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🍱 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.

🌏 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:

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).

🚀 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.

🍗 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.

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

🗾️ 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

🦖 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.

🔍 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.

📋 Input parameters

Common address coordinates (for testing)

📩 Feedback

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