more than 3,200 academic and corporate researchers across 113 countries, the report highlights widening regional differences in researcher attitudes, evolving views on mobility, and a shift in how researchers see their own role in a changing world. #Research #Researchers #Elsevier #AI
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Elsevier last week shared the results of its global Researcher of the Future survey, offering fresh insights into how researchers view the rapidly evolving research landscape. Drawing on insights from more than 3,200 academic and corporate researchers across 113 countries, the report highlights widening regional differences in researcher attitudes, evolving views on mobility, and a shift in how researchers see their own role in a changing world. #Research #Researchers #Elsevier #AI
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Elsevier last week shared the results of its global Researcher of the Future survey, offering fresh insights into how researchers view the rapidly evolving research landscape. Drawing on insights from more than 3,200 academic and corporate researchers across 113 countries, the report highlights widening regional differences in researcher attitudes, evolving views on mobility, and a shift in how researchers see their own role in a changing world. #Research #Researchers #Elsevier #AI
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Elsevier last week shared the results of its global Researcher of the Future survey, offering fresh insights into how researchers view the rapidly evolving research landscape. Drawing on insights from more than 3,200 academic and corporate researchers across 113 countries, the report highlights widening regional differences in researcher attitudes, evolving views on mobility, and a shift in how researchers see their own role in a changing world. #Research #Researchers #Elsevier #AI
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Elsevier last week shared the results of its global Researcher of the Future survey, offering fresh insights into how researchers view the rapidly evolving research landscape. Drawing on insights from more than 3,200 academic and corporate researchers across 113 countries, the report highlights widening regional differences in researcher attitudes, evolving views on mobility, and a shift in how researchers see their own role in a changing world. #Research #Researchers #Elsevier #AI
Kellton•2K followers
5moFascinating application of LLMs in market research. The SSR approach really bridges the gap between human nuance and AI precision, offering a scalable way to simulate early-stage consumer insights without losing behavioral realism. Completely agree that the key lies in keeping human judgment where it matters most: context and creativity.