As of 13 August 2026, AI can only partly compare your Google reviews with your competitors' reviews.

Most people should hand this to a purpose-built tool.

Can you do it?

15 minutesto a draft.

1 hourto something you’d act on.

Cost, all in£0

Skill neededchat-fluent

Who has to check ityou

What the alternative costsA human analyst can collect and compare the review data for you, but no price is stated here.

If this goes wrong: you treat a biased or incomplete review sample as market evidence and change your offer, service or priorities in the wrong direction.

What to actually do

Use a tool built for this
The route this page recommends
One pick for this task
Julius AI
Julius AI is designed to analyse uploaded spreadsheets and produce charts and analysis, which fits a prepared review dataset better than a general website-building tool.

Do it yourself

Second choice

A chat interface, chat-fluent skill, and roughly 1 hour until you can act on the result.

How to actually do it

Open your Google Business Profile review data or another permitted export, and copy the review text, rating, date and source URL for your business into a spreadsheet.
Open each competitor's public Google listing and record the same fields for a clearly defined date range, without presenting a small sample as the complete review history.
Add columns for business name, review date, rating, review text and source URL, then remove duplicate rows and mark missing or inaccessible reviews.
Paste the spreadsheet data and the completed prompt into Julius AI or a chatbot, replacing the bracketed fields with your business name, competitors and comparison period.
Ask the model to produce the comparison, then check every quoted review, count and source URL against the spreadsheet and the original listing.
Remove conclusions based on uneven samples, and choose actions only after comparing the flagged themes with your complaints, cancellation reasons, customer feedback and current service priorities.

Prompt

Compare my business's Google reviews with the competitor reviews supplied below. Use only the review text, dates, ratings, business names and source URLs that I provide. Do not invent reviews, counts, themes, customer motives or competitor facts. First state the dataset coverage and any important gaps, including differences in date range, number of reviews, rating scale or business type. Then produce: 1) a table of recurring positive and negative themes for my business and each competitor, with the number of supplied reviews containing each theme; 2) representative exact quotes, labelled with the business and source URL; 3) themes that appear distinctive to my business; 4) themes where competitors appear stronger; 5) complaints that may indicate an actionable service or product issue; and 6) five cautious business actions, each linked to the evidence. Separate observed evidence from interpretation. Do not claim that the supplied sample represents all customers. Flag any theme supported by only a small number of reviews. Finish with the three most useful follow-up questions I should investigate. My business: [BUSINESS NAME]. Review data and source URLs: [PASTE YOUR DATA]. Competitor review data and source URLs: [PASTE THE COMPARABLE DATA]. Comparison period: [DATE RANGE]. Competitors included: [NAMES].

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

Hand it to a person
The distant third
A person who owns the outcome does this end to end, worth it when the failure is dear.

What it gets wrong

AI cannot obtain a complete, consistently dated competitor review history when the platform does not provide it to you.
AI cannot tell whether differences reflect service quality, customer mix, location, review volume or selective reviewing without further evidence.
AI cannot decide which complaints matter most to your business or whether a proposed response is commercially sensible.
AI can make a theme look established by grouping different complaints together, so the underlying reviews and counts still need checking.
AI cannot turn review comparisons into reliable market research when the samples are small or collected by different methods.

What caps this at PARTLY: private data access, real time truth and judgement under ambiguity.

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.

Axis	Score (0–2)
Output	2
Inputs	1
Verification	1
Liability	2
Effort delta	1
Total	7 / 10

The methodology and its thresholds are published in full.

FAQ

Can AI analyse my Google reviews?: Yes, if you supply the review text and relevant metadata. It can group themes, compare ratings and summarise recurring praise or complaints, but it cannot guarantee that your dataset is complete.
Can AI see my competitors' Google reviews?: Not reliably without data you provide or an appropriate permitted data connection. Give it public review text, dates, ratings and source URLs, then treat the result as a comparison of that sample rather than the whole market.
What should I compare in Google reviews?: Compare recurring praise, complaints, service attributes, response times, product or location issues and the evidence behind each theme. Keep the date range, review volume and data collection method consistent across businesses.
Can AI tell me what to improve from customer reviews?: It can suggest priorities by linking repeated complaints to possible actions. You still need to check the original reviews and compare the suggestions with your operational data before changing your offer or service.

Nearby answers

All Competitor analysis rulings

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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