Wildberries Seller Analytics: Аналитика ВБ

Get supplier intelligence and seller analytics for Wildberries in one structured dossier. Search a seller ID, store URL, or product keyword to collect public legal-entity data and catalog metrics for sourcing, competitor research, supplier verification, and scheduled monitoring. This Wildberries seller analytics tool, or аналитика продавцов Вайлдберриз, exports clean records for Excel, Google Sheets, APIs, and automated workflows.

Profile any Wildberries seller: legal entity plus catalog intelligence. Turn a seller ID, store URL, or keyword into a full dossier: the registered company (name, tax ID, address, trademark) plus catalog stats like product count, brands, price range, reviews, rating, and stock. Built for competitor research, sourcing, and lead generation.
Wildberries (Вайлдберриз) suite   •  Products, sellers, reviews, prices, resale, and niche analysis
Product Search Products, prices, and full catalogs	Seller Scraper ➤ You are here	Reviews Scraper Ratings, text and buyer photos
Price Tracker Price and search-rank monitoring	Resale Scraper Resale and secondhand listings	Niche Analyzer Competition, price and demand

Get the full company profile and catalog intelligence behind any Wildberries seller in seconds. Turn a seller ID, store URL, or search keyword into a complete dossier: the registered company (name, tax ID, legal address, trademark) plus catalog stats like product count, brands carried, price distribution, review concentration, discounts, and stock levels. Built for competitor research, sourcing, and lead generation on Russia's largest marketplace. Runs fast and reliably with no login, and exports clean data you can open in Excel, Google Sheets, or your own tools.

🔍 Wildberries seller analytics and supplier intelligence

This Actor profiles sellers on Wildberries, the largest online marketplace in Russia and the CIS. For each seller it combines two layers of public data into one row: the company record (display name, full legal name, tax IDs, registered address, trademark) and catalog statistics computed across the seller's products (how many products they list, which brands they carry, their price range, total customer reviews, average rating, and units in stock).

Most scrapers only tag each product with a seller name. This one returns a true per-seller intelligence record, so you get the whole picture of a store in a single line of data. Its portfolio signals are transparent measurements, not a black-box seller score: every catalog metric is calculated only from scannedProducts, with catalogCoveragePct showing how representative that sample is.

⚡ A whole store in one row. No account or login required. Get a seller's legal entity plus catalog stats (products, brands, price band, reviews, inventory) in a single dossier, ready to schedule.

💡 Why use Wildberries seller analytics?

"I source products to resell, and I need to size up a competitor fast. One run tells me how many products a seller lists, their price band, and how many reviews they pull, so I know in seconds whether they are a serious player."

"I build prospect lists for a B2B service. I feed in a category keyword and get back every seller behind the top products, complete with their legal company name and tax ID for outreach and verification."

"I vet suppliers before placing wholesale orders. Seeing the registered legal entity and address next to their catalog size and ratings saves me from dealing with fly-by-night stores."

"I track a market over time. I schedule this weekly and watch how a seller's product count, pricing, and review totals move, which is a clean demand signal for the whole category."

🚀 How to use Wildberries seller analytics

1. Open the input form.
2. Add one or more sources: seller IDs, seller store URLs, or search keywords.
3. Optional: turn on "Include product rows" for a full per-product breakdown, or set a region for local pricing.
4. For scheduled monitoring, choose "Snapshot with changes" or "Changes only" and keep the same monitoring state name.
5. Click Start. When the run finishes, download the data as JSON, CSV, Excel, or HTML, or pull it from your account.

⚙️ Input

At least one source is required: seller IDs, seller URLs, or search keywords.

Field	Type	Required	Description
sellerIds	array of IDs	No	Wildberries seller IDs (the number in a seller store URL).
sellerUrls	array of URLs	No	Seller store page URLs. The ID is read from each automatically.
searchQueries	array of text	No	Product keywords. The Actor finds the sellers behind matching products and profiles them.
includeProducts	boolean	No	Also return one row per product in each seller's catalog.
maxProductsPerSeller	integer	No	How many catalog products to scan per seller when computing stats. Higher is more accurate and costs more.
maxSellersPerSearch	integer	No	Cap on how many distinct sellers to profile per keyword.
maxItems	integer	No	Hard cap on total rows returned.
region	enum	No	Region used for catalog pricing and availability.
outputMode	enum	No	snapshot preserves legacy rows; snapshot_with_changes adds change metadata to every seller; changes_only emits only new or updated sellers.
monitoringStateName	string	No	Isolates monitoring state for one client, schedule, or project.
emitBaseline	boolean	No	Emit current sellers as NEW on the first changes-only run. Disable for a silent baseline.

changes_only cannot be combined with product rows. Product rows are snapshot output, while monitoring compares seller dossiers.

