Shopify App Reviews Scraper (Stars, Text, Replies)

Get the reviews of any Shopify app (stars, date, text, merchant country, time using the app, developer reply) as JSON/CSV. $0.50 per 1,000 reviews.

Paste app URLs or handles and get one row per review, with the app's overall rating attached. Filter by star rating, sort by newest, and use a date cutoff so scheduled runs only pick up new reviews.

Quick start

1. Press Start. The form is already filled with this example input, so the first run returns real rows in under a minute:

{
  "apps": [
    "judgeme",
    "https://apps.shopify.com/klaviyo-email-marketing"
  ],
  "maxReviewsPerApp": 50
}

2. Swap in your own values, or call the Actor through the Apify API, Make, Zapier, n8n or the Python/JS clients with the same JSON.
3. Download the results as JSON, CSV or Excel from the Output tab, or schedule the run to repeat.

Full guide: How to Scrape Shopify App Store Reviews (Real Run: 1 and 2 Star Reviews of Judge.me, Loox and Yotpo)

What the Shopify App Reviews Scraper does

You give the Actor app handles or App Store URLs. For each app it reads the public review pages, newest first or in the App Store's own relevance order, and saves one row per review: star rating, date, review text, the merchant's store name and country, how long they had used the app, the developer's reply and its date, and a link to the review. Each row also carries the app's name, developer, overall rating and total review count, so rows from different apps sit side by side.

You can keep only certain star ratings (for example 1 and 2 stars), stop at a date with Only reviews newer than, and cap the number of reviews per app and per run.

• Review text, rating, date, store country and usage time.
• The developer's public reply and its date.
• Star filter, date cut-off and per-app limit.

Who it is for

• App founders and product managers: read every new review of your app and your competitors', and spot recurring complaints early.
• Customer support leads: see which reviews are still unanswered and how fast you reply compared with rivals.
• Investors and analysts: track sentiment and complaint themes for apps you follow.
• Agencies and merchants: check real merchant experiences before recommending or installing an app.

Real example: the newest 1 and 2 star reviews of three review apps

Say you work on a product reviews app and want to know why merchants are unhappy with the market leaders. You ask for the 10 newest 1-star and 2-star reviews each of Judge.me, Loox and Yotpo.

Step 1: Enter the input

Open the Shopify App Reviews Scraper in Apify Console, switch the input form to JSON and paste this. It is the exact input used for the run in this guide:

{
  "apps": [
    "judgeme",
    "loox",
    "yotpo-social-reviews"
  ],
  "ratings": [
    "1",
    "2"
  ],
  "sortBy": "newest",
  "maxReviewsPerApp": 10
}

What each setting does:

• apps: App Store handles (the part after apps.shopify.com/). Full URLs work too.
• ratings: "1" and "2" keep only 1-star and 2-star reviews, using the App Store's own filter.
• sortBy: newest, so the most recent reviews come first.
• maxReviewsPerApp: 10 per app, 30 in total.

Step 2: Run it

Press Start. On 30 October 2026 this input finished with status SUCCEEDED in about 6 seconds and saved 30 rows to the run's dataset. The run was charged 30 review events plus one start event, so the bill was $0.015 on the Free/Bronze tier. All three apps were read; the summary shows 10 reviews for each.

Step 3: Read the output

Here are the first rows of the real dataset, cut to the most useful columns:

appHandle	rating	date	storeCountry	usageDuration	developerReplyDate
loox	1	2026-10-17	United States	About 2 years using the app	2026-10-18
loox	1	2026-10-16	France	8 months using the app
loox	1	2026-10-08	Sweden	9 months using the app	2026-10-19
loox	1	2026-09-26	United States	12 days using the app	2026-09-30
loox	1	2026-09-18	United States	About 1 month using the app
loox	1	2026-08-18	Mexico	Over 5 years using the app	2026-08-19
loox	1	2026-08-18	Türkiye	3 days using the app
loox	1	2026-08-18	United States	7 months using the app	2026-08-19
loox	1	2026-08-12	Türkiye	Over 1 year using the app
loox	1	2026-08-02	United Kingdom	Over 3 years using the app
yotpo-social-reviews	1	2026-07-21	United States	6 days using the app	2026-08-17
yotpo-social-reviews	1	2026-07-16	United States	Over 2 years using the app	2026-08-17

