# Naver Shopping Product Research Dataset by Keyword

**Use case:** 

Build a research dataset from Naver Shopping. Enter an English keyword like 'skincare serum' and this task returns 27 fields per product — productName, originalPrice, discountedPrice, discountRate, sellerName, isBrandStore, averageRating, totalReviews and status flags — across as many pages as you set (here 4, ~200 products). Export to CSV or JSON for market and pricing analysis.

## Input

```json
{
  "keywords": [
    "skincare serum"
  ],
  "maxCrawlPages": 4
}
```

## Output

```json
{
  "isAd": {
    "label": "Ad Status",
    "format": "text"
  },
  "productName": {
    "label": "Product Name",
    "format": "text"
  },
  "productId": {
    "label": "Product ID",
    "format": "text"
  },
  "productPageUrl": {
    "label": "Product Link",
    "format": "link"
  },
  "productImageUrl": {
    "label": "Product Image",
    "format": "image"
  },
  "sellerName": {
    "label": "Seller Name",
    "format": "text"
  },
  "sellerPageUrl": {
    "label": "Seller Link",
    "format": "link"
  },
  "sellerId": {
    "label": "Seller ID",
    "format": "text"
  },
  "isBrandStore": {
    "label": "Brand Store",
    "format": "boolean"
  },
  "originalPrice": {
    "label": "Original Price",
    "format": "text"
  },
  "discountedPrice": {
    "label": "Discounted Price",
    "format": "text"
  },
  "discountRate": {
    "label": "Discount Rate",
    "format": "text"
  },
  "shippingFee": {
    "label": "Shipping Fee",
    "format": "text"
  },
  "averageRating": {
    "label": "Average Rating",
    "format": "text"
  },
  "totalReviews": {
    "label": "Total Reviews",
    "format": "text"
  },
  "stock": {
    "label": "Stock Availability",
    "format": "text"
  },
  "orderCutoffTime": {
    "label": "Order Cutoff Time",
    "format": "text"
  },
  "authenticationType": {
    "label": "Authentication Type",
    "format": "text"
  },
  "naverPaySellerId": {
    "label": "Naver Pay Seller ID",
    "format": "text"
  },
  "isNewProduct": {
    "label": "New Product",
    "format": "boolean"
  },
  "isGroupProduct": {
    "label": "Group Product",
    "format": "boolean"
  },
  "isFreshProduct": {
    "label": "Fresh Product",
    "format": "boolean"
  },
  "isOverseaProduct": {
    "label": "Overseas Product",
    "format": "boolean"
  },
  "hasGift": {
    "label": "Gift Included",
    "format": "boolean"
  },
  "isSuperPointProduct": {
    "label": "Super Points Product",
    "format": "boolean"
  },
  "searchKeyword": {
    "label": "Search Keyword",
    "format": "text"
  },
  "crawledAt": {
    "label": "Crawled At",
    "format": "text"
  }
}
```

## About this Actor

This example demonstrates how to use [Naver Shopping Product Scraper](https://apify.com/delicious_zebu/naver-shopping-product-scraper.md) with a specific input configuration. Visit the [Actor detail page](https://apify.com/delicious_zebu/naver-shopping-product-scraper.md) to learn more, explore other use cases, and run it yourself.


## How to integrate an Actor?

This Task's input is already configured above. Use it as-is rather than inventing a new one.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For full API examples (JavaScript, Python, CLI, MCP, OpenAPI), see this Task's Actor page: https://apify.com/delicious_zebu/naver-shopping-product-scraper.md

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).