Platform · Naver

Naver Data Scraping

Q: How do you handle paid placement?

We record placement_type as paid, organic or undetermined, and undetermined is a real value used where the platform does not disclose it clearly. Conflating the two gives you a visibility metric that mixes prominence bought with prominence earned, and the mix is not constant, so you cannot correct for it later.

A price comparison layer with a marketplace inside it. The record is a merchant offer, and treating it as a product loses the whole picture.

Naver data scraping collects Naver Shopping price comparison listings, merchant offers, SmartStore products and review content. The structural point: Naver Shopping is primarily a comparison layer where one product carries many merchant offers at different prices, so the record is the offer — the same discipline our Idealo work uses.

An inquiry last quarter asked for product reviews across Coupang, Naver and OliveYoung together. That is the right way to see the Korean market, and Naver is the one that needs a different record shape.

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naver_offers.jsonl LIVE FEED

{"product_key":"aw-kr-4471","naver_product_id":"np-7712049", "merchant_id":"m-44120","merchant_name":"Example Official", "offer_price":28900,"currency":"KRW", "offer_count":34, "price_min_across_offers":24500, "price_spread_pct":18.0, "placement_type":"organic", "position":3,"search_term":"토너", "is_smartstore":false} {"product_key":"aw-kr-4471","merchant_id":"m-99021", "offer_price":31200,"position":1, "placement_type":"paid", "note":"position 1 and NOT cheapest — prominence bought, not earned"} {"product_key":"aw-kr-8890", "placement_type":"undetermined", "note":"platform did not disclose clearly — not guessed"}

3 of 4,412,090 OFFER rowsrecord = merchant offer, not product · schema v1.0

Independence and trademarks. Actowiz Solutions is not affiliated with, endorsed by or connected to Naver or its owners. Naver and related marks belong to their respective owners, used here only to name the publicly accessible source this service collects from.

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Naver at a glance

How we handle Naver specifically

Platform-specific handling, not a generic retail template pointed at a different domain.

Platform: Naver Shopping plus SmartStore, South Korea
The record: A merchant offer, not a product
Consequence: One product page can carry dozens of offers at different prices
Offer type: Paid placement and organic offers must be distinguished
Script: Hangul retained. Matching runs on attributes and images, not titles
Reviews: Content and ratings without reviewer identity
SmartStore: Merchant-operated storefronts, a distinct surface
Refresh: Daily standard; sub-daily where offer competition is the question

Platform specifics

What makes Naver different from a marketplace

These are the reasons a Naver dataset needs its own handling rather than a shared retail schema.

One product, many offers, and the lowest is not always the shown one

Naver Shopping aggregates offers from many merchants against a single product. What appears prominently is not purely the cheapest — placement, merchant rating, delivery terms and paid promotion all affect what a shopper sees first.

Collecting one price per product throws away the spread, which is the actual competitive picture.
The prominent offer is not the market price, and using it as one overstates or understates depending on placement.
Merchant identity matters for a brand watching its own distribution.

We deliver one record per offer with merchant_id, the offer price, delivery terms and the position, plus offer_count and price_min_across_offers so the spread is visible without aggregating it away.

This is the same structure our Idealo service uses, and for the same reason: on a comparison platform the offer is the commercial unit.

Paid placement is a separate signal from price competitiveness

Naver carries paid placement alongside organic comparison results, and the two look similar in a listing.

Conflating them produces a badly wrong read of the market: a merchant appearing prominently because they paid for it is not the same as a merchant appearing because they are cheapest.

We record placement_type as paid, organic or undetermined.
Undetermined is a real value, used where the platform does not disclose it clearly, rather than guessing.
Position is recorded with the search term, since position without the query is meaningless.

A vendor delivering position without placement type is giving you a visibility metric that mixes two different things, and the mix is not constant.

Hangul, reviews, and the boundary on reviewer data

Matching

Korean product titles do not tokenise the way Latin-script titles do, and merchant-written titles on Naver are frequently keyword-stuffed. Title-similarity matching produces poor results.

We match on identifiers where they exist, normalised attributes where they do not, and perceptual image hashes as a weight. Titles are retained exactly and used as evidence rather than as the matching signal. We report the match rate on your own Korean product set in the pilot rather than quoting a site-wide average.

Reviews

The inquiry behind this page asked for reviews across three Korean sites. We collect review text, ratings, timestamps and counts.

We do not collect reviewer names, profiles, purchase histories or any identifier that ties a review to a person. That boundary is the same in every market and it matters here because Korea's PIPA is among the stricter regimes, with meaningful penalties.

Scope

What we collect on Naver, and what we do not

The right column matters more than the left. Anyone can list fields; the limits are what tell you whether the dataset will hold up.

✅ What we collect

One record per merchant offer, with merchant identity and offer price
Offer count and price spread across offers for the same product
Placement type — paid, organic or undetermined
Position recorded with the search term it was observed for
SmartStore products as a distinct surface, flagged
Hangul titles retained exactly, matching on attributes and image hashes
Review text, rating, timestamp and counts
Delivery terms and any shipping cost per offer
Category as the platform presents it

❌ What we do not, and why

One price per product, which discards the offer spread
Placement type guessed where the platform does not disclose it
Reviewer names, profiles, purchase histories or identifiers
Machine translation written into title or review fields
Sales volumes or merchant revenue

Core Naver fields

The full dictionary is agreed during scoping. These are the fields specific to this platform.

