Naver Product Shopping Scraper

Naver Product Shopping Scraper is an Apify Actor for people who need current Naver Shopping search results as one normalized record per product result. It accepts one or more plain-text product queries, and each returned dataset record represents a single public search-result item with fields such as product ID, title, product URL, image URL, current price in KRW, original price, discount percent, seller name, brand, manufacturer, category path, rating, review count, delivery fee, availability, source URL, and scraped time. The outcome is a comparison-ready dataset for Korean ecommerce price research, with provenance preserved in each record.

Best fit and connected workflows

This Actor fits workflows built around Naver Shopping search terms, such as keyword-based price tracking, marketplace comparison, catalog exploration, and downstream enrichment from public search-result data.

It is a strong routing choice when your starting point is a product phrase like "wireless earbuds" or "무선 이어폰" and you want the public shopping results that Naver shows for that phrase. It also works well as an input step for agents and internal tools that need a compact comparison dataset before they sort, deduplicate, or alert on changes.

If your workflow begins with a Naver product search phrase and ends with normalized search-result records, this Actor covers that path cleanly.

Practical scenario

A procurement analyst types a search phrase for "mechanical keyboard" and sets a result cap to keep the comparison concise. The returned records include title, priceKrw, sellerName, brand, rating, reviewCount, and productUrl. The analyst uses those fields to compare sellers, then opens the sourceUrl for the same search query to confirm the public Naver Shopping context before sharing the dataset with the buying team.

Input

Use searchQueries for one or more product phrases. Use maxResults to cap how many product-result records are written.

Field	Type	Required	Description
searchQueries	array of strings	Yes	Naver Shopping search queries. Accepts up to 20 plain-text product phrases, each up to 200 characters.
maxResults	integer	No	Maximum number of product records to return and charge. Minimum 1, maximum 200, default 20.

Valid focused input example:

{
  "searchQueries": ["wireless earbuds", "무선 이어폰"],
  "maxResults": 20
}

Output

The default dataset contains one record per validated product result. The main output fields are below.

Field	Type	Description
productId	string	Naver Shopping product or catalog identifier.
title	string	Product title shown in the search result.
productUrl	string or null	Product or catalog URL when available.
imageUrl	string or null	Main product image URL when present.
priceKrw	number or null	Current or lowest listed price in KRW.
originalPriceKrw	number or null	Original or comparison price in KRW when shown.
discountPercent	number or null	Displayed discount percentage when available.
sellerName	string or null	Merchant or mall name.
sellerUrl	string or null	Merchant page URL when provided.
brand	string or null	Brand named in the record.
manufacturer	string or null	Manufacturer named in the record.
categoryPath	array of strings	Category labels from broad to specific when available.
rating	number or null	Product rating on a 0 to 5 scale when shown.
reviewCount	integer or null	Review count when exposed in the result.
deliveryFeeKrw	number or null	Delivery fee in KRW when shown.
availability	string	One of IN_STOCK, OUT_OF_STOCK, or UNKNOWN.
sourceUrl	string	Naver Shopping search URL that produced the record.
scrapedAt	string	ISO 8601 timestamp for normalization and write time.

Illustrative dataset record:

{
  "productId": "1234567890",
  "title": "Wireless Bluetooth Earbuds",
  "productUrl": "https://search.shopping.naver.com/catalog/1234567890",
  "imageUrl": "https://shopping-phinf.pstatic.net/main/example.jpg",
  "priceKrw": 59900,
  "originalPriceKrw": 79900,
  "discountPercent": 25,
  "sellerName": "Example Mall",
  "sellerUrl": "https://smartstore.naver.com/example",
  "brand": "Example Brand",
  "manufacturer": "Example Manufacturer",
  "categoryPath": ["Digital", "Audio", "Earbuds"],
  "rating": 4.8,
  "reviewCount": 123,
  "deliveryFeeKrw": 3000,
  "availability": "UNKNOWN",
  "sourceUrl": "https://search.shopping.naver.com/search/all?query=wireless%20earbuds",
  "scrapedAt": "2026-07-14T12:00:00.000Z"
}

The default key-value store also exposes OUTPUT for compact run outcome data and RUN_SUMMARY for billing and coverage details.

