Turn your review history into campaign data.
Match your Google review history to real customer profiles, then use it as dated customer events across your marketing stack for segmentation, goals and campaigns.
People who have performed left_google_review at least once, ever
Every review is stored as an event dated to the day it was left, so a segment can reach the whole history rather than the weeks since the connection was made.
Your reviews sit outside your marketing stack.
Reviews shape repeat revenue, retention and customer trust, but most of the data lives away from the campaigns that should act on it.
Marketing platforms need events, not static flags, to satisfy goals and “has performed event” segments. A field records a person’s current state. An event records that something happened on a date. We write review history back as dated customer activity, so your automations can respond to what happened, when it happened and who did it.
Tells you the current state of a person.
- Use in a goal
- Trigger a campaign
- Segment by date
Records that something happened, on the day it happened.
- Use in a goal
- Trigger a campaign
- Segment by date
Every review becomes usable customer activity.
Each day, the dashboard pulls Google reviews for every configured location, resolves each reviewer to a customer profile in your marketing platform, and writes two records: current profile attributes and a left_google_review event carrying the review’s original date. A review from 2014 becomes a 2014 event, so segments, goals and reporting can use your full review history.
01 Google
★★★★★
Dave M.
Great experience with the team
12 Mar 2019
02 Match
Dave M.
d.mcallister@…
Rule: last initial
03 Write
Profile attributes
Dated event
left_google_review
04 Your platform
- Segments
- Goals
- Reporting
Historic review events work where lifecycle automation needs them: segments, goals and reporting. That is the point of writing events, not just attributes.
Google gives you “Dave M.” Your platform needs a match.
Identity resolution is the hard part. Google publishes a display name, while your marketing platform needs an identifier before it records activity against a profile. We use a five-rule cascade across exact names, initials, surnames, shortened names, nicknames and accent expansion, so “Fürhauser” can resolve to “Fuerhauser”. Every match records the rule that found it.
The preview shows how many reviews link to customers and highlights match types for confirmation before data writes to your marketing platform.
Match preview
34% of reviews found a person
These are the ones that can land on the wrong person. You confirm them before anything is written.
This is a governed review data pipeline.
The valuable work is not moving a new review from one system to another. The value is historical import, identity matching, dated events, deduplication, retry logic and per-location reporting, all configured against the marketing stack your campaigns already use.
01
Original dates
Historic reviews arrive with their original dates intact.
02
Recorded matching
Display names resolve to customer profiles through a recorded matching rule.
03
No duplicates
Google review IDs prevent duplicate events from polluting campaigns and reports.
04
Automatic retry
Unmatched reviews retry on later runs as customer records change.
The pipeline gives your marketing stack a usable review history, matched to people, dated correctly and ready for campaigns.
Reviewing ends the request sequence.
When a customer leaves a review, your lifecycle platform receives the event and can stop future requests automatically. Everyone else keeps moving through the request logic you already run.
From there, review history behaves like campaign data: segment on it, trigger from it, hold goals against it and report on it beside the rest of the customer record.
Stop review requests the moment a customer reviews
Segment from years of review history, not connection date
Trigger thank-you journeys from the review event
Getting review history into your marketing stack, three ways.
Marketing platforms have no native Google reviews integration, so the question is how the data gets in, and what happens to it on the way.
- MxD Customer Review Dashboard
- Purpose built
- A Zapier or Make build
- A New Review trigger and a Create Event action
- Review management platforms
- None found in any vendor's published integration directory
- MxD Customer Review Dashboard
- Yes
- A Zapier or Make build
- Triggers fire on new reviews only
- Review management platforms
- Not published
- MxD Customer Review Dashboard
- Yes, dated to the day the review was left
- A Zapier or Make build
- No timestamp field on the Create Event action
- Review management platforms
- Not applicable
- MxD Customer Review Dashboard
- A five rule name cascade, with the matching rule recorded
- A Zapier or Make build
- Your platform needs an identifier Google does not supply
- Review management platforms
- Not applicable
- MxD Customer Review Dashboard
- On Google's own review ID
- A Zapier or Make build
- Shallow, per trigger
- Review management platforms
- Not applicable
- MxD Customer Review Dashboard
- Run in your own campaigns
- A Zapier or Make build
- You would build and maintain the logic
- Review management platforms
- Run in their platform
Access is scoped to the job.