And one full row as JSON (long text shortened with …):

{
  "appHandle": "loox",
  "appUrl": "https://apps.shopify.com/loox",
  "appName": "Loox ‑ Product Reviews App",
  "appDeveloper": "Loox",
  "appRating": 4.9,
  "appReviewCount": 10001,
  "reviewId": "2398974",
  "rating": 1,
  "date": "2026-10-17",
  "reviewText": "They will repeatedly over charge you with sneaky changes to their policy and plan names with out notifying you - their entire business model relies on these sne…",
  "storeName": "Lunavere",
  "storeCountry": "United States",
  "usageDuration": "About 2 years using the app",
  "developerReply": "Hey,\nThanks for taking the time to leave feedback.\n\nWe keep our pricing clear and transparent, with all details available on our website, in our Shopify listing…",
  "developerReplyDate": "2026-10-18",
  "reviewUrl": "https://apps.shopify.com/loox/reviews/2398974"
}

What the 30 real reviews show:

• Loox: pricing is the main complaint. Six of its 10 newest 1-star reviews are about cost or billing, including a merchant on a $49.99 plan who says they were billed $399.99 in usage charges for July, and another who calls the Convert plan's pricing misleading. Loox replied to 5 of the 10, and fast: four replies came within 4 days.
• Yotpo: support is the main complaint. Seven of its 10 newest 1-star reviews mention support; two describe an AI chat that opens a ticket nobody follows up, and two more complain that reviews are hard to export. Yotpo replied to all 10, but slowly, with a median of about 25 days between review and reply. Its 10 newest 1-star reviews go back to January 2026, so negative reviews arrive more slowly than for Loox or Judge.me.
• Judge.me: sent review requests and moderation. Complaints include review request emails that were not sent, spam mentions of other marketplaces in reviews, an Etsy sync glitch, and a store that says its imported reviews stopped counting for Google Shopping after a change in April 2026. Judge.me replied to 8 of 10, with a median of about 9 days. Three of its 10 were 2-star; all the others in this run were 1-star.
• Reviews are global. The 30 merchants are from 15 countries, and replies are often in the reviewer's own language (German, Portuguese and Spanish here).
• Some text is empty or junk. Two reviews have no text and one is random letters. Filter on reviewText length before running sentiment analysis.

Each app's overall score stays high (Judge.me 5.0 with 49,362 reviews, Loox 4.9 with 10,001 and Yotpo 4.8 with 4,631), which is exactly why the low-star filter is useful: it pulls the few critical reviews out of thousands of 5-star ones.

Weekly monitoring with a date cut-off

For ongoing monitoring, set Sort to newest and Only reviews newer than to the date of your last check, for example 2026-10-23. With newest sort the Actor stops reading an app as soon as it reaches older reviews, so a weekly run only pays for the new reviews. Leave ratings empty to see every new review, or keep 1 and 2 to get only the ones that need a reply.

Join the rows on reviewId with last week's export to see which reviews have received a developer reply since.

Output fields

Each row in the dataset has these fields. The example column shows a value from the run above.

Field	What it means	Example from this run
appHandle / appUrl / appName / appDeveloper	The app the review belongs to.	loox / https://apps.shopify.com/loox / Loox ‑ Product Reviews App / Loox
appRating / appReviewCount	The app's overall rating and total reviews at run time.	4.9 / 10001
reviewId / reviewUrl	Review ID and direct link.	2398974 / https://apps.shopify.com/loox/reviews/2398974
rating	Stars given, 1 to 5.	1
date	Review date (YYYY-MM-DD).	2026-10-17
reviewText	The review text, which may be empty.	They will repeatedly over charge you with sneaky changes to…
storeName / storeCountry	The reviewing store and its country, as shown on the App Store.	Lunavere / United States
usageDuration	How long the store had used the app, as shown on the review.	About 2 years using the app
developerReply / developerReplyDate	The developer's public reply and its date, or null.	Hey, Thanks for taking the time to leave feedback. We keep… / 2026-10-18
scrapedAt	When the row was saved.	2026-10-30T16:14:52.798Z

Export Shopify app reviews to CSV, Excel or Google Sheets

Every run saves its rows to a dataset, and a dataset can be downloaded in any common format. You do not need to write code for this.

