Most guides on review monitoring tell you to "use a tool" and call it a day. That's not helpful.
Review monitoring is infrastructure. It's not a feature you turn on. It's a system you build. The goal: get the right information to the right people at the right time, without creating noise.
This guide covers the full picture. Platform identification. Monitoring infrastructure. Alerting systems. Channel routing. Analysis.
For each step, we'll show you how to do it yourself with no-code tools. And how ReviewFlowz makes it faster.
Let's get into it.
1. Platform Identification: Know Your Review Landscape
Before you monitor anything, you need to know what you're monitoring.
Run an audit
Start by listing every platform where customers can leave reviews about your business.
There are a few categories:
- General platforms: Google, Facebook, Yelp
- Industry-specific: Tripadvisor, G2, Capterra, Trustpilot, Booking.com, Hotels.com
- Marketplaces: Amazon, App Store, Google Play
- Niche/vertical: Houzz, Angi, Avvo, Healthgrades
Your list depends on your industry. A SaaS company cares about G2 and Capterra. A restaurant cares about Google and Tripadvisor. A hotel cares about Booking.com and Expedia.
If you're not sure which platforms matter, check your competitors. Look at their websites for review badges. Search their brand name and see which review sites rank.
Quantify your exposure
Next question: how many reviews do you get per month, per platform?
This matters. A business getting 500 Google reviews per month has different needs than one getting 5. High-volume platforms need automated systems. Low-volume ones can be handled manually.
Calculate your total monthly review volume. Break it down by platform. This gives you a baseline.
Identify gaps
Look for platforms where you should be present but aren't. Maybe you haven't claimed your Google Business Profile. Maybe competitors are getting reviews on a niche site you've ignored.
Be extremely careful with claiming profiles. You might not have a choice, but know this. There are a million review platforms out there.
The vast majority get virtually zero traffic, and have no chance at getting any traffic on your brand name unless you start giving them unique reviews.
They'll often have sales teams reaching out, and telling you how big they are, and how important their buyer community is. Just to get you to post a few reviews.
It might sound harmless enough, but before you know it, they'll rank on your brand name. And you'll end up having to work with them for years.
If you've never heard of them, there's a good chance your customers haven't either.
And look i get it. A backlink's a backlink.
But every platform you're listed on is a liability before it becomes an asset. And it can only be an asset if you're in control.
DIY approach
As much as you might read otherwise, you don't need anything complicated here.
Create a spreadsheet with the following columns: Platform, URL, Claimed (Y/N), Reviews Last Month, Average Rating, Priority.
Go through each platform manually. Count reviews. Note your ratings. Update monthly.
It's tedious, but you'll be done in 10minutes.
Literally.
With ReviewFlowz
You can search for your brand on each platform, and Reviewflowz will pull review counts and ratings automatically.
You get your full review landscape in one dashboard — volume per platform, rating trends, gaps in coverage.
Note that you can also run this for competitor brands, and create a dedicated report to compare your presence, rank, rating, and review velocity on each platform.
While we charge per review profile (each platform X brand is a review profile) you can add or remove profiles at any point during your subscription, and your cost will be pro-rated. This means if you run an audit for 3 - 4 days, you'll only pay about 10% of a profile for each profile you connect.
It's definitely not as cheap as running the audit over a spreadsheet yourself, but if you're doing this at scale, it really makes sense to spend a couple hundred dollars.
2. Building Your Monitoring Infrastructure
Most guides just say "use a tool" without explaining what the tool actually needs to do, or the fact that you can do it yourself.
What monitoring actually means
It's not just about new reviews. A complete monitoring system tracks:
- New reviews
- Updates and edits
- Owner replies
- Review deletions
Most people only think about new reviews. But you're not getting a full picture without updates and deletions.
If you get 20 "new reviews" on Google, but all of them are updates of past reviews, and the score is unchanged, your rating will not be impacted – at all.
