Review Intelligence reads the actual text of your reviews and groups it into themes, sentiment and recurring complaints, with the source reviews attached so you can check any claim against what was really written.
The pattern is in there. Six people mentioned the wait on Saturdays, four mentioned parking, and one specific staff member is named in every five-star review you have. But finding that means reading two hundred paragraphs and keeping a tally, so it never happens and the pattern stays invisible.
A star average tells you the temperature, not the cause.
The same complaint phrased six different ways reads as six unrelated problems.
Praise worth repeating in your marketing is buried in the middle of the list.
By the time a trend is obvious from the average, it has been running for months.
What Dropwebs does: Review Intelligence summarizes your synced reviews: counts, rating distribution, unanswered, response rate, themes, and recurring issues.
Themes
Recurring topics are pulled from the review text and grouped, with the underlying reviews attached to each theme. When Dropwebs tells you wait times are a recurring complaint, you can open the six reviews that made that true and read them yourself.
Sentiment
Positive themes and recurring complaints are listed apart, because they lead to different work. The praise column tells you what to protect and what to put in your marketing. The complaint column is your operational to-do list.
Distribution
A 4.3 average made of forty fives and ten ones is a very different business from one made entirely of fours. Rating distribution, review velocity and the unanswered count are computed in application code from your stored reviews, never estimated.
Sync reviews for the location.
Open Intelligence to see calculated metrics.
Read themes grounded in review text when enough reviews exist.
Jump to Review Manager for unanswered items.
Six mentions of one problem beats the loudest single review.
Every theme opens onto the reviews behind it.
A rising complaint shows up before it moves the average.
Positive themes are the words your own customers chose.
Sync reviews first. Without reviews, there is nothing to analyze.
Check rating distribution and unanswered count.
If themes appear, open source reviews to verify.
Use findings to change operations, then reply via Review Manager.
Themes are AI-derived from your review text and are labelled as such. The counts and distributions beside them are computed in code.
Reviews with no written comment contribute a rating but no theme.
A small review set produces weak themes. Dropwebs flags insufficient data rather than inventing a pattern.
What it does, what it will not do, and where the data comes from.
We need enough text-bearing reviews. Short or empty comments are not enough for reliable themes.
No. Counts and rates are PHP calculations. AI only helps with language and themes from real text.
No. Themes are derived from the text of your own reviews, and each one links back to the reviews that produced it. If the sample is too small to support a theme, the tool says so instead of guessing.
No. Review text is sent to the configured AI provider to produce your analysis and is not used to train anything. Numbers on the page are calculated by Dropwebs, not by the model.
Free plan, no card. Connect Google and this tool has real data to work with in about a minute.
Workspace steps and real screens. This is a how-to, not a second overview of the tool.
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