Naver Shopping Brand Store Scraper: Prices, Stock, Ratings & Reviews

This actor turns any Naver Shopping Brand Store (brand.naver.com) into clean, structured product data. Give it one or more brand store slugs or full store links and get 36+ fields per product back: price, real discount, stock, rating, reviews, brand, maker, seller, full category path, delivery, and images, plus the complete raw record so nothing is lost. Optional per-product review detail, and export to JSON, CSV, Excel, or straight into your app through the API.

Why This Scraper?

• 36+ structured fields per product, plus the full raw object so nothing is dropped.
• Whole-catalog coverage. Paginates each store and pulls every listed product, up to your cap.
• Real discount prices, not just the list price: the actual discounted price a shopper would pay, matched to the store's own discount badge.
• Specials fields: isOnSpecial flags a genuine markdown; originalPrice, discountPercent, and savingsAmount are filled in only when it is true, and left empty otherwise.
• Rich detail: rating and review counts, stock, brand and maker, seller, full category path, delivery type, arrival guarantee, and same day delivery flags.
• Multiple stores per run, sorted by popularity, newest, price, review count, or best selling.
• Built for monitoring. Resume an interrupted run without double counting, or track new, updated, reappeared, and expired products across scheduled runs.

Use Cases

• Brand and e-commerce teams: watch your own or a partner brand's Naver storefront for price, stock, and catalog changes.
• Price monitoring and repricing tools: track discount prices and specials across multiple brand stores at once.
• Market research: compare ratings, review volume, and category coverage across Korean brands selling on Naver.
• Inventory and merchandising checks: monitor stock levels and product availability across a seller's full catalog.
• Product feeds: build a feed of prices, images, and categories for a comparison site, marketplace, or app.

Data You Get

Sample shape: values are illustrative placeholders, not from a live listing.

Field	Example
productName	"오가닉 코튼 티셔츠"
productNo	"00000000000"
salePrice	23800
discountedPrice	21420
originalPrice	23800
discountPercent	10
isOnSpecial	true
savingsAmount	2380
stockQuantity	1900
averageRating	4.9
totalReviews	1144
brand	"샘플브랜드"
maker	"샘플메이커"
sellerName	"샘플공식스토어"
sellerId	"samplestore"
categoryName	"티셔츠"
categoryPath	["패션의류", "남성의류", "티셔츠"]
freeDelivery	true
arrivalGuarantee	true
productUrl	"https://brand.naver.com/samplestore/products/00000000000"
imageUrl	"https://shop-phinf.pstatic.net/00000000/000000000.jpg"
crawledAt	"2026-01-01T00:00:00.000Z"

Each product also carries identifiers and stock/status codes (channelProductId, productStatus, displayStatus, saleType), extra price fields (mobileDiscountedPrice, immediateDiscount), extra category ids, delivery fee details, a raw object with the complete upstream item, and (when fetchReviews is on) a reviewDetail object.

How to Use

1. Pick a mode: search (brand store slugs) or url (paste full brand.naver.com links).
2. List the stores for that mode, and choose a sort order.
3. Set Max products to control run size and cost, and turn on review detail if you need it.
4. Click Start, then download the dataset as JSON, CSV, or Excel, or read it through the API.

One store:

{ "brandStores": ["marschoco"], "maxItems": 100 }

Several stores, cheapest first:

{ "brandStores": ["marschoco", "nike"], "sort": "price_asc", "maxItems": 200 }

Store URL mode, with review detail:

{ "mode": "url", "startUrls": ["https://brand.naver.com/marschoco"], "fetchReviews": true, "maxItems": 50 }

Recurring catalog monitoring:

{ "brandStores": ["marschoco"], "incrementalMode": true, "stateKey": "sample-catalog", "maxItems": 100 }

Run it from your code

Python:

from apify_client import ApifyClient

client = ApifyClient("<YOUR_APIFY_TOKEN>")
run = client.actor("abotapi/naver-brand-store-scraper").call(run_input={"brandStores": ["marschoco"], "maxItems": 100})
for product in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(product["productName"], product["salePrice"])

JavaScript:

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: '<YOUR_APIFY_TOKEN>' });
const run = await client.actor('abotapi/naver-brand-store-scraper').call({ brandStores: ['marschoco'], maxItems: 100 });
const { items } = await client.dataset(run.defaultDatasetId).listItems();

Or connect it to Make, Zapier, n8n, Google Sheets, or webhooks from the Integrations tab.

Resume and recurring updates

• Resume (resumeFromRunId) skips products already collected in a previous run or dataset, useful after an interrupted collection, so you don't pay twice.
• Incremental mode (incrementalMode) is for scheduled runs over the same stores. Each product is classified NEW, UPDATED (with changedFields), UNCHANGED (suppressed and not billed unless emitUnchanged is on), REAPPEARED, or EXPIRED (only after every selected store's entire catalog was read in one run, without hitting Max products, and only with emitExpired on). stateKey names or shares the stored baseline; leave it empty and the actor derives one from the selected stores and options.
• Resume and Incremental mode are intentionally separate and cannot be combined in one run.

