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Service · Seller & vendor

Seller & Vendor Data Scraping Services

That name who is actually selling your products.

Seller and vendor monitoring is a managed service in which Actowiz Solutions tracks who is listing your products across marketplaces, identifies unauthorised and returning resellers, and delivers evidence-grade records your brand protection team can act on.

Unauthorised sellers do not stop when you send one takedown. They come back under a new account name. The service is built around recognising them when they do.

Free pilot on your own sources, returned in 48 hours. No card, no trial clock — and you keep the sample data either way.

200+ marketplaces covered Unauthorised reseller detection Free pilot sample in 48 hours
seller_profiles_2026-08-05.json LIVE FEED
{"seller_id":"A2K9XQ4L7BNVZM", "marketplace":"amazon.com", "store_name":"TechValue Depot", "business_name":"Techvalue Trading LLC", "registered_address":"Wilmington, DE, US", "feedback_count":18402,"rating_12mo":94, "first_seen":"2023-11-02", "listing_count":2841, "your_brand_skus":17, "authorised":false, "fulfilment":"FBA", "buybox_win_rate":0.41, "avg_price_vs_map":-8.3, "map_violations_30d":23, "top_categories":["Electronics","Home"]}
1 of 6,318 seller profiles · run 2026-08-05T05:00Zprofile completeness 97.4% · schema v3.1

Key facts at a glance

What it is
Structured profiles of marketplace sellers including identity, history, inventory breadth and pricing behaviour
Marketplace coverage
200+ marketplaces including Amazon, Walmart, eBay, Flipkart, MercadoLibre, Noon, Coupang, Shopee
Seller identity
Storefront name, registered business name and address where the marketplace publishes it
Behavioural fields
Buy Box win rate, price positioning versus MAP, violation frequency, listing churn
Refresh options
Daily for offer-level tracking; weekly for full seller profile refresh
Authorisation matching
Cross-referenced against your authorised distributor list to flag unauthorised sellers
Delivery formats
JSON, CSV, Parquet; S3, GCS, Azure, SFTP, Snowflake, BigQuery, REST API
Who it's for
Brand protection, channel management, marketplace category teams, investment researchers
200+marketplaces coveredseller-level extraction
Dailyoffer and Buy Box trackingseller rotation captured
97.4%profile field completenesswhere publicly shown
30-daybehavioural history windowsrolling metrics

Key takeaways

  • What it is: Structured profiles of marketplace sellers including identity, history, inventory breadth and pricing behaviour
  • Marketplace coverage: 200+ marketplaces including Amazon, Walmart, eBay, Flipkart, MercadoLibre, Noon, Coupang, Shopee
  • Seller identity: Storefront name, registered business name and address where the marketplace publishes it
  • Behavioural fields: Buy Box win rate, price positioning versus MAP, violation frequency, listing churn
  • Refresh options: Daily for offer-level tracking; weekly for full seller profile refresh
  • Authorisation matching: Cross-referenced against your authorised distributor list to flag unauthorised sellers

Last verified 5 August 2026 by the Actowiz Solutions Data Engineering team.

Definition

What is seller and vendor data, and why is seller identity so hard to establish?

Seller and vendor data describes the supply side of a marketplace rather than the products on it. For any given listing, it answers: who is offering this, how long have they operated, what else do they sell, how do buyers rate them, how do they fulfil, how do they price, and how often do they win the Buy Box.

For brands, the critical question is narrower and more urgent: which of these sellers is authorised, and which is not. Grey-market and unauthorised resellers erode price integrity, undermine authorised partner margins, and create warranty and counterfeit exposure. But you cannot enforce against a seller you cannot identify.

Why seller identification is genuinely difficult

Marketplaces publish seller information inconsistently. A storefront name is almost always visible; a registered legal name and address sometimes are, depending on marketplace and jurisdiction — EU and UK transparency rules have expanded this considerably. Sellers also churn deliberately: a suspended account reappears under a new storefront name with the same inventory pattern and the same fulfilment footprint.

Actowiz builds seller profiles that persist across runs and link behavioural fingerprints: inventory overlap, pricing patterns, first-seen dates and category concentration. When a suspended seller returns under a new name, the inventory signature usually gives them away. We surface those candidate links with the supporting evidence and let your brand protection team make the judgement — we flag patterns, we do not assert legal conclusions about identity.

What we extract

Six seller and vendor data dimensions

Profile-level and offer-level data are delivered as joinable tables, so you can analyse sellers as entities or track individual offers over time.

Seller identity

Everything the marketplace publicly discloses about who the merchant is.

