A fantastic example of this comes from Isabella Ritz, who on Seller Sessions detailed her process for using AI for deep product and market research. She uses tools like ChatGPT for analyzing customer feedback in a really smart way. Instead of just looking for positive or negative sentiment, she performs deep dives on customer reviews for existing products in a niche. By feeding this data into an AI, she can ask it to identify the most common pain points, repeated complaints, and features customers wish the products had. This isn’t about just getting a summary; it’s about using the AI to systematically pull out unmet needs from thousands of lines of text, which becomes a blueprint for creating a product that people are already asking for.
This method allows you to build a product that directly addresses the market’s shortcomings. As Isabella explained, it’s a powerful way to de-risk product development. Danny McMillan and Dorian Gorski, also on Seller Sessions, took this idea even further. They talked about how AI can be used for forum scraping and understanding buyer psychology on a much broader scale. You can move beyond your direct competitors’ Amazon reviews and look at what people are discussing on Reddit, in hobbyist forums, and on social media. This gives you a richer, more candid view of the customer’s world, their language, and their "why." It moves your research from just data collection to genuine insight, helping you craft marketing messages that resonate because they’re built from the customer’s own words.
A great contrasting example of using AI for market research came from Steve Chou on The EcomCrew Ecommerce Podcast. He didn't use it to research a new product, but to better understand his existing market. Steve fed his entire customer list into an AI and tasked it with identifying potential B2B customers who were hiding in his DTC data. The AI looked for patterns, like customers from company email domains or those making unusually large or frequent purchases. This is a brilliant, often overlooked form of market research that finds new revenue opportunities within the audience you already have, simply by looking at the data in a new way.
Whether you’re analyzing public reviews, scraping forums, or mining your own customer lists, the principle is the same. As Arv Natarajan mentioned on Ecommerce Coffee Break, your goal is to feed the AI data that reflects user intent, like on-site behavior, searches, clicks, and purchases. The AI’s job is to be an tireless research assistant that can sift through enormous amounts of messy, unstructured human language and find the actionable signals. It’s not about replacing your intuition as a founder; it’s about feeding that intuition with better, clearer insights directly from your market.