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Shopify App Store Review Intelligence API | Merchant Reviews, Pricing & Themes

After this run

Turn this Actor's output into a capped paid report with App Release Review Regression Report. Use it when mobile product, QA, support, and app growth teams need to decide whether a public app release created review regressions that need QA or product action.

• First report: $9 / release_review_snapshot_report; set maxChargeUsd to $9.
• Deeper report: $29 / release_regression_report; use only when the first result needs competitor or action-depth.
• This is an internal Apify flow aid. It is not revenue proof until accounted paid usage appears.

Monitor one Shopify App Store listing with a structured row of ratings, pricing hints, AI summary text, and recurring merchant feedback themes. This actor is for app teams, partnerships, support, and growth operators who need a low-maintenance way to spot praise patterns, complaint clusters, and pricing or onboarding friction from public Shopify App Store pages without custom scraping.

Store Quickstart

• Start with store-input.example.json for the smallest useful dataset run.
• Check sample-output.example.json for a recent single-listing sample captured from the repo's live parser output.
• Check live-proof.example.json for the latest live canary + contract proof on the public actor surface.
• Then move through store-input.templates.json in this order:
  • Quickstart Baseline (Single App Listing) — smallest useful dataset run for one listing
  • Recurring Baseline (Multi-App Merchant Feedback Watch) — same schema tuned for repeat monitoring
  • Webhook Routed Alerts (Merchant Review Handoff) — route the same payload after the dataset baseline and destination schema are stable

What first success looks like

A good first run gives you:

1. apps[] — one merchant-review row with rating, pricing hints, AI summary text, and the recurring themes buyers care about first.
2. reviewSamples[] plus rollups — enough raw evidence and grouped complaints to validate whether onboarding, support, billing, or feature friction is real.
3. warnings and endpointStatuses — explicit source honesty when review pages are thin or a listing surface degrades.

What it does

For each app URL or slug you provide, the actor:

1. Fetches the public listing page at apps.shopify.com/{slug}.
2. Extracts structured metadata from JSON-LD and HTML (name, developer, description, icon, category).
3. Parses the rating summary — overall score, total review count, per-star distribution, and star counts.
4. Extracts pricing plan details — plan names, pricing hints, free-trial indicators, and feature-list snippets.
5. Collects the AI-generated review summary that Shopify surfaces on listing pages.
6. Samples individual reviews from listing and paginated review pages (stars, date, store name, location, usage duration, body text, developer replies).
7. Builds keyword-based rollups — top themes (support, ease of use, bugs, billing, onboarding, etc.) and feature-request excerpts.

If certain surfaces are unavailable (for example no reviews or changed HTML structure), the actor returns explicit warnings instead of failing silently.

Use Cases

Input

Input Example

{
"appUrls":[
"https://apps.shopify.com/omnisend",
"klaviyo-email-marketing"
],
"reviewLimit":50,
"delivery":"dataset",
"dryRun":false
}

Input Examples

Example: Single app reviews

{
"appSlugs":[
"klaviyo"
]
}

Example: Competitor portfolio

{
"appSlugs":[
"klaviyo",
"omnisend",
"privy"
],
"maxReviewsPerApp":50
}

Example: Recent only

{
"appSlugs":[
"klaviyo"
],
"sinceDays":30,
"sortBy":"newest"
}

Output

{
 "meta": {
 "generatedAt": "2026-04-10T17:06:42.111Z",
 "implementationStatus": "live",
 "totalSources": 1,
 "totals": { "apps": 1, "ok": 1, "errors": 0, "warningCount": 0, "totalReviewsSampled": 10 },
 "notes": ["..."]
 },
 "apps": [
 {
 "slug": "omnisend",
 "url": "https://apps.shopify.com/omnisend",
 "status": "ok",
 "app": { "name": "...", "developer": "...", "description": "...", "icon": "...", "category": "..." },
 "rating": { "overall": 4.8, "totalReviews": 2939, "distribution": { "5": 90, "...": "..." }, "starCounts": { "5": 2632, "...": "..." } },
 "pricing": { "hasFreeOption": true, "hasFreeTrial": false, "pricingHint": "Free to install", "plans": ["..."] },
 "reviewSummary": "Merchants praise this app for ...",
 "reviewSamples": [{ "id": "2090002", "stars": 5, "date": "February 21, 2026", "storeName": "...", "body": "..." }],
 "rollups": { "themes": [{ "theme": "customer support", "count": 5 }], "featureRequests": ["..."] },
 "warnings": [],
 "endpointStatuses": { "listing": "ok", "reviews": "ok" },
 "error": null
 }
 ]
}

Proof buyers can inspect

Related Actors

Use this actor first when merchant-review intelligence is the starting signal. Add the next actor when you need to widen the commercial story:

• Shopify Store Intelligence API — Add when you also need the merchant's public storefront, catalog, or launch-page changes to explain review swings.
• App Review Intelligence API | App Store + Google Play Summary — Add when the same company also operates iOS / Android apps and you want merchant feedback plus consumer app sentiment in one portfolio.
• Website Content Extractor — Add when pricing, onboarding, or help-center pages need clean copy for battlecards, churn analysis, or support QA.
• Domain Security Audit API — Add when merchant complaints point to login, checkout, or trust issues on owned web properties.

Pricing & Cost Control

Apify Store pricing is usage-based, so total cost mainly follows how many app listings you analyze and how many review pages you sample. Check the Store pricing card for the current per-event rates.

• Start with Quickstart Baseline on 1–2 listings in dataset mode while you validate the schema.
• Keep reviewLimit modest for Recurring Baseline; increase it only when deeper theme clustering changes decisions.
• Move to Webhook Routed Alerts once the merchant-feedback schema already fits your routing workflow.
• Use dryRun: true before recurring runs or larger watchlists.

Local run

npm start # Uses input.json
npmtest# Run unit tests

Docker

docker build -t shopify-app-reviews .
docker run --rm shopify-app-reviews

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Bug report or feature request? Open an issue on the Issues tab of this actor.

Next Step: Listing Gap Report

If you need a buyer-ready listing improvement report instead of raw metadata or review rows, run mobile-app-listing-gap-report next. It compares one target app with competitor App Store / Google Play listings, checks buyer-provided seed keyword coverage in public metadata, and returns prioritized listing edits.

Recommended handoff:

{
"targetApp":"APP_STORE_URL_OR_GOOGLE_PLAY_PACKAGE",
"competitorApps":["COMPETITOR_APP_1","COMPETITOR_APP_2","COMPETITOR_APP_3"],
"reportTier":"premium",
"maxChargeUsd":29,
"dryRun":false
}

Related report Actors

Use these follow-on Actors when you want a capped, decision-ready report instead of more raw rows. They use public or user-provided inputs, respect maxChargeUsd, and do not promise rankings, revenue, conversion lifts, or sales outcomes.

• App Release Review Regression Report - use the same review-regression workflow for public mobile app reviews.

Related paid report workflows

If this Actor gave you raw rows or source context, these follow-on report Actors are designed for a small capped paid run. They help make a decision, not just collect more data.

• App Release Review Regression Report - decide whether a public app release created review regressions that need QA or product action. Entry $9 / release_review_snapshot_report; premium $29 / release_regression_report.

Keep maxChargeUsd equal to the selected tier. Internal links are traffic aids only; real proof requires accounted paid usage.