Use case · Growth · Retention
Feedback & Review Collection
Feedback and review collection asks every customer how it went and invites every customer to leave a review, automatically, after each job, visit, purchase or stay. Low scores alert you privately so you can put things right, and customer and employee survey answers become clear reports with recommendations.
- Asked, and invited to review
- Every customer
- Rating plus an optional comment
- 1–5 stars
- On every low score
- Private alert
What is automated feedback and review collection?
Automated feedback and review collection is a system that messages every customer after a job, visit, purchase or stay, asks for a 1–5 star rating and a comment, and invites them to leave a review on Google or your booking platform. Low ratings alert the owner privately, and AI turns the results into a regular report.
It means you hear from every customer, not only the ones who complain in public.
Why do most customers never leave a review?
Most customers never leave a review because nobody asks them. Asking after every job, visit or stay takes time nobody has, so it happens now and then or not at all. The few reviews that appear don't reflect the business, and feedback that does arrive sits unread in a form tool or spreadsheet.
Customers are never asked
Feedback requests go out when someone remembers, so most customers never get one.Problems surface in public
You hear about a bad experience for the first time in a one-star review.Feedback sits unread
Survey answers and comments are collected, then nobody has time to analyse them, and the same issues come up next time.
How do automated review requests work?
Automated review requests start when a job, visit, purchase or stay is marked complete. Every customer gets a short email or SMS asking how it went, leaves a 1–5 star rating and a comment, and is invited to review you with a direct link. A low rating also alerts the owner privately, and AI summarises results into a report.
With AI automations
A completed job, visit, purchase or stay
Ask at the right moment
After a check-out, a finished job, a visit or a purchase, every customer gets a short message by email or SMS asking how it went.Collect a rating and comments
Customers leave a 1–5 star rating and, if they want, a comment.Invite a review
Every customer is invited to review you where it matters, such as Google or the booking platform, with a direct link. Nobody is filtered out because of their score.Alert on low scores
A low rating alerts the owner or manager privately straight away, so they can follow up personally.Report
Ratings and comments are collected in a dashboard, and AI summarises satisfaction and recurring themes into a regular report with suggestions on what to improve.
With an AI chat agent
The end of a chat conversation
Ask in the conversation
At the end of a chat, the agent asks for a quick rating and any comments.Same dashboard
The answers go to the same dashboard and reports.Guests after check-out
For short-term rentals and hotels, a guest agent asks for a star rating after check-out and invites every guest to review their stay on the booking platform. Low scores alert the host privately.
Can AI analyse customer and employee surveys?
Yes. When a survey round closes, AI analyses the responses from your survey tool or form, measures overall satisfaction and groups comments into recurring themes and areas for improvement. An automated report then gives management practical recommendations, such as how to improve employee motivation or what customers want changed.
Collect
Responses come in from your survey tool or form.Analyse
AI measures overall satisfaction and groups the comments into themes and areas for improvement.Report with recommendations
An automated report summarises the results and gives management practical next steps.
For employee surveys, we agree with you how anonymity is handled before the first round.
Which systems deliver it?
Feedback and review collection is delivered by AI automations, which send requests, collect ratings, invite reviews, alert on low scores and write reports, and by an AI chat agent, which can collect a rating at the end of a conversation. Both feed the same dashboard, so every rating and comment is in one place.
| System | Trigger | What it does | Package note |
|---|---|---|---|
| AI Automations | A completed job, visit, purchase or stay | Asks for a rating, invites a review, alerts on low scores, reports themes | Email and SMS |
| AI Automations | A survey round closes | Measures satisfaction, groups themes, writes a report with recommendations | Works with your survey tool or form |
| AI Chat Agents | The end of a chat | Asks for a rating and comments in the conversation; guest reviews after check-out | Added to an existing chat agent |
Where is it used?
Feedback and review collection suits any business that finishes jobs, visits or stays and depends on its reputation. Hospitality calls it guest reviews, garages and trades call it job feedback, clinics call it patient feedback, online shops call it post-purchase reviews, and HR teams use the survey side for staff satisfaction.
Same job, their words
Guest reviews. After check-out, every guest is asked for a star rating and invited to review their stay on the booking platform, and a low score alerts the host before it becomes a public review.
What should you know before you start?
Feedback and review collection asks every customer, whatever their score. We don't build review gating, where only happy customers are sent to review sites, because Google's review policies don't allow it. The private alert on a low score runs alongside the review invitation, not instead of it. Consent and opt-outs apply.
- Every customer is asked. No review gating. The low-score alert runs alongside the review invitation.
- Consent and opt-outs apply. Messages follow your contact rules, and opt-outs are honoured automatically.
- Anonymity is agreed up front. For employee surveys, reports show scores and themes, and individual responses stay as private as your survey promises.
How would we build it for you?
We haven't published a feedback and review collection case study yet. Here's how a build would run: we pick the moment that triggers each request, write the messages with you, link your Google or booking-platform review page, set who gets low-score alerts, and test the flow end to end before it goes live.
- Job completedtrigger
- Rating requestedemail · SMS
- Review invitedevery customer
- Low scoreprivate alert
- Themes reportedmonthly
Frequently asked questions
Questions
Answer
Is it allowed to only ask happy customers for reviews?
No. Sending only happy customers to review sites is called review gating, and Google's review policies don't allow it. We invite every customer to leave a review, whatever score they gave. A low score also alerts you privately, so you can follow up and put things right, but it never stops the review invitation going out.
When should you ask for a review?
Soon after the experience, while it's fresh: after a check-out, a finished job, a visit or a delivery. The trigger is the moment your system marks the job, stay or purchase complete, so the request goes out at the right time for every customer without anyone remembering to send it.
Where can customers leave their review?
Wherever it matters to you, such as your Google Business Profile or the booking platform your guests use. Each message includes a direct link, so leaving a review takes a few taps. Genuine reviews then support your local search and AI answers through search and AI visibility.
What happens when someone leaves a low score?
The owner or manager gets a private alert straight away, with the rating and comment, so they can contact the customer personally and put things right. The customer is still invited to leave a review like everyone else. The low scores also feed the report, so recurring problems are visible.
Can it analyse staff satisfaction surveys?
Yes. When a survey round closes, AI measures overall satisfaction, groups the comments into themes and writes a report with practical recommendations for management. We agree how anonymity is handled before the first round, and reports show scores and themes rather than individual answers.