  • Storefront name and seller ID
  • Registered business name and address
  • Country of operation and VAT/tax ID
  • Storefront URL and about text

Reputation & history

Trust signals and operating tenure, which reveal churn and new-account patterns.

  • Feedback count and rating windows
  • Rating trend over 30/90/365 days
  • First-seen and last-seen dates
  • Negative feedback themes

Inventory footprint

What the seller carries, which is the strongest fingerprint for identifying returning accounts.

  • Total listing count
  • Category concentration
  • Count of your brand SKUs carried
  • New listing and delisting velocity

Buy Box & competition

How effectively the seller competes for the sale, tracked through the day.

  • Buy Box win rate per SKU
  • Rotation frequency and timing
  • Offer count competed against
  • Fulfilment type (FBA/FBM/3P)

Pricing behaviour

The pattern of how a seller prices, not just their current price.

  • Average price position vs MAP
  • Undercut frequency and depth
  • Repricing cadence detection
  • MAP violation count by window

Authorisation & risk

The layer that turns seller data into enforcement action.

  • Match against your authorised list
  • Unauthorised seller flagging
  • Counterfeit risk indicators
  • Candidate returning-account links
Service scope

What the seller monitoring service includes

Detection, evidence capture and repeat-offender fingerprinting as one managed workflow.

✓ Included in every engagement

  • Authorised versus unauthorised seller classification against your reseller list
  • Returning-account fingerprinting across marketplace re-registrations
  • Timestamped evidence capture suitable for enforcement filings
  • New seller alerting within one collection cycle of first listing
  • Source discovery, scoping and a written collection plan
  • Free pilot on your own sources before any commitment
  • Full pipeline build, hosting and proxy infrastructure
  • Schema design, validation and sampled human QA on every run
  • Ongoing maintenance when source layouts change — our cost, not yours
  • Delivery to your warehouse, bucket, SFTP or API endpoint
  • Documented methodology and compliance notes for your legal review

× Not included — stated upfront

  • Legal takedown filing — we supply evidence, your counsel files
  • Seller identity behind marketplace anonymisation we cannot lawfully pierce
  • Marketplace-internal seller performance data
  • Anything behind a login, paywall or credentialed session
  • Personal data beyond a documented lawful basis
  • Licensed third-party datasets we do not hold rights to
  • Guarantees about fields a source simply does not publish
Schema

Seller and vendor data fields you receive

Every engagement delivers a documented schema. These are the core fields; the full dictionary is agreed during scoping.

Deliverable schema — seller data v3.1 — core fields shown; full dictionary has 85+ fields
Field Type What it captures Refresh
seller_id / marketplace string Marketplace-native seller identifier and normalised marketplace domain Every run
store_name / business_name string Public storefront name and registered legal name where disclosed Weekly
registered_address / country string Business address and operating country where the marketplace publishes it Weekly
feedback_count / rating_12mo int Cumulative feedback volume and rating percentage over trailing windows Weekly
first_seen / last_seen date When we first and last observed this seller, for tenure and churn analysis Every run
listing_count / your_brand_skus int Total inventory breadth and how many of your SKUs the seller carries Weekly
authorised boolean Whether the seller matches your supplied authorised distributor list Every run
fulfilment enum FBA, FBM, seller-fulfilled, marketplace-fulfilled or hybrid Daily
buybox_win_rate float Share of observations where this seller held the Buy Box, per SKU Hourly to daily
avg_price_vs_map decimal Average percentage position relative to your MAP floor Daily
map_violations_30d int Count of distinct violation events in the trailing 30 days Daily

Seller registered names and addresses are extracted only where the marketplace publishes them publicly as part of statutory seller transparency. We do not attempt to unmask sellers through other means.

Coverage

Marketplaces where we extract seller data

Seller transparency varies by marketplace and jurisdiction. These are live extractors with seller-level parsing already built.

Amazon (20+ marketplaces)Walmart MarketplaceeBayTarget PlusBest Buy MarketplaceNeweggWayfairEtsyFlipkartAmazon.inMeeshoSnapdealNoonAmazon.aeNamshiMercadoLibreAmericanasMagaluCoupangRakutenQoo10ShopeeLazadaTokopediaAllegroBol.comOttoKauflandCdiscountFnacManoManoOnBuyTemuAliExpressAlibabaIndiaMART

B2B distributor and wholesaler directories are also supported for supplier discovery and vendor due diligence use cases. Request a source we don't list →

Markets served

Countries and markets where this service is in highest demand

We deliver into 40+ countries. These are the markets where this particular service is requested most, and the reason demand concentrates there.

