Find out what customers keep telling you — and act on it
Your reviews already contain the answer to what is working and what is quietly costing you business. flyclicks.io reads all of them, scores the sentiment, groups the recurring themes, and lets you ask questions about your own data in plain language.
What usually gets in the way
The goal is simple to state and stubborn to reach. These are the three things that stall it most often.
Nobody reads three years of reviews
The signal is in the volume, and the volume is exactly what makes it unreadable. Most businesses only ever see the newest page.
The loudest review wins
One furious review reshapes a whole meeting, while the pattern mentioned quietly in forty others never gets raised at all.
You cannot see change over time
Was the wait time complaint always there, or did it start when the new shift pattern did? Without a trend line, it is an argument rather than a fact.
From raw reviews to something you can decide on
Every review is read, scored and grouped — then handed to you as a pattern, not a pile.
Reviews sync
Your full review history is pulled in from your Google Business Profile, not just the recent page.
Sentiment scored
Each review is classified positive, neutral or negative — independently of the star rating.
Themes extracted
Repeated subjects — waiting, parking, a named staff member, a specific dish — are pulled out and counted.
Trends and heatmap
Themes plotted over time, plus a heatmap of when reviews actually arrive.
Ask the AI consultant
Question your own data in plain language and get an answer grounded in your reviews.
What the insight dashboard gives you
Evidence you can take into a staff meeting, not a word cloud.
Sentiment analysis
Every review scored on what it actually says, which regularly disagrees with the stars — a four-star review can still be a complaint.
Top complaints
The negative themes ranked by how often they come up, so you fix the one costing you the most rather than the one shouted loudest.
Top compliments
What customers reliably praise — the thing your marketing should be leading with and your training should be protecting.
Theme detection
Recurring subjects surfaced automatically, including the ones nobody thought to set up a category for.
Trends over time
Watch a theme rise or fall by month so you can connect it to what changed in the business.
Review volume heatmap
When reviews actually arrive, by day and hour — useful for staffing, and for timing when you ask.
Per-location breakdown
The same sentiment and theme analysis split by branch, so a single site's problem does not hide inside the average.
AI consultant
Ask what changed last quarter, or what your worst-rated location has in common, and get an answer drawn from your own reviews.
What the analysis panel looks like
Volume, sentiment split and the themes sitting underneath it.
- Reviews analysed
- 2,417
- Positive sentiment
- 82%
- Negative sentiment
- 9%
- Themes detected
- 26
Sample figures shown to illustrate the panel layout. They are not customer results and not a benchmark to compare yourself against — your own split depends entirely on your business and your review history.
Before you decide
The things people ask us most about this, answered plainly.
How far back does the analysis go?
Across the review history we can retrieve from your Google Business Profile, not just the most recent reviews. The older material is usually where the long-running patterns become obvious.
Is sentiment just the star rating rewritten?
No. Sentiment is scored from the text itself, which is the point — a four-star review that spends two sentences on a bad wait is negative feedback, and a rating-only view would never catch it.
Do I have to set up the themes myself?
No. Themes are detected from what customers actually write, which means you find the subjects you did not know to look for. You can then follow any of them over time.
Does it show me how competitors are doing?
No. Competitor benchmarking is not part of the product — it needs a licensed third-party data source we have not brought on. Everything here is analysis of your own reviews.
The answers are already in your reviews.
Read every one of them at once, see the patterns that repeat, and ask your own data the questions you would normally guess at.