Field	What it is on this platform
product_key / naver_product_id	Our matched identity and the platform identifier
merchant_id / merchant_name	The offer's merchant, a commercial entity
offer_price / currency	This merchant's price for this product
offer_count / price_min_across_offers / price_spread_pct	So the spread is visible
placement_type	paid, organic or undetermined — never guessed
position / search_term	Position is meaningless without the query it was observed for
is_smartstore	SmartStore is a distinct surface
title_ko	Hangul retained exactly, used as evidence not as the match key
review_text / review_rating / review_date	Content without reviewer identity
delivery_terms / shipping_cost	Per offer, since they differ by merchant
observed_at	Timestamp

Use cases

What teams do with Naver data

Korean market price monitoring across offers

One record per merchant offer with the spread visible, so a brand sees the real distribution of prices rather than a single figure that depends on placement.

Distribution and unauthorised seller monitoring

Merchant identity per offer against your authorised list, showing who is selling your products on Naver and at what price. The authorisation determination stays yours.

Visibility separated from price competitiveness

Placement type recorded, so paid prominence is not mistaken for competitive pricing — two signals that look identical in a listing and mean opposite things.

Three-site Korean review coverage

Naver alongside Coupang and OliveYoung on one schema, which is how the Korean market is actually experienced and how the inquiry behind this page was framed.

The 24-hour sample — run on your sources, not ours

Send us a Naver item or category list. We run real collection against it and return the output within 24 hours, with the platform-specific fields populated so you can check them yourself rather than take our word for it.

Real extraction from your actual sources
Returned within 24 hours
Coverage and QA note included
You keep the data either way
No card, no trial clock
Named engineer on the call

Get my free sample Book a 20-min scoping call Reply within one business day. Reference calls available under NDA.

How we engage

Three ways to engage us

Same collection pipeline and QA underneath. The difference is who holds the schedule and how the data reaches you.

Managed service (most common)

We own the collection, the QA and the delivery. You receive clean data on a schedule and never touch a scraper.

Dedicated engineer assigned to your account
Site changes fixed by us, not reported to you
Scheduled delivery to your warehouse or S3
Named contact on Slack or email

Best fit: Teams who need the data, not the infrastructure.

API access

The same collection pipeline exposed as an authenticated REST endpoint your systems query directly.

On-demand and scheduled endpoints
Rate limits agreed to your load profile
Sandbox keys for integration testing
Versioned schema with deprecation notice

Best fit: Product and engineering teams building on live data.

One-time or project extraction

A defined pull for a specific question — market sizing, diligence, a pitch, a one-off audit.

Fixed scope agreed in writing upfront
Single delivery with full QA report
Methodology documented for your records
Converts to managed if you want continuity

Best fit: Research, strategy and diligence work with a deadline.

Pricing

Every engagement is quoted individually, because the honest answer depends on your scope: how many sources, how many records, how often, and how the data reaches you. We scope it with you, run a free pilot on your own sources, and then quote a fixed monthly figure — no per-request metering and no overage billing when volumes move. Request a quote and you will have a number after one call.

Naver is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. Naver data becomes useful when it sits next to the competitor set on one schema, refreshed on one schedule, so a price index or availability comparison is genuinely like-for-like.

That is what ecommerce data scraping covers, and a Naver-only engagement can be expanded into it without rebuilding. If you already know you need several platforms, start there instead — it is the same pipeline and usually the better scoping conversation.

FAQ

Naver data scraping: frequently asked questions

Platform-specific questions, including what cannot be collected here.

Because Naver Shopping is a comparison layer where one product carries many merchant offers at different prices. Collecting one price per product discards the spread, which is the actual competitive picture.

We deliver per-offer records plus the count and spread, so both the detail and the summary are available without aggregating away the thing you needed.

We record placement_type as paid, organic or undetermined, and undetermined is a real value used where the platform does not disclose it clearly.

Conflating the two gives you a visibility metric that mixes prominence bought with prominence earned, and the mix is not constant, so you cannot correct for it later.

No. Review text, ratings, timestamps and counts, but never reviewer names, profiles, purchase histories or any identifier tying a review to a person.

That boundary applies in every market and it matters here because Korea's PIPA is among the stricter regimes with meaningful penalties attached.

Yes, compounded by merchant-written titles that are frequently keyword-stuffed. Title-similarity matching performs poorly on both counts.

We match on identifiers, normalised attributes and perceptual image hashes, retain titles exactly as evidence, and report the real match rate on your own product set in the pilot.

Yes, and that is how the inquiry behind this page was framed. Those three cover most of how Korean shoppers actually research and buy.

One schema across all three means cross-site comparison is a join rather than a reconciliation project.

We quote individually. The distinctive driver is offer multiplication — one product carrying dozens of offers means record volume runs well above product count.

One scoping call, a free pilot within 24 hours, then a fixed monthly quote. Request a quote.

See real Naver data before you commit to anything

Send us an item or category list. We return the output within 24 hours with the platform-specific fields populated.

Get a free sample Talk to an engineer

Free pilot, no card, no obligation. If we cannot collect a field you need on this platform, the sample shows you that too.

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