CASE STUDY
Clinic group review monitoring system
Google reviews from every branch are monitored in one back office. New reviews are stored as they arrive, low ratings trigger an alert, and the reply status of each one is tracked. Marketing and clinic staff see the whole picture without opening each business listing.
RESULTS
What this project changed
- 11,000+
- Reviews monitored
- Real time
- Negative review alerts
The problem
In a clinic group, reputation is built by every branch together: one unanswered bad review anywhere reflects on the whole brand. Before the system, keeping track meant marketing staff opening each branch's Google Business listing by hand every day.
- With many branches, manual checking was never current, and bad reviews were often found days later.
- Nothing was recorded centrally, so which reviews had been answered was tracked by memory and ad hoc spreadsheets.
- Reviews accumulated across separate listings, making it hard to look back and find recurring complaints.
What we did
We built central review monitoring across branches so that finding and handling a review both happen in one place.
- Google reviews from every branch are collected on a schedule, stored together and tagged with branch, rating and time.
- Low ratings trigger an alert, so the person responsible knows immediately what needs attention.
- A reply can be written in the system and posted back to the platform. Every review carries a reply status, can be assigned to an owner and tracked through to closure.
- The back office reports by branch, rating and date range, so management sees the trend rather than single reviews.
Results
Reputation work changed from occasional checking into daily work with a defined process.
- More than 11,000 reviews have been monitored, with data from every branch in one back office.
- Bad reviews raise an alert immediately, so they surface when the notice arrives rather than days later.
- Reply status is tracked, which prevents reviews being missed or answered twice.
CONSTRAINTS
Constraints
The conditions that were fixed from the start. The trade-offs behind the approach only make sense once you know them.
Reviews are not our own data. How they can be obtained and how often they update are set by the source platform, and that defines what the system can do.
- Review content comes from the platform. The system can read it, organize it and post a reply on the clinic's behalf, but it cannot edit or delete a review that already exists. A reply is public the moment it is sent, so every one is stored with who sent it and when.
- Data arrives through scheduled collection rather than per-review push, so the real delay behind the word instant depends on how often collection runs. That has to be stated plainly at rollout.
- Branches share one back office, but who can see what and who is responsible for replying must follow the group's existing division of duties.
- Reviews often contain patients' accounts of their treatment, so neither the screens nor the alerts may copy that content anywhere outside the group's control.
HOW IT WORKS
Technology and integrations
What the system connects to, how the data moves and why those choices were made — written so that no technical background is needed.
The approach is simple: collect reviews scattered across branch listings into one database on a schedule, then do everything else inside our own system.
- Each review is tagged with branch, rating and time as it is stored, so looking at trends or comparing branches never means paging through the platform.
- A low rating raises an alert as it arrives, so the owner does not have to watch a screen and only opens the ones that need attention.
- Reply status is held in our own data, so handled and unhandled items are visible in the back office at handover.