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YES

As of 13 August 2026, AI can find common complaints about your competitors.

This still needs a person who signs their name to it.

Can you do it?

15 minutesto a draft.

30 minutesto something you’d act on.

Cost, all in£0

Skill neededchat-fluent

Who has to check ityou

What the alternative costsNo priced human alternative is supplied in the available tool data.

If this goes wrong: you treat a loud or unrepresentative set of public comments as a common customer problem and make a poor product or positioning decision.

What to actually do

  1. Hand it to a person

    The route this page recommends

    A person who owns the outcome does this end to end, worth it when the failure is dear.

  2. Use a tool built for this

    Second choice
  3. Do it yourself

    The distant third

    A chat interface, chat-fluent skill, and roughly 30 minutes until you can act on the result.

    How to actually do it

    1. Open a spreadsheet and create columns for competitor, source URL, source type, date, verbatim excerpt, complaint theme and notes.
    2. List the competitor names, UK market, product area and customer journey you want to compare, and define what counts as a relevant complaint.
    3. Paste the prompt into an AI research tool and replace the bracketed slots with that scope and competitor list.
    4. Copy each cited example into the spreadsheet, keeping the source URL and exact excerpt rather than only the AI summary.
    5. Open the cited pages and remove entries that are inaccessible, duplicated, clearly promotional or not genuine customer evidence.
    6. Ask the AI to regroup the cleaned spreadsheet into themes and compare the result with your product priorities and any existing customer research before using it in a strategy document.

    Prompt

    Research common customer complaints about these competitors: [COMPETITOR NAMES]. Focus on [PRODUCT, SERVICE OR CUSTOMER JOURNEY]. Use publicly accessible sources relevant to the UK where available, such as review pages, discussion forums, app-store reviews and public social posts. Do not infer complaints from marketing copy, one isolated comment or an unverified claim. For every complaint example, provide the source URL, source type, publication date if shown, a short verbatim excerpt and the competitor it concerns. Remove duplicates and group the evidence into complaint themes. For each theme, report: the theme name, what customers appear to be complaining about, the number of distinct examples found, the named competitors, the source links, and a confidence label based only on the quality and spread of the evidence. Separate direct customer comments from your interpretation. Do not claim that a complaint is common across all customers or that a competitor has breached a law. Flag inaccessible, deleted, sponsored or obviously duplicated sources. Finish with a table of themes and a short section called 'What this research cannot establish'.

    Open it prefilled in ChatGPT or Claude, or copy it into Gemini, which takes no prefill link.

What it gets wrong

  • Cannot see private complaints in support systems, closed communities or customer interviews unless you provide the material.
  • Cannot establish the true frequency of a complaint because public posters are a self-selected sample.
  • Cannot reliably distinguish a genuine customer, a competitor, a review campaign or a duplicated post in every case.
  • Cannot decide whether a complaint matters to your target customers without your market context and product judgement.
  • Cannot turn public criticism into a fair claim about a competitor without preserving the evidence and its limits.

Even on a YES, the friction has a name: verification cost, judgement under ambiguity and context depth.

How we scored this

Five axes, each scored nought to two by hand: ten means AI carries the task cleanly, and the thresholds that turn a total into YES, PARTLY or NO are published in the methodology. Each axis name links to its definition.

AxisScore (0–2)
Output2
Inputs2
Verification1
Liability2
Effort delta2
Total9 / 10

FAQ

Can ChatGPT find complaints about my competitors?
Yes, it can search and organise complaints from public sources when you give it a clear competitor list and scope. Treat the result as directional research: open the cited pages and check that the themes are supported by distinct, relevant comments.
Can AI tell me what customers dislike about competitors?
It can identify recurring themes in public comments and quote the evidence behind them. It cannot show that those themes represent all customers, or access private complaints that are not publicly available.
How do I use AI for competitor complaint research?
Ask for source URLs, dates, verbatim excerpts, duplicate removal and separate confidence labels for each theme. Put the evidence in a spreadsheet, check the pages yourself, then compare the themes with your own customer research and product priorities.
Is it legal to collect complaints about competitors with AI?
Public-source research is not automatically safe to republish, and an AI summary can turn an isolated allegation into a misleading claim. Keep the original evidence, avoid personal data and unsupported accusations, and get legal advice before publishing comparative claims.

Nearby answers

Assessed by gpt-5.6-luna (gpt-5.6-luna) on 2026-08-13, second-checked by an independent model. Wrong somewhere? Email [email protected] and it gets re-checked.

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