In this episode of Digital Marketing Masters, I sit down with product development and consumer insights expert Kay Allison to explore how AI can supercharge audience research and product innovation. We discuss why so many entrepreneurs (and big brands) mistakenly project their own preferences onto customers, how to recognize different decision-making styles and motivations, and the importance of uncovering real human conflicts—those tension points between what people want and what’s available. Kaye shares practical, low-cost methods to mine authentic customer conversations from Reddit, reviews, and social platforms, plus how to structure prompts for AI tools to identify segments, surface unmet needs, and turn messy qualitative data into actionable insights.
We also dive into advanced and “poor man’s” workflows: using Reddit’s Answers, exporting forum threads into docs, and uploading them to AI for synthesis; directing models to targeted sources and timeframes; and crafting context-rich prompts that specify role, data, output, and formatting. Kaye outlines a replicable prompt framework and offers her custom GPT for sorting customer data. We wrap by connecting these skills to broader problem-solving—from consumer research to complex health contexts—while emphasizing ethical use and the power of better questions to get better answers.
Kay: https://kayallison.com/

https://serve.podhome.fm/episodepage/digital-marketing-masters/276

(01:27) Kaye’s one‑breath background and philosophy on consumer insight

(03:06) Entrepreneurs are not their customers: assumptions and humility

(04:28) Different decision styles and motivations beyond business growth

(06:13) Moving past basic AI use cases to learn your audience

(06:35) Mining Reddit and reviews; social listening with AI

(10:08) Hands‑on with Reddit Answers and broadening competitive sets

(11:32) Scrappy workflows: collecting posts and analyzing with AI

(12:07) Low‑cost tools, brand mentions, and what to ask AI

(12:32) Finding conflicts, segments, and gaps in consumer data

(14:10) Directing AI research: where to look and time windows

(16:01) Human nuance vs aggregates; walls around data sources

(18:36) Fragmented health data and DIY analysis with AI

(21:01) Asking better questions: from focus groups to prompting

(23:04) Context engineering and prompt structure best practices

(26:06) Non‑diagnostic framing and advocating with your doctor

(27:33) How to contact Kaye and her custom GPT offer

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