Similarly, if you manage to change a 1-star review into a 5-star review, it is worth much more than collecting a new 5 star review (and keeping that 1 starreview)
If a couple of reviews get removed out of every 10 reviews you collect, you're effectively losing out on 20% of your review acquisition. Which is a massive drain.
A customer might edit a 1-star review to a 5-star after you resolve their issue. That's valuable information.
DIY approach
Option 1: Manual checks
Check each platform once a week. Copy reviews into a spreadsheet.
This works if you get fewer than 20 reviews per month, on one or two platforms.
Beyond that, it kind of falls apart.
Option 2: Email notifications
Most platforms send email alerts for new reviews.
Create a Gmail filter.
Route all review notifications to a dedicated folder or label.
You can even build a zap on top of that filter to log the contents of each incoming email into a spreadsheet
The main challenge here is you'll need to parse emails, which nobody likes doing. AI can probably help, but it can also hallucinate.
In general though, if you're getting reviews on 2 - 3 platforms and their alert system is reliable, this is a good middle-ground.
Option 3: No-code automation
Some platforms have native API integrations.
For example, Google Business Profile has a Zapier trigger for new reviews, which means you can start a zap every time you receive a new review.
When a review comes in, send it to a Google Sheet.
You can build a basic system this way. New reviews land in a spreadsheet automatically.
The main problems you'll face here is that not all platforms have this kind of API support. Also, like all things technical, the more you dig, the more challenges you'll discover, such as understanding how each platform deals with updates, review removal, language detection, etc.
Not to mention we've entered the world of paying services in the first place, and most APIs will have a minimum monthly amount. So it might actually end up being more expensive than using proper review monitoring & aggregating services.
Option 4: Build scrapers
Final DIY option – and certainly NOT the first !
If you have technical resources, you can scrape platforms without APIs.
Pull review data on a schedule. Store it in a database or spreadsheet.
Now this can be reasonably simple for a number of sites, especially with AI helping out to write the scrapers & wrappers etc.
But here's the harsh truth: Anyone can scrape a few thousand reviews once. The hard part is building something that lasts.
Before the first month, one of your scrapers will break. The platform update their HTML or API schema, your proxy gets flagged, ... there are a million variables, and each one of them can break your entire process.
It takes a fair amount of experience and trial & error to build something that is reliable, with solid error monitoring etc.
Not to mention you'll still have to make different review data schemas match, detect languages, parse different data structures, formats, etc.
A seemingly very small & simple project can quickly become a nightmare.
Long story short, scraping is fun, but scraping reviews frequently, and across several platforms is a full time job.
With ReviewFlowz
Connect your platforms, we'll get all your past reviews and pick up new ones within 1 - 2h.
You get clean data, with a single, clear data schema, that works for every platform.
We detect languages, and you can one-click translate your reviews into any language using AI.
We track new reviews, updates, deletions, replies, you get a full picture.
If you need to monitor reviews at high scale over API, you can get up and running in a matter of weeks.
If you need to monitor anywhere under 1000 review profiles, you can be up and running before the end of the week.
3. Building an Alerting System That Works
Knowing what data you need is step one.
Collecting data is step two.
Getting it to the right people is step three.
Who needs to know?
Not everyone needs every alert. Map your stakeholders:
- Customer service: Needs all reviews, especially negative ones, to respond.
- Location managers: Need reviews and / or aggregated reports for their specific location
- Marketing: Wants detail and metadata-rich positive reviews for social proof, testimonials. They also want a segment of identified happy customers so they can ask for case studies, etc.
- Operations: Needs advanced reporting capabilities, AI topic extraction, sentiment analysis, etc. to see patterns in complaints
- Leadership: Typically wants high-level reports
Different people need different information at different frequencies.
A note here, it has been incredible to see the impact of making reviews accessible to everyone in an organization. A simple feed of customer reviews on a public Slack channel is by far the most transformative action I've seen among our clients. Customer reviews just become part of everyday conversations. Marketing teams use them more, Sales teams use them more, Ops teams ask more often, product teams talk to clients more. Just like that.