How it works

The Actor runs a managed browser-based Naver Shopping search workflow from the supplied queries. It normalizes each validated product result into the dataset schema and writes it as a separate item. The dataset view surfaces comparison-friendly fields such as image, title, price, original price, seller, brand, rating, review count, product URL, and scraped time.

The actor specification indicates a Playwright and Camoufox template, uses 2048 MB default memory, and supports the provided proxy environment variables. Its charged events include an actor start event and a primary Product result event for each validated dataset item.

Pricing

This Actor uses Pay per event plus Apify platform usage. The live Pricing tab shows the current event pricing and any platform charges that apply to your account and execution configuration.

An example with twenty product-result events means one actor start event plus twenty Product result events, with Apify platform usage billed separately. For any planning or cost review, use the live Pricing tab as the source of truth.

Use with AI agents (MCP)

This Actor is available as an Apify Actor usable through Apify MCP. The precise Actor identity is khadinakbar/naver-product-shopping-scraper.

Tool behavior is straightforward: provide Naver Shopping search queries and a result cap, then read the returned dataset items and the key-value store records for run status and billing summary.

Search Naver Shopping for "wireless earbuds" and return up to 10 normalized product results. Keep the output focused on title, price, seller, brand, rating, review count, product URL, and source URL, then summarize which listings are most comparable by price.

When using the output with an agent, sourceUrl preserves the query URL that produced each record and scrapedAt preserves collection time. availability reflects the public search-result signal, and categoryPath helps with routing by product category. If you need a broader or narrower dataset, adjust searchQueries and maxResults; the latter caps the number of returned product records and the corresponding product-result events.

Apify API example

import { ApifyClient } from "apify-client";

const client = new ApifyClient({
  token: process.env.APIFY_TOKEN,
});

const run = await client.actor("khadinakbar/naver-product-shopping-scraper").call({
  searchQueries: ["wireless earbuds"],
  maxResults: 5,
});

const datasetItems = await client.dataset(run.defaultDatasetId).listItems();

console.log(datasetItems.items);

Best results and outcome guidance

Start with one clear product phrase per query when you want focused comparison records. Use maxResults to keep the run aligned with the number of products you plan to review. Query phrases in Korean or English are both suitable for the browser workflow. For downstream analysis, compare priceKrw, sellerName, brand, rating, reviewCount, and sourceUrl together so the record stays tied to the original search context.

Continue the workflow

• Then use Ozon Scraper — Russia Marketplace Products, Prices & Reviews to extend Naver Product Shopping Scraper with a neighboring commerce research source when the brief calls for Ozon data.
• Then use Myntra Product Search Scraper — Price, Discount & Rating to extend Naver Product Shopping Scraper with a neighboring commerce research source when the brief calls for Myntra data.

Design note

I found that the live output contract returns normalized product records in the dataset plus separate OUTPUT and RUN_SUMMARY records in the default key-value store, which makes the run easy to inspect from both data and execution perspectives.

FAQ

Is this Actor built for Naver Shopping search pages or product detail pages?

It is built for Naver Shopping search-result records from product queries. The input schema is centered on plain-text search phrases rather than product-detail URLs.

What kind of data comes back?

Each item is one public search result with fields for product identity, pricing, seller information, brand, manufacturer, category path, rating, review count, delivery fee, availability, source URL, and collection time.

How should I route a Korean ecommerce workflow?

Start here when the workflow begins with search phrases and needs comparable product results. Use the returned dataset as a comparison layer before any separate enrichment or reporting step.

How many queries can I send?

The input schema accepts up to 20 search queries in one run.

How can I keep costs bounded?

Use maxResults as the record cap for the run. It also caps product-result events, which helps align the run with your planned comparison size.

Responsible use

Use this Actor for lawful collection of public Naver Shopping search-result data and for workflows you are authorized to operate. Keep downstream handling aligned with your data governance, privacy, and platform obligations. Review the live Pricing tab before scaling a workflow, and preserve sourceUrl and scrapedAt in any analysis or reporting that relies on provenance.