# Korean Search & Shopping Trends - NAVER DataLab (`loopchips/korea-search-trends`) Actor

Measure Korean demand: keyword search trends over time plus NAVER Shopping insights broken down by category, keyword, gender, age band and device. Official DataLab API, timestamped for time-series work.

- **URL**: https://apify.com/loopchips/korea-search-trends.md
- **Developed by:** [Loopchips](https://apify.com/loopchips) (community)
- **Categories:** SEO tools, E-commerce, Automation
- **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $4.00 / 1,000 results

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## Korean Search & Shopping Trends - NAVER DataLab

**What Korea is searching for, and who is buying what.** NAVER holds the dominant share of Korean search, and DataLab is its official trend data. This actor turns it into rows you can chart, model or feed into a dashboard.

Two datasets in one run: **keyword search trends** and **shopping demand insights broken down by category, keyword, gender, age band and device.**

***

### What you get

**Search trends** - one row per keyword group per period

| Field | Example |
|---|---|
| group | AI |
| keywords | AI, 인공지능 |
| period | 2026-06-01 |
| ratio | 100 (index, peak in your window = 100) |
| timeUnit | month |

**Shopping insights** - one row per segment per period

| dimension | segment values | what it tells you |
|---|---|---|
| category | overall | how demand for a whole category moves |
| keyword | your product keywords | which products are pulling demand |
| **gender** | female, male | who buys the category |
| **age** | 10-19, 20-29, 30-39, 40-49, 50-59, 60+ | which age bands drive it |
| **device** | mobile, pc | where they shop from |

Real example from a single run on Korean fashion: **40-49 age band indexes highest, women index 3x men, mobile indexes 7x PC.** That is a targeting brief in three rows.

Export as **JSON, CSV or Excel**, or pull from the API.

### What people use it for

**Market entry sizing** - Before launching in Korea, see whether interest in your category is rising or falling, and who the actual buyer is by age and gender.

**Ad targeting briefs** - Age, gender and device splits per category tell you where to put budget without guessing.

**SEO and content planning** - Keyword seasonality by month or week, so you publish before the peak, not after.

**Product and merchandising decisions** - Compare keyword demand inside a category to see which product lines are heating up.

**Competitive tracking** - Put competitor brand names in as keyword groups and watch relative interest shift over time.

### How to use it

1. Get a **free NAVER API HUB credential** at https://www.ncloud.com (Services > Application Services > NAVER API HUB) and enable the Data Lab APIs
2. Set **start date**, **end date** and **interval** (daily, weekly, monthly)
3. For search trends: add **keyword groups**. Each group can bundle synonyms that count as one line, e.g. `{"groupName": "AI", "keywords": ["AI", "인공지능"]}`
4. For shopping: add **shopping categories** by code, optionally **shopping keywords**, and pick which **breakdowns** you want
5. Run it, or schedule it monthly to build a demand history

**Finding a category code** - open any category on NAVER Shopping and copy the `cat_id` value from the URL. `50000000` is fashion apparel, `50000003` is digital and appliances.

### Reading the numbers

DataLab returns a **relative index, not absolute volume**. Within each requested window the peak point is 100 and everything else is scaled to it. That means:

- Comparisons **inside one run** are meaningful (this month vs last month, women vs men)
- Comparisons **across separate runs with different date ranges** are not directly comparable
- To build a long time series, run one long window rather than stitching short ones

This is how NAVER publishes the data, and this actor passes it through without inventing precision that is not there.

### Pairs with

**Korean Price Comparison Scraper** (what things cost) plus this actor (what people want) is a complete Korean e-commerce demand picture. Also works with **NAVER Search Scraper** for the qualitative side - what people are actually saying about a keyword.

### FAQ

**Do I need my own credential?** Yes, and it is free. Your usage runs under your own quota.

**How far back does data go?** Search trends from 2016-01-01, shopping insights from 2017-08-01.

**Why is the current month low?** A partial month indexes low because it is not finished yet. Use complete periods for analysis.

