Naver DataLab Shopping Insight Scraper

Export the top 500 popular Naver Shopping search keywords for any shopping category, filtered by device, gender and age, plus click trends and audience splits from Naver DataLab Shopping Insight (쇼핑인사이트).

What does Naver Shopping Insight Scraper do?

Naver DataLab Shopping Insight is Korea's closest equivalent to Google Trends for e-commerce. For every Naver Shopping category it shows which search keywords led to the most clicks over a period, and how category interest moved over time. The web page only lets you page through 20 keywords at a time for one category and one filter combination. This Actor collects the same public statistics in bulk. It returns one clean row per category and keyword, plus optional click trend series and device, gender and age breakdowns, ready for spreadsheets, BI tools or AI agents.

Why use it

• Up to 500 keywords per category in one run. The Actor pages through the full DataLab top 500 for you. On the website you page through it 20 at a time.
• Category names work, not only IDs. Type 패션의류, 스킨케어 or 화장품/미용 > 스킨케어 > 크림. Typos return a clear error with suggestions such as 화장품/미용 > 스킨케어 (50000190), and you are not charged for them.
• Same filters as DataLab. Device (PC or mobile), gender and age groups (10s to 60+), and any date range from 2017-08-01 up to yesterday.
• Rank changes the website does not show. Turn on compareWithPreviousPeriod to get previousRank, rankChange and rankTrend (up, down, same, new) for each keyword against the previous period of the same length.
• Trend rows built for analysis. Each trend row has the full [{period, ratio}] series, the peak, the average, partial-period flags, and device, gender and age shares.
• Free category browser. List the category tree with IDs before you run paid jobs.
• Polite and reliable. Requests are rate-limited, and 429/5xx responses are retried with exponential backoff. No login and no browser are needed, so runs are fast and cheap.

What data you get

Keyword ranking rows (type: "keywordRank"), one per category and keyword:

Field	Example	Description
rank	1	Popularity rank in the category for the period (1 to 500)
keyword	ahc아이크림	Search keyword exactly as DataLab shows it
categoryId	50000190	Naver Shopping category ID
categoryName / categoryPath	스킨케어 / 화장품/미용 > 스킨케어	Category name and full path
categoryLevel	2	Depth in the category tree (1 = top level)
startDate / endDate	2026-09-16 / 2026-10-15	Period actually queried
periodPreset	last30days	Preset used, or custom
device / gender / ages	mo / f / 20,30	Filters used (all when not filtered)
previousRank / rankChange / rankTrend	5 / 3 / up	Only with compareWithPreviousPeriod. rankChange > 0 means the keyword climbed. new means it was not in the previous top N.
previousPeriod	2026-10-02..2026-10-08	The comparison period
naverShoppingUrl	link	Naver Shopping search results for the keyword
datalabKeywordUrl	link	DataLab keyword page for the keyword in this category
collectedAt	ISO timestamp	When the run collected the data

Trend rows (type: "categoryTrend"), one per category when includeTrend is on: series ([{period, ratio, isPartial}]), points, peakPeriod, peakRatio, averageRatio, deviceBreakdown, genderBreakdown, ageBreakdown (each [{code, label, ratio, sharePct}]), plus the same category and filter fields.

Category rows (type: "category", browser mode only): categoryId, categoryName, categoryPath, categoryLevel, isLeaf, parentId.

Error rows (type: "error"): input, error, suggestions. They are never charged.

How to use

1. Click Try for free and open the Input tab.
2. Add one or more categories. Use IDs, names such as 패션의류 or 스킨케어, or paths such as 화장품/미용 > 스킨케어 > 크림. Not sure which ones exist? Turn on List categories, run once for free, and copy the IDs.
3. Choose a period preset (last 7, 30 or 90 days, or last year), or set a custom start and end date.
4. Optionally set the device, gender and age filters, the number of keywords per category, the previous-period comparison and the click trend.
5. Click Start. Download the results as JSON, CSV or Excel, or read them through the API.