Send results into your apps (MCP connectors)

Optionally pipe the scraped results into the apps you already use, via Model Context Protocol (MCP) connectors. This is an extra delivery step after the scrape: the Apify dataset is never changed.

What gets written to the connector: a condensed, human-readable summary of each product, not the full JSON record. The complete record always stays in the Apify dataset.

1. Authorize a connector once under Apify, Settings, Integrations (Notion, Linear, Airtable, or Apify).
2. Select it in the "Pipe results into your apps" input field.
3. For Notion, also set notionParentPageUrl to the page under which product pages should be created.

Leave mcpConnectors empty to skip this entirely.

Input Parameters

Parameter	Type	Default	Description
mode	string	search	search = brand store slugs; url = brand store links
brandStores	array	(none, prefilled with marschoco)	Brand store slugs or brand.naver.com URLs (Search mode)
startUrls	array	(none, prefilled with a sample URL)	Full brand.naver.com store URLs (URL mode)
sort	string	popular	popular, recent, price_asc, price_desc, review, or sales
fetchReviews	boolean	false	Also fetch per-product review detail
maxItems	integer	20	The single result cap, split across stores; 0 = unlimited
proxy	object	(none, prefilled with residential, South Korea)	Connection configuration; keep the default selection for reliable results
resumeFromRunId	string	(none)	Skip products already collected by a previous run or dataset
incrementalMode	boolean	false	Track catalog changes across scheduled runs
stateKey	string	(none, derived automatically)	Optional name for an incremental monitoring campaign
emitUnchanged	boolean	false	Include products with no detected change in incremental mode
emitExpired	boolean	false	Include products no longer found after a complete catalog scan
mcpConnectors	array	(none)	Optional MCP connectors to export results into
notionParentPageUrl	string	(none)	Notion connector only: parent page for the export
maxNotifyListings	integer	50	Cap on items exported per connector, per run

Output Example

Sample shape: values are illustrative placeholders, not from a live listing.

{
  "productNo": "00000000000",
  "channelProductId": "00000000000",
  "productName": "오가닉 코튼 티셔츠",
  "productUrl": "https://brand.naver.com/samplestore/products/00000000000",
  "imageUrl": "https://shop-phinf.pstatic.net/00000000/000000000.jpg",
  "salePrice": 23800,
  "discountedPrice": 21420,
  "mobileDiscountedPrice": 21420,
  "immediateDiscount": 2380,
  "originalPrice": 23800,
  "discountPercent": 10,
  "isOnSpecial": true,
  "savingsAmount": 2380,
  "stockQuantity": 1900,
  "productStatus": "SALE",
  "displayStatus": "ON",
  "saleType": "NEW",
  "authenticationType": "NORMAL",
  "averageRating": 4.9,
  "totalReviews": 1144,
  "premiumReviews": 88,
  "brand": "샘플브랜드",
  "maker": "샘플메이커",
  "sellerName": "샘플공식스토어",
  "sellerId": "samplestore",
  "channelNo": 1000001,
  "channelUid": "sampleuid",
  "categoryId": "50000000",
  "categoryName": "티셔츠",
  "categoryPath": ["패션의류", "남성의류", "티셔츠"],
  "wholeCategoryId": "50000000_50000001_50000002",
  "freeDelivery": true,
  "deliveryFeeType": "FREE",
  "deliveryBaseFee": 0,
  "arrivalGuarantee": true,
  "todayDelivery": false,
  "brandSlug": "samplestore",
  "rank": 1,
  "crawledAt": "2026-01-01T00:00:00.000Z",
  "raw": { "...": "complete upstream item" }
}

Plan Requirement

An Apify account is required to run this actor. The default connection setting, residential and South Korea, is required for reliable results; changing it may return no results at all.

FAQ

How much does it cost?

You pay per product returned, plus an optional per-product charge only when you turn on review detail. The Pricing tab shows the current rates. Use Max products to cap the cost of any run.

Is it legal to scrape Naver Shopping?

This actor reads publicly available Naver Brand Store product pages. You are responsible for how you use the data: follow Naver's terms and the laws that apply to you, and get legal advice if you plan commercial redistribution.

Does this scrape general Naver Shopping search results?

No. It scrapes brand.naver.com Brand Store catalogs, the storefronts brands run directly on Naver. Give it a brand store slug (for example marschoco) or its brand.naver.com URL; it does not take a keyword search across all of Naver Shopping.

Can I monitor a store on a schedule and only get what changed?

Yes. Turn on Incremental mode and schedule the actor from the Schedules tab. Each run then compares every product against the last completed scan and returns only what is new, updated, reappeared, or (once a full scan confirms it, and only with emitExpired on) expired. Unchanged products are skipped and not billed unless emitUnchanged is on.

Why did my run fail instead of returning an empty dataset?

The run fails only in three cases: every store request was rejected so nothing could be read at all, the resumeFromRunId could not be read, or Resume and Incremental mode were both turned on in the same run (they cannot be combined). If a store genuinely has no matching products, or an incremental scan found no changes, the run still succeeds and reports that clearly instead of stopping with an error.

Can I use it with AI agents or MCP?

Yes. Call it from any Apify integration or MCP client, and use the connector field to push results into Notion, Linear, or Airtable.

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