Uber Eats Reviews Scraper

Reputation monitoring for Uber Eats — track store ratings and the latest public customer comments your locations (or competitors) show on Uber Eats.

Paste store URLs. Export aggregate scores plus a recent review sample in one table — ready for spreadsheets, alerts, or BI.

No login. No API key. No Uber Eats account.

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What this Actor is for

Use case	What you monitor	What you get
Reputation management	Score drift + fresh negative/positive comments	aggregateRatingValue, aggregateReviewCount, latest review texts
Local / franchise ops	Each location on Uber Eats	One row per public comment · store context on every row
Agency client reporting	Client restaurants vs competitors	Paste URL lists · schedule recurring runs
Quality / CX benchmarking	Voice-of-customer snapshot + headline rating	JSON/CSV export · dedupe with reviewUuid

What this Actor is not

• Not a full review history export — Uber Eats does not expose every past comment on the public store page.
• Not a per-review star rating tool — individual review stars are usually missing; use aggregateRatingValue for score tracking.
• Not a replacement for Uber Eats merchant dashboards — this reads public store pages only.

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How many reviews per store?

Uber Eats embeds a small recent sample in each store page — we export all of it, not a subset.

Market (observed)	Typical text reviews per run	Total ratings shown by Uber
France	Often 1–3 comments	Often 500–4,000+ (aggregate only)
United States	Up to ~14 on some stores	Can be 800+ (aggregate only)

Example: a French Burger King with 4,000+ total ratings may yield 3 review rows — that is expected, not a scraper bug.

Every row still carries aggregateRatingValue and aggregateReviewCount so you can monitor the headline score even when few comments are public.

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What you get

Field	Description
reviewText	Verbatim customer comment
eaterName	Display name (e.g. Valerie S.)
createdAt	Review timestamp when available (ISO 8601)
rating	Per-review star score — often null (store average only)
storeId	Stable store UUID from the URL
storeName	Restaurant name
aggregateRatingValue	Store average rating
aggregateReviewCount	Total ratings count on Uber Eats
reviewUuid	Stable review identifier for deduplication
storeUrl	Source store page

Typical fill rates (reference export)

Field	Coverage	Notes
reviewText, eaterName, createdAt	High on stores with public comments	SSR sample only
aggregateRatingValue, aggregateReviewCount	~100% on valid store pages	From structured data
rating (per review)	~0%	Uber Eats usually omits per-review stars
Stores with zero text reviews	Possible	Logged, no dataset row (no review charge)

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Important limitations

• Reputation snapshot, not an archive — only the recent public sample Uber Eats puts on the store page (see table above).
• aggregateReviewCount ≫ row count — total ratings can be in the thousands while you export a handful of recent comments.
• France often means 1–3 rows — plan dashboards and billing accordingly; US stores may return more.
• Per-review stars are usually null; monitor aggregateRatingValue instead.
• Data reflects what Uber Eats shows publicly at scrape time — no logged-in or merchant-only views.

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Quick start

1. Open an Uber Eats store page in your browser.
2. Copy the URL (contains /store/restaurant-slug/store-id).
3. Paste into Store URL(s) in the Actor input.
4. Click Start — rows appear in the dataset (one row per review).

Need store URLs at scale? Use the Uber Eats Pro Scraper to discover restaurants by city, then paste URLs here. For menus and prices on the same stores, see the Uber Eats Menu Scraper.

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Input parameters

Parameter	Type	Default	Description
storeUrls	Array	Wamys Annecy example	Uber Eats store page URLs
maxStores	Integer	10	Cap stores processed (0 = no cap)
maxReviewsPerStore	Integer	0	Cap reviews per store (0 = full SSR sample)

API-only parameters

Parameter	Type	Default	Description
verboseLogs	Boolean	false	Technical extraction details in run log
proxyConfiguration	Object	none	Apify proxy settings if pages block datacenter egress

Input example

{
  "storeUrls": [
    { "url": "https://www.ubereats.com/fr/store/wamys/oCwUa_VfWJ2wlyiU6xz3Hg" },
    { "url": "https://www.ubereats.com/fr/store/burger-king-epagny/ZOjYdsqgQcqPQ5q40GJoXQ" }
  ],
  "maxStores": 0,
  "maxReviewsPerStore": 0
}

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Output example

{
  "storeUrl": "https://www.ubereats.com/fr/store/wamys/oCwUa_VfWJ2wlyiU6xz3Hg",
  "storeId": "oCwUa_VfWJ2wlyiU6xz3Hg",
  "storeName": "Wamy's",
  "aggregateRatingValue": 4.3,
  "aggregateReviewCount": 1000,
  "reviewUuid": "cc0aa07e-f83b-4e9b-a7e8-3c4f5b846378",
  "eaterName": "Valerie S.",
  "reviewText": "Super bon ! Contente de mes crevettes",
  "reviewTextShort": "Super bon ! Contente de mes crevettes",
  "createdAt": "2025-01-28T00:00:00Z",
  "rating": null,
  "scrapedAt": "2026-06-07T12:00:00.000Z"
}

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How it works

1. Fetches each public store page over HTTP (Chrome-like headers for full SSR).
2. Reads store rating + total ratings count from structured data (your reputation KPIs).
3. Parses the embedded storeReviews sample — the same recent comments visible on the page.
4. Pushes one dataset row per review, with store aggregates duplicated on each row for easy filtering.

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How much does it cost to scrape Uber Eats reviews?

Pay-per-event pricing (HTTP-only — compute stays low):

Scenario	Approx. cost
~10 reviews (first test)	~$0.06
500 reviews	~$0.55
5,000 reviews	~$5.05

Includes $0.05 run start + $0.001 per review row. Stores with zero text reviews in the SSR sample are not billed as reviews.

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Is it legal to scrape Uber Eats?

This Actor only accesses data that Uber Eats makes publicly available on store pages. As with any dataset containing personal information (customer names, comments), ensure your use complies with GDPR and applicable regulations.

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Local development

cd uber-eats-reviews-scraper
npm install
npm test
npm run sync-input
apify run

• .actor/INPUT.json mirrors Console Try prefill and is copied to storage/.../INPUT.json by npm run sync-input.
• Root input.json can hold heavier presets; merged at runtime when not on Apify Cloud (KV input wins on key conflicts).
• Local dataset: storage/datasets/default/

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Also available

• Uber Eats Pro Scraper — discover restaurant store URLs by city or territory, with phones, addresses, and company IDs for outbound teams.
• Uber Eats Menu Scraper — export full menus (items, sections, prices) from the same store URLs — pricing benchmarks and category research.

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Support

Contact corentin@outreacher.fr for custom scrapers or tailored automation.