🌟 Editor's Note
Another exciting week in the world of Artificial Intelligence, Business, and Productivity. Today we dive into the Market Mapper Agent — your first step toward building scalable money models with AI.
Outcome: A clean spreadsheet of your niche’s top pains, desired outcomes, buying triggers, objections, and “money model” opportunities — pulled from real customer conversations.
You’ll need (15–45 min): ChatGPT (or your LLM of choice), Google Sheets, and 5–10 public URLs (Reddit threads, Quora questions, YouTube comments, Amazon/app reviews, niche forums).
Tip: Follow site TOS and only use public data.
🗓️ Upcoming Deadlines
Apply to Be the Demo Company
At the end of this 10-issue series, we’ll host a live workshop where we build an AI Money Model in real-time. Want your business to be the example? Apply now.
Workshop Date: Saturday, Sep 20, 2025 at 10:00 AM CT
Application Deadline: Friday, Sep 12, 2025
👉
Reader Challenge: Market Mapper Agent
Your action step this week is to run the Market Mapper prompt on your niche.
Deadline to Share Results: Sunday, March 23, 2025
Where: Reply to this email or drop your insights in our community thread.
Today’s Big Idea
Most offers fail because they’re built on assumptions.
The Market Mapper Agent kills guesswork by pulling customer pains straight from Reddit, Quora, Amazon reviews, and forums — then clustering them into themes with money potential.
When you know exactly what people want (and what they’re paying for already), your offer basically writes itself.
🔧 Tool of the Week — The Market Mapper Prompt
Step 0 — Quick Source Hunt (3–5 min)
Use these Google queries (copy–paste, replace {niche}):
site:reddit.com "{niche}" problems
site:reddit.com/r/{niche} “how do I”
site:quora.com "{niche}" “biggest challenge”
site:youtube.com "{niche}" “review” OR “scam” OR “worth it”
site:amazon.com "{niche}" “customer reviews”
Grab 5–10 of the best links.
Master Prompt (Tell-and-Show Format)
Paste this once, then feed it your URLs or pasted comments when asked:
IDENTITY
You are “Market Mapper,” an extraction + clustering analyst that converts noisy customer language into structured opportunity maps for building scalable money models.
CONTEXT
Niche: {describe your niche in 1–2 lines}
Target buyer: {who they are + job title/role}
Goal: Extract pains, outcomes, triggers, objections, and spending behaviors from PUBLIC text and cluster them into money-model themes.
GUIDELINES
- Read only the text I provide (or the public URLs I paste).
- Preserve customer language; quote short lines verbatim when useful.
- Be concrete. No generic marketing speak.
- Output clean, machine-readable tables.
- Include lightweight source references (URL or “Source #3”).
OUTPUT #1: RAW FINDINGS (TABLE)
Columns (exactly):
id | pain_point | desired_outcome | current_workaround | trigger_event | objection | urgency(1–5) | dollars_spent_est | representative_quote | source
OUTPUT #2: CLUSTERS
- Cluster the rows into 8–12 themes with names like “Lead Quality,” “Fulfillment Bottlenecks,” etc.
- For each cluster: size (#rows), intensity avg(urgency), money potential (low/med/high), why-now insight (1–2 bullets).
OUTPUT #3: MONEY MODEL ANGLES
For the top 5 clusters, propose 3 monetization patterns each:
- Tool/SaaS, Service/Concierge, Info/Training, Marketplace, or Hybrid.
- For each: core promise, rough price range, key KPI you improve.
VALIDATION
List the 5 most common phrases buyers use (n-grams) and 10 SEO/paid keywords to test.
PROCESS
1) Acknowledge and ask me for either:
a) a batch of URLs, or
b) pasted comment text.
2) After extraction, show OUTPUT #1–#3.
3) Offer to export as CSV.
Fast “Paste-Only” Prompt (for raw comments)
Use this if you’ve copied text directly from a page (Reddit, Quora, YouTube comments, etc.):
DATA (Customer comments from {source label}):
"""
{paste up to ~1,500 words of public comments here}
"""
Task: Extract rows for OUTPUT #1 using the same columns:
id | pain_point | desired_outcome | current_workaround | trigger_event | objection | urgency(1–5) | dollars_spent_est | representative_quote | source({source label})
Return only a markdown table. Keep quotes under 25 words.
Repeat for each page. Once you’ve got 80–150 rows total, run:
Now perform OUTPUT #2 (Clusters) and OUTPUT #3 (Money Model Angles) on the combined table you’ve built so far.
CSV Export Prompt (for Google Sheets)
Convert the current RAW FINDINGS table into CSV.
- Use commas as separators.
- Escape commas in quotes.
- No extra commentary.
Copy the CSV → Google Sheets → File ▸ Import ▸ Upload.
Weekly Auto-Refresh (Optional, ~10 min setup)
Scraper: Apify / Browse AI / Bardeen → pull new comments from the same URLs or searches.
Store: Append to Google Sheets tab “raw_feed.”
Summarize: Run a daily/weekly summarizer Agent to turn new rows into a 1-page insights brief.
Summarizer Prompt:
Summarize new rows added to “raw_feed” since {YYYY-MM-DD}.
1) Top 10 pains (with counts)
2) New/changed objections
3) Shifting triggers
4) Updated cluster scores (size, intensity)
5) Any emerging money-model angles
Return 1-page brief with bullet points and a “Test Next” list (3 paid keywords, 3 content hooks, 1 offer tweak).
✅ Done-When Checklist
80–150 rows in RAW FINDINGS with real quotes + sources
8–12 clear clusters with size × intensity × money potential
Top 5 clusters each mapped to 3 monetization patterns
A “Test Next” list: 10 keywords, 5 hooks, 3 offers
Weekly refresh automation scheduled
🚀 What You’ll See Immediately
Crystal-clear customer language for ads, landing pages, and offers
A ranked list of where the money actually is (not guesses)
Ready-made angles for SaaS, service, training, or hybrid “money models”
🤔 Did You Know?
The first chatbot was built in 1966 — ELIZA. She mimicked a psychotherapist, and people actually felt emotionally attached to her responses. Proof that humans connect deeply with even the simplest AI.
Till next time,
AI Out of the Internet — Newsletter
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