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Parker Rex DailyMay 22, 2025

How I Use AI For Market Research

Discover how I use AI for market research: PRDs, churn trends, growth, landing pages, VS Code tips, and AI tools shaping my strategy.

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Show Notes

Parker maps out how he’s using AI for market research to reshape his community offering, test tooling, and build a scalable content engine. Practical, punchy notes on what he’s trying, what’s working, and what he’s loading into the AI stack next.

AI tooling and experiments

  • Augment: asked it to produce an implementation plan from a highly detailed PRD; results influence landing-page work (Astro) and overall IA.
  • VS Code: using the editor as a strategy workspace to draft and refine plans.
  • Postgres plugin (Microsoft): new plugin with schema visualization, database explorer, file navigator, query history, and IntelliSense—aims to bring DB visibility into the IDE.
  • ConvexH: attempted to auto-build the entire app; interesting pattern guidance, but/**
    • Strong opinions on backend patterns if you’re TS-based; less ideal if your stack is Python.
  • TanStack tooling and early beta explorations: experimenting with how TanStack patterns fit into the app/landing-page workflow.
  • Astro for landing pages: ongoing choice for front-end architecture.
  • Echo (YouTube metadata automation SAS): automates generating subtitles, metadata, and parts of the video script into a production pipeline; current gaps are thumbnail/YouTube interactions and end-to-end UX.
  • Notebook LM: slides into a core theme—useful for scaling coding research and pre-planning R&D work instead of prompting blindly.

Market research and ICP strategy

  • Repositioning the community: three subsegments under a single AI-for-developers intent.
    • Subsegments: tech-adjacent (dipping toes in), developers on teams who want to level up, and business owners who see ROI from AI skills.
    • The aim is a single strong ICP with nuanced targeting, not a generic “everyone who codes” approach.
  • Research approach:
    • Name-generation prompts and origin-story prompts to study company names.
    • Compare and model communities beyond school-based ones.
  • 3-tier community model:
    • Tier 1: online community
    • Tier 2: marketplace with partner distribution and member discounts
    • Tier 3: IRL events
  • Evergreen content pillars + channel mapping + automations:
    • Build a content engine where actions feed back into growth and retention (action 1 → action 2 → action 3+).
  • Metrics focus:
    • MOM growth and churn as the north star; LTV/CAC is less critical for his YouTube-driven model.
    • Target churn: sub-1% if possible; current churn in high single digits due to broad initial ICP.
  • Offer design via problem mapping:
    • Reddit scraping to identify adjacent problems and craft offers around them.
    • Emphasis on riffing and iteration—prompts are thought partners, not one-size-fits-all.

Content strategy and execution

  • Evergreen content pillars and a clear content engine wheel.
  • Channel mapping and automations that support value delivery and retention.
  • Not just “Parker the news guy”—aiming to be a builder who marketing-savvy; alignment of content with the evolving ICP.
  • Thumbnails and templating:
    • Experimenting with template thumbnails to enable programmatic generation and more variety beyond Parker’s face with text.

Community experience and engagement

  • Discord automation MVP:
    • V0: index two channels, notify members when relevant topics appear; onboarding and post-event roundup.
    • V1: add Q&A via DMs and more targeted sentiment prompts; future: smarter prompts and role-based notifications.
  • Aiming to elevate membership experience by delivering timely, role-aware information and easier access to important updates.

AI in the software development life cycle (SDLC)

  • Notebook LM is a core example of using AI for upfront research and planning:
    • Reduces “blind prompting” by providing structured research and rationale.
    • Helps with coding tasks by bridging R&D, architecture, and feature delivery.
  • The broader view: AI-supported research, architecture, bug-hunting, and feature development—not just code generation.

Takeaways (actionable)

  • Define a precise ICP with three subgroups and tailor messages to each, then test and refine.
  • Use Augment to convert detailed PRDs into concrete implementation plans; treat prompts as iterative collaborators.
  • Build a structured content engine: evergreen pillars, channel mapping, and automations to compound impact.
  • Design a three-tier community model early: online, marketplace with partner distribution, and IRL events.
  • Run a minimal Discord automation MVP to index key channels and notify members; plan DM-backed Q&A in later iterations.
  • Use Echo to streamline YouTube metadata workflows; expect to iterate on thumbnails and upload integration.
  • Leverage Notebook LM for research-heavy tasks to reduce blind prompting and speed up decision-making.
  • Keep churn in the single digits and monitor MOM growth; constantly tighten the ICP to reduce leaky funnel effects.

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