{
  "categories": ["패션의류", "화장품/미용 > 스킨케어", "50000167"],
  "period": "last30days",
  "device": "mo",
  "gender": "f",
  "ages": ["20", "30"],
  "maxRank": 100,
  "compareWithPreviousPeriod": true,
  "includeTrend": true,
  "timeUnit": "week"
}

To browse the category tree for free:

{ "listCategories": true, "categories": ["화장품/미용"], "listDepth": 2 }

Output example

Keyword row (taken from a real run and trimmed):

{
  "type": "keywordRank",
  "status": "ok",
  "rank": 2,
  "keyword": "트위드자켓",
  "categoryId": 50000000,
  "categoryName": "패션의류",
  "categoryPath": "패션의류",
  "categoryLevel": 1,
  "startDate": "2026-10-09",
  "endDate": "2026-10-15",
  "periodPreset": "last7days",
  "timeUnit": "date",
  "device": "all",
  "gender": "all",
  "ages": "all",
  "previousRank": 5,
  "rankChange": 3,
  "rankTrend": "up",
  "previousPeriod": "2026-10-02..2026-10-08",
  "naverShoppingUrl": "https://search.shopping.naver.com/search/all?query=%ED%8A%B8%EC%9C%84%EB%93%9C%EC%9E%90%EC%BC%93",
  "collectedAt": "2026-10-16T08:23:46.852Z"
}

Trend row (series and age split trimmed):

{
  "type": "categoryTrend",
  "status": "ok",
  "categoryId": 50000190,
  "categoryPath": "화장품/미용 > 스킨케어",
  "startDate": "2026-07-18",
  "endDate": "2026-10-15",
  "timeUnit": "week",
  "points": 14,
  "peakPeriod": "2026-09-07",
  "peakRatio": 100,
  "averageRatio": 87.3,
  "series": [
    { "period": "2026-07-13", "ratio": 83.119, "isPartial": true },
    { "period": "2026-07-20", "ratio": 88.303, "isPartial": false },
    { "period": "2026-10-05", "ratio": 97.043, "isPartial": false },
    { "period": "2026-10-12", "ratio": 54.474, "isPartial": true }
  ],
  "deviceBreakdown": [
    { "code": "mo", "label": "모바일", "ratio": 100, "sharePct": 87.6 },
    { "code": "pc", "label": "PC", "ratio": 14.14, "sharePct": 12.4 }
  ],
  "genderBreakdown": [
    { "code": "f", "label": "여성", "ratio": 100, "sharePct": 80.5 },
    { "code": "m", "label": "남성", "ratio": 24.22, "sharePct": 19.5 }
  ],
  "ageBreakdown": [
    { "code": "40", "label": "40대", "ratio": 100, "sharePct": 32.4 },
    { "code": "50", "label": "50대", "ratio": 99.53, "sharePct": 32.3 }
  ]
}

Pricing

This Actor uses pay-per-event pricing:

Event	Price	When it is charged
keyword-rank-row	$0.003 per keyword row ($3 per 1,000)	Once for each keyword row in your dataset
trend-series	$0.01 per trend series	Once for each category trend row (the device, gender and age breakdown is included)

Worked examples:

• The prefilled run (1 category, top 20 keywords) costs 20 × $0.003 = $0.06.
• 10 categories × top 100 keywords = 1,000 rows = $3.00.
• 5 categories × the full top 500, with a click trend for each = 2,500 × $0.003 + 5 × $0.01 = $7.55.
• A weekly schedule for 3 categories × top 100 with trends costs $0.93 per run, about $4 per month.

Not charged: error rows (unknown category names or IDs, failed requests), empty results, the category browser (listCategories), the previous-period comparison requests, the audience breakdown requests, and retries. You can set a maximum cost per run in the run options. The Actor stops cleanly when it reaches that limit.

Integrations and scheduling

• Schedules: run the Actor daily or weekly to track rising keywords. Use compareWithPreviousPeriod to see movement without storing past runs.
• Webhooks: trigger your pipeline (Slack alert, Google Sheets import, data warehouse load) when a run finishes.
• API and SDKs: start runs and download datasets from any language with the Apify API, or the JavaScript and Python clients.
• MCP and AI agents: expose the Actor to AI agents through the Apify MCP server, so an assistant can answer questions like "what are women in their 30s searching for in 스킨케어 this month?"
• Integrations: connect to Make, Zapier, n8n, Google Sheets, Airbyte and more from the Integrations tab.

