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How do I mine public reviews of competitors for the phrases customers use to praise and complain, and turn them into positioning and messaging angles

the agent that answers this

review mining for positioning. mine public reviews of competitors for the phrases customers use to praise and complain, and turn them into positioning and messaging angles.

Updated Jul 12

Cost: Free; on your own plan
Runs: On demand; or scheduled
Built from: 3 steps; 1 verified skills
Runs in: Claude or Codex; as you

what you get

a positioning insight set: what customers love and hate about alternatives, in their words, and the angles those gaps open for you.

insights_minedmessaging_sharpened

example result

illustrative

A finished run delivers: a positioning insight set: what customers love and hate about alternatives, in their words, and the angles those gaps open for you. Each run captures: insights_mined, messaging_sharpened.

Example output, to show the shape of the deliverable. It has not run on your data. Build the agent to get this on yours.

the steps

3 steps · 1 from verified skills

Step 1
collect public reviews of the relevant competitors
Structured competitive analysis with feature matrices, SWOT, positioning maps, and UX review. Covers research frameworks, pricing comparison, review mining,...
--- name: competitor-teardown description: "Structured competitive analysis with feature matrices, SWOT, positioning maps, and UX review. Covers research frameworks, pricing comparison, review mining, and visual deliverables. Use for:…
Full skill: Competitor Teardown
Step 2
extract the recurring praise and complaint themes
Your model fills this step
Step 3decision
translate the gaps into positioning and messaging angles
Decision step

Run this agent in Implexa, on your own Claude or Codex, free

Get the Implexa app (or connect your Claude Code / Codex), then say build the review mining for positioning agent and approve the schedule. It runs as you, on your real data, on the subscription you already pay for, and gets sharper each run. Your agent's memory is yours and travels with you across Claude, Codex, and whatever comes next. About 5 minutes to your first real run.

Run this agent in Implexa →

common questions

How do I mine public reviews of competitors for the phrases customers use to praise and complain, and turn them into positioning and messaging angles?

The review mining for positioning agent. a positioning insight set: what customers love and hate about alternatives, in their words, and the angles those gaps open for you.

Is the review mining for positioning agent free?

Yes. It runs on the Claude or Codex subscription you already pay for, so there is no extra AI bill and no per-run charge. You can build and run unlimited agents on the free plan.

How often does the review mining for positioning agent run?

You choose: run it on demand, or put it on a schedule (hourly, daily, weekly). Once scheduled it runs unattended, as you, on your own machine.

What does the review mining for positioning agent need to run?

Just Implexa installed in your Claude or Codex. There are no fragile integrations to wire up, and Implexa never touches your accounts, passwords, or the contents of your work.

Does the review mining for positioning agent use my data? Is it private?

It runs as you, on your own machine, on your real data. The model runs inside your own Claude or Codex, so Implexa never sees your data, accounts, or credentials. Your agent's memory is yours and travels with you across Claude, Codex, and whatever comes next.

How do I build the review mining for positioning agent?

Install Implexa into your Claude or Codex, then say "build the review mining for positioning agent" and approve the schedule. Implexa assembles the 3 steps (1 from verified skills) and it runs on its own. About 5 minutes to your first real run.

Can I change what the review mining for positioning agent does?

Yes. Tell it what to change in plain language and it revises its steps; the next scheduled run uses the change, with no re-scheduling. Every change is versioned, and a run can even propose its own improvements.