From Apify Console. Open the run, go to the Output tab and click Export. Pick CSV, Excel (XLSX), JSON, XML, HTML or RSS. You can tick only the columns you want, for example appName, rating, date, reviewText, storeCountry. Nested values such as lists are flattened into columns like developerReply in CSV and Excel.

From a URL. The same export is a plain HTTPS link. Replace <DATASET_ID> with the dataset ID shown on the run's Storage tab:

# CSV with just the columns you need
curl -H "Authorization: Bearer $APIFY_TOKEN" \
  "https://api.apify.com/v2/datasets/<DATASET_ID>/items?format=csv&clean=1&fields=appName,rating,date,reviewText,storeCountry,usageDuration,developerReply,developerReplyDate,reviewUrl" -o shopify-app-reviews.csv

# Excel
curl -H "Authorization: Bearer $APIFY_TOKEN" \
  "https://api.apify.com/v2/datasets/<DATASET_ID>/items?format=xlsx&clean=1" -o shopify-app-reviews.xlsx

Into Google Sheets. You have three good options:

1. One-off: download the CSV and use File > Import > Upload in Google Sheets. Choose "Replace current sheet" to refresh an existing tab.
2. Automatic, no code: in Make, Zapier or n8n, use the Apify app's "Watch Actor runs" (or "Run Actor") trigger, then "Get dataset items", then the Google Sheets "Add row" action. Every scheduled run then lands in your sheet. Apify's own Google Sheets Import & Export Actor can also append a dataset to a sheet, and you can chain it to this Actor's runs with an integration.
3. Formula: =IMPORTDATA("https://api.apify.com/v2/actor-tasks/<USERNAME>~<TASK_NAME>/runs/last/dataset/items?status=SUCCEEDED&format=csv&token=<YOUR_TOKEN>") always pulls the latest successful run of a saved task. The token sits in the formula, so anyone who can open the sheet can see it. Only use this in a private sheet.

Schedule it to run every Monday at 7 AM

New reviews trickle in, so a weekly run with a date cut-off is enough for most apps. Remember to move Only reviews newer than forward, or simply set a per-app limit that covers a week of reviews; you only pay for reviews saved.

1. In the Actor's input form, set up the input from Step 1 and click Save as new task. Give it a clear name such as review-apps-weekly.
2. Go to Schedules > Create new, set the cron expression to 0 7 * * 1 and pick your timezone, so it runs every Monday at 7 AM.
3. Add the task to the schedule and save. Each run gets its own fresh dataset.
4. Optional: on the task's Integrations tab, add a webhook, Slack or email notification for "Run succeeded", or connect Make, Zapier or n8n, so the new rows go where you need them.

To fetch the newest results without looking up a run ID, use the task's last-run link: https://api.apify.com/v2/actor-tasks/<USERNAME>~review-apps-weekly/runs/last/dataset/items?status=SUCCEEDED&format=json.

Call it from the API (Python, JavaScript or curl)

You can start the Actor from your own code with the official Apify clients. Put your API token (Console > Settings > API & Integrations) in an environment variable called APIFY_TOKEN rather than in the code.

Python (pip install "apify-client>=3"). Tested with apify-client 3.4:

import os
from apify_client import ApifyClient

client = ApifyClient(os.environ["APIFY_TOKEN"])
run_input = {
    "apps": [
        "judgeme",
        "loox",
        "yotpo-social-reviews"
    ],
    "ratings": [
        "1",
        "2"
    ],
    "sortBy": "newest",
    "maxReviewsPerApp": 10
}

run = client.actor("mrslm_data/shopify-app-reviews").call(run_input=run_input)

# apify-client 3.x returns a Run object, so use run.default_dataset_id
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item["appHandle"], item["rating"])

In apify-client 3.x, call() returns a typed object. Older examples that use run["defaultDatasetId"] were written for 1.x and 2.x and fail in 3.x with TypeError: 'Run' object is not subscriptable.