Highest-demand markets for this service, and why demand concentrates there
Market Why demand concentrates here
United States The largest unauthorised reseller problem, and the most active enforcement environment.
United Kingdom & Germany Cross-border EU selling creates constant authorisation ambiguity.
India High marketplace seller churn and frequent account re-registration.
United Arab Emirates Growing grey-market import activity across regional marketplaces.

North America

United StatesCanadaMexico

United Kingdom & Ireland

United KingdomIreland

Western Europe

GermanyFranceNetherlandsBelgiumSpainItalySwitzerlandAustria

Nordics

SwedenNorwayDenmarkFinland

Middle East

United Arab EmiratesSaudi ArabiaQatarKuwaitIsrael

Asia Pacific

SingaporeAustraliaNew ZealandJapanSouth KoreaMalaysiaIndonesiaThailandVietnamPhilippines

South Asia

IndiaBangladeshSri LankaPakistan

LATAM

BrazilArgentinaChileColombia

Africa

South AfricaNigeriaKenyaEgypt

We run production collection across 40+ countries. Coverage depth varies by market and by source, so we confirm what is actually available for your specific markets during scoping rather than claiming uniform global coverage. Ask about a market we don't list →

Who buys this data

Which teams buy seller and vendor data

Brand protection is the largest buyer, but marketplace operators and investors use the same underlying feed differently.

Brand Protection Manager

CPG, electronics, appliances, apparel
The problem

Unauthorised sellers appear, undercut MAP, and disappear before enforcement completes — and returning accounts under new names restart the cycle.

What we deliver

Persistent seller profiles matched against your authorised list, with inventory-fingerprint links surfacing likely returning accounts and evidence attached.

Metric that moves

Unauthorised seller count

Channel / Distribution Manager

Manufacturers and brands
The problem

Authorised partners complain about being undercut, but you have no data showing who is doing it or from which region the goods originate.

What we deliver

Complete seller list per SKU with pricing behaviour, fulfilment type and operating country, so channel conflict conversations start from evidence.

Metric that moves

Channel price integrity

Marketplace Category Manager

Marketplace operators
The problem

You need to understand supply-side concentration in a category, but internal data doesn't show how the same sellers behave on rival marketplaces.

What we deliver

Cross-marketplace seller profiles showing which merchants operate where, their inventory breadth and their pricing posture on each platform.

Metric that moves

Category supply health

Marketplace Seller / Aggregator

3P sellers and brand aggregators
The problem

You cannot see which competing sellers hold the Buy Box, how often it rotates, or what pricing behaviour actually wins it.

What we deliver

Per-SKU Buy Box win rates by seller with rotation timing and offer counts, so repricing strategy is informed by observed outcomes.

Metric that moves

Buy Box win rate

Legal & IP Enforcement

Brand legal teams
The problem

Takedown and cease-and-desist actions need documented evidence of infringing offers, captured at a specific time.

What we deliver

Timestamped evidence capture per offer including seller identity, price, listing content and screenshots, packaged for enforcement filings.

Metric that moves

Enforcement cycle time

Investment Research

Funds and diligence teams
The problem

Marketplace GMV concentration and 3P seller dynamics are material to retail theses, but no clean seller-level panel exists.

What we deliver

Seller-level panels showing count, churn, inventory breadth and category concentration over time, delivered as modelling-ready series.

Metric that moves

Signal lead time

Use cases

How seller and vendor data gets used

Four patterns, with measured outcomes.

Unauthorised reseller identification and enforcement

Every seller offering your SKUs is profiled and matched against your authorised distributor list. Unauthorised sellers are ranked by the volume of your inventory they carry and the depth of their MAP undercutting, with timestamped evidence attached per offer.

Outcome: Enforcement effort concentrated on the sellers doing measurable commercial damage rather than the first ones noticed.

Detecting returning suspended accounts

Suspended sellers routinely reappear under new storefront names. Inventory overlap, category concentration, pricing patterns and first-seen timing form a behavioural fingerprint that links candidate accounts, surfaced with supporting evidence for your team to assess.

Outcome: Repeat offenders identified in weeks instead of going undetected through multiple account cycles.

Buy Box strategy grounded in observed outcomes

Per-SKU Buy Box win rates by seller, tracked hourly, reveal which price points and fulfilment methods actually win the box on each marketplace — rather than what a repricing tool's documentation assumes.

Outcome: Repricing rules tuned to observed Buy Box behaviour, with clearer visibility into when winning it is not worth the margin.

Supplier discovery and vendor due diligence

For sourcing teams, the same extraction applied to B2B directories and wholesaler platforms builds a structured supplier universe with product ranges, stated capabilities, tenure and reputation signals for shortlisting.

