🍱 Coupang Eats Crawler

Extract restaurants, menus, prices, ratings, and delivery info from Coupang Eats (쿠팡이츠) — South Korea's #2 food delivery platform and one half of a fast-tightening duopoly that now controls ~88% of the Korean delivery market.

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🌏 What is Coupang Eats?

Coupang Eats is the food-delivery arm of Coupang, Korea's largest e-commerce company (the "Amazon of Korea", NYSE-listed CPNG). Coupang Eats launched in 2019 as a third-place challenger to Baemin (Baedal Minjok) and Yogiyo. As of May 2026 it has overtaken Yogiyo and become the clear #2:

Platform	Weekly Active Users (May 2026)	Market position
Baemin (Baedal Minjok)	~12M	#1
Coupang Eats	8.45M	#2 — fast growing
Yogiyo	~2M	distant #3

Baemin + Coupang Eats together hold 88.3% of Korea's delivery app market — the highest concentration on record. Korea is now effectively a duopoly, and Coupang Eats is the side that's still gaining share, largely because every Coupang Wow membership (Coupang's Amazon-Prime equivalent, ~15M subscribers paying ₩7,890/month) bundles free Eats delivery into the subscription.

The platform is address-gated: every search, category browse, and store ranking is computed relative to a delivery point (latitude + longitude). The same restaurant can rank #1 in Gangnam and not appear at all in Hongdae 8 km away.

🇺🇸 US equivalent

• DoorDash — closest by market structure (the dominant scale player whose growth was unlocked by the DashPass subscription bundle).
• Uber Eats — closest by parent-company logic (a delivery service riding on top of a bigger consumer platform's logistics + payments + identity).

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🚀 What does this actor do?

Extracts restaurant listings and menus from Coupang Eats. You can:

• Provide one or more delivery addresses (addresses) — required for search/category browsing because Coupang Eats results depend on the delivery location.
• Provide search queries (searchQueries) — Korean or English. Use Korean (치킨, 피자, 떡볶이) for the broadest results; English (Starbucks, McDonald's, pizza) works for global chains and cafes with English names.
• Provide category IDs (categoryIds) — browses each category at each address.
• Provide direct store IDs (storeIds) — fetches each store; no address required.
• Mix any combination in a single run.

You give it inputs, it returns the dataset.

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🍗 Why this data matters (buyer personas)

Food-delivery data has more independent buyer personas than almost any other vertical — that's what makes it a high-margin scraping target.

Buyer	What they use the data for	Refresh cadence
Hedge fund / alt-data desks	"How many restaurants did Coupang Eats add this quarter? Is Baemin pushing harder on discounting? Use weekly catalog deltas as a leading indicator on CPNG and on the privately held Baemin parent (Woowa/DH)."	Daily / weekly
Restaurant brands & franchises	"Where is BBQ Chicken under-distributed? What's our menu pricing vs Kyochon and bhc, per district?"	Daily
Brand-protection / IP teams	"Find restaurants illegally selling our trademarked menu items or impersonating our brand."	Daily
F&B market-research firms	"What's the average delivery fee in Gangnam vs Jeju? How often do new openings stick after 90 days?"	Weekly / monthly
Restaurant SaaS (POS, ad tools)	"Cold-call lists of independents on Coupang Eats sorted by review count — they're the ones doing volume."	Monthly
Cross-border quick-commerce expansion teams	DoorDash / Uber / Wolt strategy desks studying the Korean duopoly playbook (Wow-membership bundle).	Quarterly
Logistics & last-mile	Delivery time benchmarks per district + time-of-day; courier-supply intelligence.	Weekly
Consumer-trend / agencies	What cuisines are trending? Which delivery times are demanded most? Promotion-frequency analysis.	Weekly

These personas pay separately, which lets you stack the same dataset into multiple revenue streams instead of competing on a single buyer.

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🗾️ Example input

Search by keyword at a single address (most common) — works in Korean or English:

{
  "addresses": [
    { "latitude": 37.4979, "longitude": 127.0276, "label": "Gangnam, Seoul" }
  ],
  "searchQueries": ["치킨", "피자", "Starbucks"],
  "maxPagesPerQuery": 1
}

Browse categories across multiple districts:

{
  "addresses": [
    { "latitude": 37.4979, "longitude": 127.0276, "label": "Gangnam" },
    { "latitude": 37.5563, "longitude": 126.9236, "label": "Hongdae" },
    { "latitude": 35.1796, "longitude": 129.0756, "label": "Busan Seomyeon" }
  ],
  "categoryIds": ["1", "2", "3"],
  "maxPagesPerQuery": 3
}

Direct store fetch by ID (no address required):

{
  "storeIds": ["461101", "837003"],
  "fetchMenu": true
}

Mix everything:

{
  "addresses": [
    { "latitude": 37.4979, "longitude": 127.0276, "label": "Gangnam" }
  ],
  "searchQueries": ["치킨", "pizza"],
  "categoryIds": ["1"],
  "storeIds": ["461101"],
  "fetchMenu": true,
  "maxPagesPerQuery": 2
}

---

📦 What data do you get?