📦 Output

Each seller becomes one dossier row. You can download the dataset as JSON, CSV, Excel, or HTML.

{
  "recordType": "seller",
  "supplierId": "1418867",
  "supplierName": "OOO TAYKONG",
  "supplierFullName": "OBSHCHESTVO S OGRANICHENNOY OTVETSTVENNOSTYU TAYKONG",
  "inn": "0800011139",
  "kpp": "771401001",
  "ogrn": "1230800005523",
  "legalAddress": "125315, Moscow, proezd Bolshoy Koptevsky, 3, str. 1",
  "trademark": "JOYCITY",
  "sellerUrl": "https://www.wildberries.ru/seller/1418867",
  "productCount": 622,
  "scannedProducts": 200,
  "brandCount": 1,
  "brands": ["JOYCITY"],
  "priceMinRub": 388,
  "priceMaxRub": 3163,
  "priceAvgRub": 1559,
  "totalReviews": 477847,
  "avgRating": 5,
  "totalInventory": 10119,
  "inStockProducts": 200,
  "scrapedAt": "2026-06-29T19:42:30.995Z"
}

📊 Data table

Field	Type	Description
supplierId	string	Wildberries seller ID.
supplierName	string	Seller display name.
supplierFullName	string	Full registered legal name.
inn	string	Tax identification number, when published.
kpp	string	Tax registration reason code, when published.
ogrn	string	State registration number, when published.
legalAddress	string	Registered legal address.
trademark	string	Registered trademark.
sellerUrl	string	Link to the seller store page.
productCount	integer	Total products in the seller's catalog.
scannedProducts	integer	Products actually scanned for the stats below.
brandCount	integer	Number of distinct brands the seller carries.
brands	array	List of brands carried.
topBrand, topBrandProducts, topBrandSharePct	text, integer, number	The most represented brand in the scanned sample, its product count, and sample share.
priceMinRub	integer	Lowest product price in rubles.
priceMaxRub	integer	Highest product price in rubles.
priceAvgRub	integer	Average product price in rubles.
priceP25Rub, priceMedianRub, priceP75Rub	number	Lower-quartile, median, and upper-quartile prices in the scanned sample.
totalReviews	integer	Sum of review counts across products, a demand signal.
avgRating	number	Average product rating.
reviewConcentrationTop10Pct	number	Percentage of observed reviews held by the ten most-reviewed scanned products.
totalInventory	integer	Total units in stock across scanned products.
inStockProducts	integer	Number of scanned products currently in stock.
inventoryObservedProducts, outOfStockProducts, outOfStockRatePct	integer, integer, number	Products with a published inventory count, the zero-inventory count, and its percentage.
inventoryConcentrationTop10Pct	number	Percentage of observed inventory held by the ten highest-stock scanned products.
discountedProductRatePct	number	Percentage of scanned products with a published positive discount.
catalogCoveragePct	number	Percentage of the advertised catalog represented by the scanned sample.
catalogComplete	boolean	Whether the scan reached the end of the catalog rather than the configured product cap.
pagesScanned	integer	Number of validated catalog pages used for the dossier.
partialReason	string	MAX_PRODUCTS_PER_SELLER only when the requested sampling cap stopped the scan.
catalogScanComplete, catalogPagesScanned, catalogScanStopReason	legacy aliases	Retained for compatibility; use the fields above in new integrations.
scrapedAt	string	Timestamp of the run.

With product rows enabled, extra rows carry per-product name, brand, priceRub, rating, feedbacks, and productUrl.

🔄 Seller monitoring and deltas

Monitoring state is scoped by seller ID, region destination, and maxProductsPerSeller, so changing coverage does not compare unlike samples. Wildberries does not provide a reliable seller-expiry signal here, so this Actor intentionally never invents an expired state.