Two scoped credentials, each doing one job, and nothing at all on the Google side.
Google: no access required, reviews are public
From connection to live campaigns.
- 01
Connect your platform
Credentials are validated before data processing begins.
- 02
Add locations
Each location is configured separately for reporting and control.
- 03
Import review history
Review count and date range appear before platform writes begin.
- 04
Review the match preview
Linked customers and surname-based matches are shown for confirmation.
Nothing is written until you confirm this
- 05
Choose the request rule
Start from a template and adjust the campaign logic.
- 06
Preview this week's audience
See names and counts before messages send.
- 07
Go live
Review requests ramp by location.
MxD sets this up and operates it against the marketing stack you already run. Your team keeps working inside the tools they know.
Your reviews are already customer data.
Put that history where your lifecycle campaigns can use it. Match reviews to customers, write the events into your marketing platform and build the request logic around real behaviour.
Frequently asked questions
Which reviews does it read?
Google reviews for every location you configure. Each location is set up separately, so a group with twelve sites gets twelve streams of review data and reporting that holds them apart.
How far back does the history go?
As far back as your reviews do. The import runs through your full Google review history and each review keeps the date it was actually written, so a review from 2014 arrives dated 2014 rather than dated today.
How does a Google display name become a customer?
Google publishes a display name; your marketing platform needs an identifier. Five matching strategies close that gap, running from an exact first and last name through to last-initial and nickname forms, with accented names expanded so Fürhauser also matches Fuerhauser and Furhauser. Every match records which strategy produced it.
What happens before anything is written to our platform?
You see the match preview: how many reviews linked to a customer, and every match made on a surname alone. You confirm those before the first write. Nothing lands in your platform on a guess you have not seen.
What happens to reviews that do not match anyone?
They are held and retried on later runs at no extra cost, because the review data has already been fetched. Customer records change constantly, so a review that matches nobody in March often matches cleanly once that person appears in your platform.
Could we end up with duplicate events?
No. Every review carries a Google review ID and that ID is checked both across runs and within a single batch, then again per person and per review before an event is written. Re-running an import does not produce a second event.
Which marketing platform does it write into?
The one your campaigns already run in. The write path sits behind a three-operation adapter, so the destination is a matter of configuration rather than a rebuild, and review data lands in the same place as the rest of your customer record.
What access do you need from us?
Two scoped credentials for your marketing platform: one that writes review attributes and dated events, one that reads customer records so a review can find its person. Nothing on the Google side, because reviews are public. Credentials are stored per client and masked on read.
Can we correct a match that went to the wrong person?
Yes. A review can be unmatched from a person with a reason recorded, and the correction sticks: that review is marked corrected rather than returned to the pool, so no later run, retry or backfill quietly re-matches it to the same wrong person.
Do you read what the reviews actually say?
Yes. Each review is scored from strongly negative to strongly positive and tagged with up to four themes, each carrying its own polarity, so a review praising your staff and criticising your parking is recorded as both rather than averaged into one flat number. You can then find the reviews a single score would hide, such as the lukewarm five stars.
What happens when a customer asks to be deleted?
Deletion requests are honoured against the review record, and data ages out on a retention schedule rather than accumulating indefinitely.
How do we know it is still running?
A weekly digest covers what was imported and matched, errors alert immediately, and anomaly detection watches for the failure that matters most: a run that reports success while quietly writing nothing.
Can we get the data back out?
Yes. Reviews are queryable through a filtered, paginated API and exportable to CSV with access auditing, alongside per-location reporting. The review history is yours and it stays legible outside the dashboard.
Is it self-serve?
No. MxD configures and operates the pipeline for you, and the setup walk-through doubles as the demo: you watch your own review history match against your own customers before you commit to anything.