Every restaurant is returned with the following fields:

{
  "store_id": "461101",
  "name": "BBQ치킨 강남점",
  "name_en": null,
  "categories": ["치킨", "한식"],
  "category_id": null,
  "rating_avg": 4.7,
  "rating_count": 1234,
  "review_count": 1234,
  "address": "서울 강남구 테헤란로 152",
  "address_road": "테헤란로 152",
  "lat": 37.4979,
  "lng": 127.0276,
  "phone": null,
  "delivery_fee_min": 0,
  "delivery_fee_max": 3000,
  "delivery_fee_text": "0원 ~ 3,000원",
  "delivery_time_min": 25,
  "delivery_time_max": 35,
  "minimum_order_amount": 15000,
  "currency": "KRW",
  "is_coupang_delivery": true,
  "is_wow_free_delivery": true,
  "is_open": true,
  "is_new": false,
  "is_promoted": false,
  "discount_badge": "20% 할인",
  "promotion_text": "신규 가입 5,000원 쿠폰",
  "thumbnail": "https://image.coupangcdn.com/.../bbq.jpg",
  "images": [],
  "hours": null,
  "menu_count": 12,
  "menu": [
    {
      "menu_item_id": "...",
      "name": "황금올리브 치킨",
      "price": 23000,
      "original_price": null,
      "description": "바삭한 후라이드 치킨",
      "thumbnail": "https://...",
      "is_signature": true,
      "is_popular": true,
      "options": []
    }
  ],
  "search_query": "치킨",
  "search_rank": 1,
  "search_address_label": "Gangnam",
  "search_lat": 37.4979,
  "search_lng": 127.0276,
  "store_url": "https://web.coupangeats.com/share?storeId=461101",
  "scraped_at": "2026-06-09T13:42:24.433Z"
}

Field reference

Field	Type	Notes
store_id	string	Coupang Eats numeric store ID (the storeId in the share URL)
name, name_en	string	Restaurant name (Korean; English when available)
categories	array	Cuisine categories (치킨, 한식, 일식, etc.)
category_id	string	Set when the store was found via a category browse
rating_avg	number	Average review rating (0–5)
rating_count / review_count	number	Total review count
address, address_road	string	Full address and road-name address (도로명 주소)
lat, lng	number	Restaurant coordinates
phone	string	Phone (often null — many stores hide this)
delivery_fee_min, delivery_fee_max	number	Delivery fee range in KRW (varies by distance / promotions)
delivery_fee_text	string	Original humanised fee string when not numeric
delivery_time_min, delivery_time_max	number	Estimated delivery time in minutes
minimum_order_amount	number	Minimum order in KRW
currency	string	Always "KRW"
is_coupang_delivery	bool	True when delivered by Coupang's own fleet (쿠팡배달), not the restaurant's
is_wow_free_delivery	bool	True when the store qualifies for free delivery under a Coupang Wow membership
is_open	bool	Currently accepting orders
is_new	bool	Recently opened on the platform
is_promoted	bool	Paid placement (Coupang Eats ads)
discount_badge	string	E.g. "20% 할인"
promotion_text	string	Free-form promo text
thumbnail	string	Primary restaurant image URL
images	array	Additional images
hours	object	Operating hours per day-of-week (when available)
menu_count	number	Number of items in the menu (populated when fetchMenu=true or when listings inline the menu)
menu	array	Menu items with name, price, description, thumbnail, options
search_query	string	Set when the store was found via a search
search_rank	number	Position in the listing
search_address_label, search_lat, search_lng	string/number	The address slice this result was collected under
store_url	string	Public share URL — https://web.coupangeats.com/share?storeId={store_id}
scraped_at	string	ISO timestamp when the page was extracted

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🦖 How to use

1. Enter at least one input source — addresses + searchQueries / categoryIds, OR storeIds.
2. Optionally set maxPagesPerQuery (default 1) and fetchMenu (default false).
3. Run the actor.
4. Download the results from the Dataset tab or fetch them via the API.

Note: A run with 1 address × 3 queries × 1 page completes in under 10 seconds.

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🔍 Key features

• Fast — most runs complete in seconds, not minutes.
• Geo-aware search — every search/category is anchored to a real delivery address, so rankings match what a Gangnam customer would actually see.
• Three input modes — keyword search, category browsing, and direct store fetch — mixable in one run.
• Search in Korean or English — Korean returns more results, English works great for global chains.
• Structured output — 40+ fields per restaurant, plus optional embedded menu array.
• Automatic retries — transient errors are retried with exponential backoff.

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📋 Input parameters

Parameter	Type	Default	Description
addresses	array of {latitude, longitude, label}	[]	Delivery addresses to browse from. Required when using searchQueries or categoryIds.
searchQueries	array of strings	[]	Search keywords — Korean or English. Korean returns the broadest results; English works for global chains and English-named cafes.
categoryIds	array of strings	[]	Numeric Coupang Eats category IDs (e.g. 1 for chicken).
storeIds	array of strings	[]	Direct numeric store IDs — no address required.
maxPagesPerQuery	integer	1	Pages to walk per search/category at each address. Each page ≈ 20 stores.
fetchMenu	boolean	false	Attach the full menu and additional store fields to each discovered store.
proxyConfiguration	object	none	Optional. Not required for normal runs.

Common address coordinates (for testing)

District	Latitude	Longitude
Seoul — Gangnam Station	37.4979	127.0276
Seoul — Hongdae	37.5563	126.9236
Seoul — Itaewon	37.5347	126.9947
Seoul — Jongno	37.5703	126.9824
Busan — Seomyeon	35.1796	129.0756
Daegu — Dongseong-ro	35.8693	128.5953
Incheon — Songdo	37.3831	126.6562
Jeju City	33.4996	126.5312

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📩 Feedback

Found a bug or have ideas? Open an issue on the actor's Apify page — happy to improve it.