**Will it stay free?** NAVER API HUB is currently free and states that a move to paid pricing will be announced in advance. Check the terms when you register.

### Support

Need another breakdown or dataset? Open an issue on the actor page - issues are answered within 3 business days.

# Actor input Schema

## `ncpApiKeyId` (type: `string`):

Your own free NAVER API HUB credential. Create an application at https://www.ncloud.com (Services > Application Services > NAVER API HUB) and enable the Data Lab APIs.

## `ncpApiKey` (type: `string`):

The Client Secret from the same NAVER API HUB application.

## `startDate` (type: `string`):

YYYY-MM-DD. Data is available from 2016-01-01 for search trends and 2017-08-01 for shopping insights.

## `endDate` (type: `string`):

YYYY-MM-DD.

## `timeUnit` (type: `string`):

Granularity of each data point.

## `keywordGroups` (type: `array`):

Keyword groups for search trend comparison. Each group can bundle synonyms that count as one line, e.g. {"groupName": "AI", "keywords": \["AI", "인공지능"]}. Up to 5 groups are compared per request; more are sent in batches.

## `shoppingCategories` (type: `array`):

NAVER Shopping category codes to analyse, e.g. \[{"name": "Fashion", "code": "50000000"}]. Find a code in the cat\_id parameter of any NAVER Shopping category URL.

## `shoppingKeywords` (type: `array`):

Optional. Product keywords to measure inside each shopping category (up to 5).

## `shoppingBreakdowns` (type: `array`):

Extra segment dimensions to pull for each shopping category.

## `requestDelayMs` (type: `integer`):

Politeness delay between API calls.

## Actor input object example

```json
{
  "startDate": "2026-01-01",
  "endDate": "2026-08-01",
  "timeUnit": "month",
  "keywordGroups": [
    {
      "groupName": "AI",
      "keywords": [
        "AI",
        "인공지능"
      ]
    },
    {
      "groupName": "전기차",
      "keywords": [
        "전기차"
      ]
    }
  ],
  "shoppingCategories": [
    {
      "name": "패션의류",
      "code": "50000000"
    }
  ],
  "shoppingBreakdowns": [
    "gender",
    "age",
    "device"
  ],
  "requestDelayMs": 400
}
```

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "startDate": "2026-01-01",
    "endDate": "2026-08-01",
    "keywordGroups": [
        {
            "groupName": "AI",
            "keywords": [
                "AI",
                "인공지능"
            ]
        },
        {
            "groupName": "전기차",
            "keywords": [
                "전기차"
            ]
        }
    ],
    "shoppingCategories": [
        {
            "name": "패션의류",
            "code": "50000000"
        }
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("loopchips/korea-search-trends").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "startDate": "2026-01-01",
    "endDate": "2026-08-01",
    "keywordGroups": [
        {
            "groupName": "AI",
            "keywords": [
                "AI",
                "인공지능",
            ],
        },
        {
            "groupName": "전기차",
            "keywords": ["전기차"],
        },
    ],
    "shoppingCategories": [{
            "name": "패션의류",
            "code": "50000000",
        }],
}

# Run the Actor and wait for it to finish
run = client.actor("loopchips/korea-search-trends").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "startDate": "2026-01-01",
  "endDate": "2026-08-01",
  "keywordGroups": [
    {
      "groupName": "AI",
      "keywords": [
        "AI",
        "인공지능"
      ]
    },
    {
      "groupName": "전기차",
      "keywords": [
        "전기차"
      ]
    }
  ],
  "shoppingCategories": [
    {
      "name": "패션의류",
      "code": "50000000"
    }
  ]
}' |
apify call loopchips/korea-search-trends --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,loopchips/korea-search-trends"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/Q6iEjX3SpsqodROBk/builds/YrDrlqrG3cWHDdvOp/openapi.json