FAQ and limitations

Are the ratios search volumes? No. As on DataLab, every ratio is a relative index. In each series the highest point for the chosen category, period and filters is 100, and the other points are scaled against it. Device, gender and age ratios are also relative to the largest group, and sharePct converts them into percentages of the total. You can compare shapes and shares, but you cannot convert them into absolute click or search counts. Two separately requested series are each scaled to their own peak.

What date ranges are supported? DataLab Shopping Insight has data from 2017-08-01 to yesterday (Korea time), with at most 1 year per query. End dates after yesterday are moved back to yesterday. Ranges longer than one year are rejected with a clear message, so split them into several runs. Rankings always cover the whole period, and timeUnit only sets the trend granularity. With weekly or monthly trends, the first and last buckets can cover only part of a week or month. Those buckets are flagged with isPartial: true and usually look lower.

How many keywords can I get? Up to 500 per category, the same top 500 that DataLab offers. Narrow filter combinations or small categories can return fewer.

How do category names resolve? Top-level and second-level names work on their own (패션의류, 스킨케어). Deeper categories need a path with > (화장품/미용 > 스킨케어 > 크림) or an ID. Matching ignores spaces and slashes. If a name is ambiguous or unknown, you get an error row with suggestions, and you are not charged.

Is this the Naver Shopping product ranking? No. Naver states that DataLab statistics are separate from the Naver Shopping product ranking. This Actor reports search keyword popularity, not product positions.

Translation? Keywords are returned in Korean exactly as DataLab shows them. No romanization or translation is added.

Responsible use. The Actor reads public, aggregated statistics that Naver DataLab publishes without login. It collects no personal data, accounts or user-level information. It uses a conservative default rate limit (2 requests per second, adjustable from 1 to 4). Use the data in line with Naver's terms of service and your local laws, and cite Naver DataLab as the source when you publish it.

한국어 안내

네이버 데이터랩 쇼핑인사이트(분야 통계)의 분야별 인기 검색어 TOP 500, 클릭량 추이, 기기·성별·연령 비중을 한 번에 수집합니다. 카테고리는 ID 외에 패션의류, 스킨케어, 화장품/미용 > 스킨케어 > 크림처럼 이름이나 경로로 입력해도 됩니다. 기기(PC/모바일), 성별, 연령(10대~60대 이상) 필터와 기간(2017-08-01부터 어제까지, 1회 최대 1년)도 웹과 동일하게 지정할 수 있습니다. compareWithPreviousPeriod를 켜면 직전 같은 길이의 기간과 비교한 순위 변동(상승/하락/유지/신규)도 함께 나옵니다.

• 셀러: 내 카테고리에서 지금 뜨는 검색어를 매주 받아 상품명·태그·키워드 광고에 반영할 수 있습니다. 순위 변동으로 막 오르기 시작한 키워드를 먼저 잡고, 시즌 시작 시점은 클릭 추이로 확인합니다.
• 브랜드: 우리 브랜드명이나 경쟁 브랜드명이 카테고리 TOP 500의 몇 위에 있는지 추적합니다. 20·30대 여성처럼 타깃 세그먼트별 순위를 비교해 신제품 기획과 포지셔닝에 활용할 수 있습니다.
• 마케터: 카테고리 클릭 추이와 기기·성별·연령 비중으로 캠페인 시점과 타깃을 정합니다. 스케줄 실행 결과를 Google Sheets나 BI 대시보드에 자동으로 연결할 수 있습니다.

참고: 모든 수치는 데이터랩과 같은 상대 지수(기간 내 최댓값 = 100)이며 절대 검색량이 아닙니다. 공개된 집계 통계만 수집하며 개인정보는 다루지 않습니다. 가격은 키워드 행당 $0.003, 추이 시리즈당 $0.01이며, 오류 행·빈 결과·카테고리 목록 조회는 과금되지 않습니다.