06-reference

WriteWithAI — Build & Launch A VOC Landing Page With Claude Code In 5 Steps

Wed May 06 2026 20:00:00 GMT-0400 (Eastern Daylight Time) ·reference ·source: WriteWithAI ·by Dickie Bush, Cole Schafer
voice-of-customercopywritingredditclaude-codemcplanding-pagesvercelwritewithaidickie-bushcole-schafer

WriteWithAI — VOC Landing Page With Claude Code (5 Steps)

Why this is in the vault

Files the PullPush MCP + Reddit-copy-mining technique as a candidate workflow for any future RDCO landing-page work — MAC info-product, Sanity Check subscribe page, Squarely paid-ads creative testing. The “Reddit primary-source corpus beats Deep Research synthesis” framing is a useful prior even if oversold; the technique itself (save raw quotes to a file, synthesize at the end) is a clean copy-mining loop worth keeping when 2026-04-15-thariq-claude-code-session-management-1m-context-style context-rot considerations apply. PullPush MCP itself is a security-review candidate per the feedback_mcp_install_security_review_default SOP before any always-on agent install.

Source

Core thesis

Best conversion copywriters don’t write copy — they steal it from customers’ mouths. Most AI users reach for ChatGPT/Claude Deep Research and get a polished 12-page synthesis of secondary sources. That’s the opposite of what you want. Voice of Customer (VOC) research demands primary sources — unfiltered customer language — which lives on Reddit, in reviews, and in forum threads, not in research-agent reports.

The frame contrast is sharp: Deep Research synthesizes the wrong thing well. Reddit copy-mining surfaces the right thing raw.

The 5 steps

Step 1 — Install the PullPush MCP connector

Step 2 — Build a Pains & Gains modifier list

Pick ONE research goal per query. Six options:

  1. Find pain (people complaining)
  2. Find buying intent (people asking what to buy)
  3. Find comparisons (X vs Y)
  4. Find workflows (how do you actually do this)
  5. Find objections (why people don’t buy)
  6. Find switching signals (people leaving a tool)

Generate modifier phrases (2–4 words) with this prompt skeleton:

“I’m doing VOC research on [topic]. My research goal is to find [pain / buying intent / comparisons / workflows / objections / switching signals]. Give me 10 phrase snippets (2–4 words each) that people would actually type into a Reddit comment when they’re in this state.”

Pain examples: “takes forever,” “no one downloads,” “doesn’t convert,” “wasted my time.” Desire examples: “I want,” “I wish,” “where can I find.”

Step 3 — Mine Reddit into a swipe file

Three-prompt sequence (run in Claude Code, one at a time):

Prompt 1 — Find hot threads:

“Use PullPush to find the 10 most-discussed threads in r/[subreddit] about [topic], with at least 20 upvotes. Show me titles and links.”

Prompt 2 — Mine comments for language:

“Now search comments in r/[subreddit] for ‘[modifier phrase]’ and ‘[topic]’. Give me 15 results sorted by score. Save the verbatim comment text to a file called [topic]-voc.md.”

Repeat with 2–3 phrase variations. After 4–5 prompts you have 50–100 verbatim quotes.

Prompt 3 — Synthesize at the end (not after every query):

“Read [topic]-voc.md. Pull the top 10 verbatim quotes that capture pain about [topic]. Group them by recurring pattern (time waste, conversion struggles, copy problems, etc.). Then tell me which 3 phrases would work as landing page headlines.”

Key technique: save raw quotes to a file, synthesize at the end — don’t make Claude re-summarize across turns. Cheaper and avoids context bloat.

Step 4 — Turn swipe file into landing page copy

Feed swipe file + product description to Claude. Author’s example headline pulled from his research: “What’s the value of traffic that doesn’t convert? $0”

Pro tip: train claude.ai/design on your brand style guide first, then share the design system with Claude Code so the page renders on-brand from the start.

Step 5 — Deploy via Vercel

Mapping against Ray Data Co

Strength: medium.

The “Reddit copy-mining beats AI Deep Research” framing is a sharp prior worth holding. Reddit’s structured complaint/pain-point threading IS the dataset that Deep Research often hallucinates around — it’s the closest thing to a clean primary-source corpus on most B2C/SMB topics.

Where this could intersect existing RDCO surfaces:

Where the framing breaks down: Deep Research is not the opposite of VOC. They’re complementary. Deep Research is the right tool when you’re synthesizing across many secondary sources for a strategic question (market sizing, competitive landscape). VOC mining is the right tool when you’re writing copy that needs to mirror a specific reader’s inner voice. The headline contrast oversells the dichotomy — file the technique, don’t adopt the frame uncritically.

What to actually do with this

  1. Hold the technique for the next time RDCO needs landing-page copy — MAC, Sanity Check subscribe surface, Squarely.
  2. Evaluate PullPush MCP for installation on the always-on agent (security review first).
  3. Don’t import the frame that “Deep Research is wrong” — both tools have valid use cases.
  4. No newsletter writeup of this technique solo — it would be a derivative Sanity Check piece restating WriteWithAI. If RDCO uses it for an actual MAC or Sanity Check page, the result (case study with the pulled quotes + finished page) is the publishable artifact, not the method recap.