What triggers an alert?
There are a few things to consider when routing reviews.
You'll want to avoid "alert fatigue" so it might be worth filtering the reviews you're sending to specific channels.
For example, we group review alerts into single emails we send every few hours rather than sending an email for every new review.
Then, you might want to filter where you send reviews based on their rating, or based on the source (platform), or based on some keywords, or based on the language, etc.
You might also want to translate reviews on the fly, or to make translations available easily.
Forcing people to copy paste into google translate just means they won't read them.
DIY approach
Email forwarding
Set up platform email notifications. Use Gmail filters to sort by sender (Google, Tripadvisor, etc.).
Forward specific alerts to specific people.
Crude, but functional.
With smart filters, you can even throw in keyword filtering or star rating functional.
You can make that even smarter with Zapier or Make.com on top.
Google Sheets + Apps Script
If your reviews land in a Google Sheet, you can write Apps Script to send alerts.
Check the sheet every hour. If new row + rating < 3, send email to support team.
You can ask chat GPT or Claude for help to set this up. Google scripts can even send reviews to your Zendesk instance or that sort of thing with more advanced integrations.
Whatever you do, and this is super important, make your notifications beautiful and easy to read. It takes many iterations, especially if you're tracking several platforms, but it's absolutely key.
Believe it or not, this is how I set up review monitoring for Brevo. It was a much smaller company back then but my crazy spreadsheet supported 12 review platforms, and a solid 100 new reviews a month.
They're happy customers of Reviewflowz now though 😇
With ReviewFlowz
Reviewflowz integrates with every channel there is – Slack, Teams, Zendesk, Intercom, Google Sheets, Power BI, you name it.
The notifications engine is the first thing I built, and it's still very much at the core of the product.
You can set different filters based on ratings, platform, brand, review content, etc.
It's super easy to set up and we have tons of clients with 15 - 20 different automation flows so you can build pretty advanced systems if you need to.
We also allow you to build custom reports, custom dashboards, and custom filters.
You can either access those in-app – and invite anyone who needs access – or send them over email.
4. Channel Routing: Matching Alerts to Use Cases
Once you know what to send, to whom, then comes the question of where.
It's fairly reasonable to create a new spreadsheet and ask people to access it if they need information about reviews.
But it's definitely not reasonable to ask them to log into a new system to access information about reviews.
I mean you can do that, but they won't do it.
So it's important to bring reviews into the tools your teams use.
Chat GPT, Claude, etc
Use case: all of them!
This is a special Reviewflowz thing so I had to put it first as a good marketer.
Hear me out though.
You've probably already tried to upload a half broken CSV into Claude or Chat GPT.
Whether that worked for you or not, you can probably see the value of doing that.
Now imagine doing it right.
Imagine if Chat GPT could search your reviews like it searches google
That's what Reviewflowz MCP does.
Helpdesk (Zendesk, Freshdesk, Intercom)
Use case: Operational response management.
Whether reviews require responses or not, route them to your helpdesk. Treat them as tickets, or cases.
Every review deserves attention.
Whether it's a personal note on the CRM, a public response, or a full-on investigation to figure out what happened.
A helpdesk integration gives you:
- Assignment (who's handling this?)
- Status tracking (open, pending, resolved)
- SLAs (response time targets)
- Templates (consistent replies)
- History (what happened with this customer before?)
If you're serious about responding to reviews, helpdesk integration is the right approach.
Instant messaging (Slack, Teams)
Use case: Team awareness. Internal marketing. Morale.
Slack is great for visibility. Post positive reviews to a #wins channel. The team sees them. It builds morale.
At reasonably small scale, you can even use Slack or Teams for your operations.