• snapshot is the backward-compatible default and returns the same seller/product rows as before.
• snapshot_with_changes returns every seller with NEW, UPDATED, or UNCHANGED.
• changes_only returns only NEW and UPDATED seller dossiers. Set emitBaseline to false when the first run should save state without emitting paid baseline rows.

Updated rows can include productCountDelta, priceAvgRubDelta, priceMedianRubDelta, totalReviewsDelta, totalInventoryDelta, inStockRatePctDelta, outOfStockRatePctDelta, topBrandSharePctDelta, previousTopBrand, brandsAdded, and brandsRemoved, plus firstSeenAt and lastSeenAt.

The comparison fingerprint is limited to those auditable catalog metrics and brands. A temporary omission from the separate legal-profile endpoint therefore cannot create a false UPDATED event. Existing monitoring states migrate without classifying a seller as updated solely because these newer fields are now available. A successful changes_only run with zero rows simply means no monitored seller changed. It is not an error.

Malformed, blocked, truncated, or internally inconsistent catalog pages are retried and never converted into a paid partial dossier. Monitoring state advances only after a fully validated dossier is handled, and no state is advanced when an output row cannot be stored. Legal-profile fields remain best-effort because Wildberries legitimately omits them for some sellers.

💰 How much does Wildberries seller analytics cost?

Pricing is pay per result, so you only pay for the data you actually get. A seller dossier costs $0.00699. There are no subscriptions and no monthly fees, and runs that return nothing cost nothing. Each seller dossier scans that seller's catalog, so you can lower the products scanned per seller for cheaper, faster runs when you only need headline stats.

⭐ Enjoying Wildberries Seller Scraper?

⭐ ⭐ ⭐ ⭐ ⭐ Glad this Actor helped you size up a Wildberries seller in one run instead of digging through pages by hand. A 5-star rating takes 10 seconds and helps other sourcing teams and competitor researchers find it. Your feedback also tells us what to build next.

★  Rate this Actor on Apify

✨ Tips

• Start with a smaller products-per-seller value for fast headline stats, then raise it when you need precise price and inventory aggregates on large catalogs.
• Use search keywords to map every seller in a category in one run.
• Turn on product rows only when you need the full catalog, since it multiplies the number of rows returned.
• Keep the region, products-per-seller cap, and monitoring state name stable across scheduled comparisons.

❓ FAQ

Is it legal to scrape Wildberries seller data?

This Actor collects publicly available marketplace and business-registry information that Wildberries already shows on its own pages. You are responsible for following Wildberries' terms and any data-protection laws that apply to how you use the data.

Why are some legal fields empty for a seller?

Wildberries publishes different details for different sellers. Sole proprietors, for example, often have no registration code or legal address. Missing values are left out rather than filled with fake placeholders, so every field you see is real.

Do I need an account or login for the target site?

No. You do not need any Wildberries login or cookies. Just provide sellers or keywords and run it.

Что такое аналитика продавцов Вайлдберриз?

Аналитика продавцов Вайлдберриз is seller-level marketplace research. This Actor returns a public seller's legal entity and catalog metrics, including product count, brands, price range, reviews, rating, and inventory, in one exportable dossier.

Can I use this as a парсер продавцов Wildberries?

Yes. Provide seller IDs, Wildberries store URLs, or product keywords. The Actor discovers and profiles matching sellers, then returns structured data for supplier research, competitor analysis, or monitoring.

Can I get every product for a seller, not just the stats?

Yes. Turn on the product rows option and the Actor returns one row per product in the seller's catalog alongside the dossier.

Found a problem or need another field?

Open a ticket on the Issues tab of this Actor. Custom data fields and tailored solutions are available on request.

🔗 Other actors

• Wildberries Resale Scraper: Ресейл ВБ ↗ - searches Wildberries' secondhand marketplace for used listings and prices.
• Wildberries Scraper: Product search, catalog, and prices ↗ - searches Wildberries by keyword, category, or NM-ID for live prices and catalogs.
• Kaspi Product Scraper: Kaspi Магазин (Казахстан) ↗ - pulls product listings and prices from Kazakhstan's Kaspi marketplace.
• Detmir Scraper: Парсер Детский Мир ↗ - collects product and pricing data from the Detsky Mir marketplace.
• Avito Auto Scraper: Парсер Авито Авто ↗ - collects used car listings and prices from Avito.