JavaScript / Node.js (npm install apify-client):

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const input = {
    "apps": [
        "judgeme",
        "loox",
        "yotpo-social-reviews"
    ],
    "ratings": [
        "1",
        "2"
    ],
    "sortBy": "newest",
    "maxReviewsPerApp": 10
};

const run = await client.actor('mrslm_data/shopify-app-reviews').call(input);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items.length, items[0]);

curl, one request that runs the Actor and returns the rows as CSV (it waits up to 5 minutes, so keep the input small):

curl -X POST "https://api.apify.com/v2/acts/mrslm_data~shopify-app-reviews/run-sync-get-dataset-items?format=csv" \
  -H "Authorization: Bearer $APIFY_TOKEN" -H "Content-Type: application/json" \
  -d '{"apps": ["judgeme", "loox", "yotpo-social-reviews"], "ratings": ["1", "2"], "sortBy": "newest", "maxReviewsPerApp": 10}' -o shopify-app-reviews.csv

What it costs

The Actor uses pay-per-event pricing. Review: $0.50 per 1,000 on the Free and Bronze tiers, $0.45 on Silver and $0.40 on Gold. Each run also has a start fee of $0.00005 per GB of memory (one event at the default memory). You are not billed separately for compute on normal runs: the event prices are the whole bill.

Price per event

Current pay-per-event prices for this Actor on Apify, by Apify subscription plan:

Event (per 1,000)	Free / Bronze	Silver	Gold / Platinum / Diamond
Review (review)	$0.50	$0.45	$0.40
Actor start (once per run at the default memory, not per 1,000)	$0.00005	$0.00005	$0.00005

You are charged only for the events in this table: nothing for compute, and nothing for searches that return no rows beyond the start fee.

Scenario	Free / Bronze	Silver	Gold
The run in this guide (30 reviews)	$0.015	$0.0135	$0.012
Every review of a 2,000-review app (one-off)	$1.00	$0.90	$0.80
10 apps, ~25 new reviews each per week (4 runs a month)	$0.50	$0.45	$0.40
All reviews of 5 large apps, 10,000 each	$25.00	$22.50	$20.00

To cap spending, set Maximum cost per run in the run options (or maxTotalChargeUsd in the API). The Actor stops cleanly at the cap and you are never billed past it. Set Max results to cap reviews across all apps in a run.

Limits

• Only public reviews on apps.shopify.com. Reviews that Shopify has hidden or removed are not shown.
• Store names are what the App Store shows; the Actor does not collect reviewer names, emails or other personal details.
• Review text is saved as written, in any language. Translate it yourself if you need one language.
• Large apps can have tens of thousands of reviews. Use the star filter, date cut-off and limits to keep runs small.
• With relevance sort the date cut-off cannot stop early, so it reads more pages.

FAQ

Can I get every review of an app? Yes. Set Max reviews per app to 0. Large apps can have 10,000 or more.

How do I see only unhappy reviews? Pick 1 and 2 stars under Only these star ratings. The filter is the App Store's own, so it does not read the other reviews.

Does it collect the reviewer's name? No. It saves the store name and country shown on the review, not personal details.

Can I track app rankings too? Yes, with our Shopify App Store Rankings Tracker.

Can I run it on a schedule? Yes. Save your input as a task and attach a schedule, as shown above. Each run is independent and returns the Shopify app reviews available at that moment.

Does it work with Make, Zapier or n8n? Yes. All three have an Apify app. Use "Run Actor" (or run a saved task), then "Get dataset items" to pass the rows to the next step, such as Google Sheets, Airtable, a CRM or Slack.

Can AI agents use it through MCP? Yes. Apify's hosted MCP server can expose this Actor as a tool to any MCP client (Claude Desktop, Cursor, VS Code and others). Add https://mcp.apify.com/?tools=mrslm_data/shopify-app-reviews as a remote MCP server and sign in with your Apify account or API token. The agent fills in the same input fields shown above and gets the same rows back, billed at the same per-event prices.

Is it legal?

This Actor only reads pages and feeds that the website itself publishes to every visitor without a login. It obeys each site's robots.txt, keeps a polite request rate, and never tries to get past logins, paywalls, captchas or bot checks. It does not collect personal data such as emails, phone numbers or names of private individuals. You are responsible for how you use the data: follow the source site's terms, copyright and the privacy laws that apply to you (GDPR, CCPA, etc.). This tool is independent and not affiliated with or endorsed by the platforms it reads.