Each review is a message on a dedicated channel. You can assign them to someone in-thread. Replies get automatically posted in Slack in-thread (or you copy paste them if you're DIY)
At scale, Slack is not great for operations. Messages get buried. There's no tracking. You can't assign a review to someone and follow up. But for small teams, this works wonders.
Use case: Periodic summaries. Low-urgency stakeholders.
Email works for weekly digests. Leadership doesn't need real-time alerts for every review. They need a summary.
Data visualization / BI tools
Use case: Analysis. Trends. Reporting.
Pipe your review data into Looker, Tableau, Power BI, or Google Data Studio. Build dashboards. Track ratings over time.
This is for strategic decisions, not daily operations.
Widgets de site web
Use case: Social proof. Conversion optimization.
Different requirements. Speed matters less. Quality matters more.
You need full metadata: reviewer name, profile picture, star rating, date, platform logo. Curation matters — you're selecting your best reviews to display.
5. Aggregation and Analysis
Monitoring tells you what was said. Analysis tells you what it means.
Topic extraction
What are customers actually talking about?
Identify themes: product features, service issues, staff mentions, pricing, wait times. Spot emerging topics before they become patterns.
Sentiment analysis
Star ratings are blunt. A 3-star review can be positive or negative depending on the text.
Sentiment analysis reads the tone. It catches frustration in a 4-star review. It identifies urgency.
Trend analysis
Track ratings over time. Look for patterns.
- Did that new policy improve sentiment?
- Is there a seasonal pattern?
- Which location is trending down?
Competitive benchmarking
How does your review profile compare to competitors? Where are you winning? Where are you losing?
DIY approach
Do not dump unstructured reviews into chat GPT and expect any degree of relevance.
It's like going to a fortune teller. They'll just tell you things that could be true of any business. Things you can't use.
Topic extraction
Export reviews to a spreadsheet.
You can buy a structured export from us if it helps (one-of purchase, $0.10 per review).
You can also use your own monitoring spreadsheet if you have one, or build scrapers or whatever.
Again, building a one-off list is considerably easier.
Create a custom GPT or ClaudeProject – or whatever the equivalent is on other platforms.
This part is important. It'll cost you $20. But it's important, you need the memory.
Then, ask the AI to go through each review in your corpus (attached CSV to the custom GPT) and extract topics.
Ask it to groom a list of 15 topics that represent everything your reviews touch on, and that are MECE – Mutually Exclusive, Collective Exhaustive.
You'll get a list of 15 topics.
Sentiment analysis
Then, ask it to process each review in your CSV by looking for "extracts" that speak to one of your key topics.
Each time it finds an extract, tell it to evaluate the sentiment of that extract between -1 and 1.
An extract can only speak to one topic.
If the sentiment score is too close to 0 (neutral), drop the extract.
Ask for a table of review extracts with extract, source review, topic, and sentiment score
Trend analysis
Now you have something you can work with.
With ReviewFlowz
We basically do all of that, with slightly more advanced tools. AI chat tools like chat GPT are great because they can do many different things;
But there are better models and techniques for topic extraction, for topic detection, for sentiment scoring, etc.
That's the main difference really. The devil is in the details!
I've heard great things about this DIY process though, so don't discount it!
Conclusion
Review monitoring is not a feature. It's infrastructure.
You can build it yourself. Spreadsheets, Zapier, Apps Script, manual processes. It works — up to a point. Then the maintenance becomes a job.
Or you can use ReviewFlowz. Connect your platforms once. Get monitoring, alerting, routing, and analysis out of the box. Spend your time acting on reviews, not collecting them.
Start with the audit. Know which platforms matter.
Build the monitoring — DIY or with software.
Design the alerting. Right information, right people, right channel.
Route by use case. Slack for awareness. Helpdesk for operations. BI for analysis. Widgets for social proof.
Layer on analysis. Make the data actionable.
Get this right, and reviews become a strategic asset. Get it wrong, and you're just collecting data nobody uses.
