Trustpilot reviews to Power BI

Reviewflowz publishes an OData v4 feed and Power BI’s built-in OData connector reads it. Paste one URL and your Trustpilot reviews arrive as an ordinary table, on the same page as the order volume they explain. There is nothing to install.

  • Set up in under five minutes
  • No demo required
Trustpilot reviews Power BI
  1. 1 Get data Home ribbon
  2. 2 OData feed Built in, already there
  3. 3 Paste The URL below
Feed URL One per account

https://app.reviewflowz.com /api/odata/v1/accounts/4821/

User name
bi_9f4c2ad7e10b
Password
left blank

Nothing to install. OData is a format Power BI has read for years, so its built-in connector does the work.

They love how easy we've made it

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    arctic

    Tour operator in Iceland

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    Luggage Storage & Lockers

  • 5000 AI replies generated

    supersushi

    Sushi Restaurant Chain

Paste one URL, get six typed tables

Reviewflowz publishes an OData v4 feed. Paste its URL into Power BI’s built-in OData connector and the navigator lists six tables: Reviews, ReviewProfiles, Locations, RatingsHistory, ReviewSummary and ReviewChanges. Reviews is one typed row per Trustpilot review, with Rating as a number and ReviewDate as a real datetimeoffset. RatingsHistory and ReviewSummary hand you the rating over time already computed.

Trustpilot reviews Navigator

Display Options

Six tables from the Reviewflowz OData feed. Tick what you need and load.

Reviews 48,200 rows
  • Rating Edm.Double
  • ReviewDate Edm.DateTimeOffset
  • Title Edm.String
  • Overall Edm.String
  • LocationName Edm.String
  • Language Edm.String
  • Tags Edm.String
  • ReviewProfiles The profiles you connected.
  • Locations So a review joins to a site.
  • RatingsHistory The rating over time, computed.
  • ReviewSummary Counts and averages per period.
  • ReviewChanges Inserts, updates and deletes.

The names are stable, because a saved report binds to them.

Reviews beside the number they explain

Trustpilot volume tracks order volume, so the raw count is not the metric. One-stars per thousand orders is, and with both tables in the model it is a DIVIDE. The retailer in the visual runs about 8,400 orders and 310 reviews a day. On 2 September the ratio went from 0.9 to 3.4 while the count looked like an ordinary busy day.

Trustpilot reviews 48,200 rows Power BI

Orders and Trustpilot reviews, daily

Invitations go out against orders, so the two series move together.

24 Aug 2 Sep 6 Sep
Orders 8,400/day Trustpilot reviews 310/day

One-stars per 1,000 orders

3.4 against 0.9 normal

A busy week raises the count of one-stars. It does not raise the ratio. That is what makes this the alarm, and it is one measure: a count divided by orders.

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Your review history imports the moment you connect, so there is something to reply to from the first minute.

A deletion is a row, not a silence

Trustpilot reviews get edited, flagged and taken down. ReviewChanges carries inserts, updates and deletes as timestamped rows, so a one-star raised to four after you fixed the delivery, and a one-star removed on appeal, both reach the model as events. The feed filters on ReviewDate, so after the first load brings your history, incremental refresh pulls only the window.

Trustpilot reviews Power BI

ReviewChanges, every edit and takedown as a row

  • Insert 2 Sep 09:14 1 star “Delivery window came and went twice.”
  • Insert 2 Sep 11:02 2 star “Third missed slot from the same courier.”
  • Update 4 Sep 16:40 1 → 4 star “Delivery window came and went twice.” Reviewer edited after the fix
  • Delete 5 Sep 08:27 1 star “Courier left it with the wrong building.” Removed by Trustpilot on appeal

Incremental refresh

$filter=ReviewDate ge {RangeStart} and ReviewDate le {RangeEnd}

ReviewDate is a real Edm.DateTimeOffset, so Power Query folds the window straight into the request.

It joins to what you already model

Relate Reviews to your date table and region dimension and Trustpilot volume sits beside churn, tickets and revenue on one page. Your row level security applies, so a regional manager sees their own region’s reviews. The connection is one token you create and revoke inside Reviewflowz: when the agency building your dashboard finishes, revoke it and their report stops refreshing.

Model view Your workspace
Reviews 48,200 rows
  • ReviewDate
  • Rating
  • LocationName
  • Language
  • Tags
  • Title

Loaded from the Reviewflowz OData feed

Date yours
  • Date
  • Month
  • Week

on ReviewDate

Orders yours
  • OrderDate
  • Region
  • Revenue

on Region

Tickets yours
  • CreatedAt
  • Region
  • Queue

on Region

Security
Your RLS roles, unchanged
Connection
One token, revocable
New tables
One. The rest were already here

FAQs

How do Trustpilot reviews actually get into Power BI?

Through an OData v4 feed that Reviewflowz publishes. You create a connection in Reviewflowz and copy its feed URL, then in Power BI choose Get data, OData feed, and paste it. Authenticate with the token as the user name and a blank password. The navigator lists the tables and you load the ones you want. There is nothing to install: OData is a format Power BI has read for years, so its built-in connector does the work.

Which tables and columns arrive?

Six: Reviews, ReviewProfiles, Locations, RatingsHistory, ReviewSummary and ReviewChanges. Reviews is one row per Trustpilot review with the rating, the review date, the title, the full text, the reviewer name, the location, the language, the tags and the link. RatingsHistory and ReviewSummary give you the rating over time without computing it yourself, and ReviewChanges is the stream of inserts, updates and deletes.

Does the report refresh on its own?

Yes. Power BI’s scheduled refresh pulls from the feed on whatever cadence you set. Incremental refresh works too, because the review date is a datetimeoffset the feed can filter on, which is exactly what Power Query folds a RangeStart and RangeEnd window into. The first load brings your Trustpilot history, then the dataset pulls the window.

What happens when a Trustpilot review is removed?

It arrives as a delete row in ReviewChanges, with a timestamp. That is the difference between a change stream and a snapshot: a review that gets flagged and taken down is an event in your model rather than a row that silently stops appearing. Edits come through the same way, so a one-star raised to four after you fixed the problem is visible as a change.

Can I put Trustpilot review volume next to our order volume?

Yes, and it is the reason to do this at all. The reviews land as a table in your own semantic model, so you relate them to your date table and your region dimension like anything else. One-stars per thousand orders is then a DIVIDE across two tables you already have. Reviewflowz does not supply the orders: they are yours, already in the model.

Does our row level security still work?

Yes. The model is yours, so your existing RLS roles apply to the review tables the same way they apply to everything else. Separately, the feed token carries the access of the Reviewflowz user who created it, so someone restricted to a subset of profiles cannot mint a token that exposes the rest.

Which plan includes the BI feed?

The BI feed is a Premium feature and it is not part of the free trial, so budget for that rather than discovering it on day three. Everything else works the way the trial suggests: you connect your Trustpilot profile yourself and see your reviews in minutes.

Do I need a demo to get started?

No. You connect your Trustpilot profile and create the BI connection yourself, in under five minutes. If you get stuck, support is the people who build it.

Put Trustpilot reviews on the page the business already reads

Set up in under five minutes. No demo required.