Outcome: Sourcing shortlists built from a documented supplier universe rather than trade-show contacts and inbound outreach.

Engagement examples

Two engagements, anonymised

Clients rarely permit naming. These are real engagement shapes with identifying detail removed, so you can judge whether the work resembles your situation.

Sporting goods brand · US/EU

Takedowns worked, then the same seller returned under a new name

Situation

Enforcement removed unauthorised listings, but the same operators re-registered within weeks and relisted, so the problem never actually reduced.

What we ran

Continuous seller monitoring with fingerprinting across marketplace re-registrations, so returning accounts were flagged on first listing rather than treated as new sellers.

Result

Repeat offenders identified at relisting instead of after months of renewed damage.

Consumer health brand · India

Nobody could say how many unauthorised sellers actually existed

Situation

The brand suspected significant grey-market selling but had no baseline count, which made it impossible to prioritise enforcement or measure progress.

What we ran

Full seller mapping across five marketplaces with authorisation status classified against the official reseller list, refreshed weekly.

Result

A measurable baseline replaced guesswork, and enforcement was prioritised by seller volume rather than by complaint.

Examples are anonymised at client request. Named references are available on request under NDA. See published case studies →

The 48-hour sample — run on your sources, not ours

Before you commit to anything, we run this service against your own sources and send you the output. If the coverage isn't there, the sample will show you that too — which is the point. We would rather lose the deal at the pilot than at month three.

  • Real extraction from your actual sources
  • Returned inside two business days
  • 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 for this work

Same collection pipeline and same 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.

Build vs buy

Should you build seller tracking in-house or hire it as a service?

Evidence quality and repeat-offender detection are what separate a usable service from a scraper.

In-house build vs self-serve tool vs Actowiz managed service
Consideration In-house scraping team Generic proxy / DIY tool Actowiz managed feed
Time to first usable data 6–12 weeks of engineering before anything is trustworthy Days, but output needs manual cleanup before use Free pilot in 48 hours, production in 5–10 business days
Who fixes it when a source changes Your engineers, at the cost of their roadmap You do — tools report failures, they don't resolve them We do, same business day, inside the retainer
Data quality assurance Whatever your team has time to build None beyond HTTP success Schema validation plus sampled human QA on every run
Compliance documentation Rarely produced, then requested urgently by legal Not provided; terms risk sits with you Sources, method and lawful basis documented for review
Accountability Distributed across a team with other priorities A support ticket queue A named engineer and an account owner
True annual cost Engineer salaries, proxies, hosting, ongoing maintenance Low licence fee plus significant hidden analyst time One fixed monthly retainer, quoted after scoping

Why seller data is the missing layer in most brand protection programmes

Most brand protection programmes monitor prices and react to violations. That catches symptoms. The seller is the actor, and without seller-level continuity you are treating each violation as a fresh incident rather than as behaviour by a known party.

The practical difference shows up in three places:

  • Prioritisation. A seller carrying 3 of your SKUs at 4% below MAP is a nuisance. A seller carrying 180 of your SKUs at 15% below MAP with FBA fulfilment and 20,000 feedback is a channel problem. Price-only monitoring makes both look like violations.
  • Escalation. Marketplaces respond better to documented patterns than to isolated reports. A file showing a seller's 90-day violation history, inventory breadth and prior account links is materially more actionable than a single screenshot.
  • Root cause. Seller registered addresses and inventory patterns often point to which authorised distributor is leaking product. That is a supply-chain conversation, and it is the only fix that actually stops the problem recurring.

Seller data pairs naturally with pricing and product data, which supplies the MAP comparison, and with promotions data, which reveals whether undercutting is structural or promotional.

What we will and will not do on seller identity

Seller identity work sits close to a privacy and legal boundary, so it is worth being explicit about where we stop.

What we do

  • Extract seller information the marketplace publishes publicly, including registered business names and addresses where seller transparency rules require disclosure.
  • Build behavioural profiles from public listing, pricing and reputation data.
  • Surface candidate links between accounts based on inventory and behavioural overlap, with the supporting evidence shown.
  • Capture timestamped evidence of public offers for enforcement use.

What we do not do

  • Create accounts, log in, or extract anything behind authentication.
  • Attempt to identify individuals behind business accounts through data sources outside the marketplace.
  • Purchase test products to obtain shipping or invoice details on your behalf.
  • Assert that two accounts are the same legal entity. We show the pattern and the evidence; the determination is yours and your counsel's.

This boundary exists because the alternative creates legal exposure for you, not just for us. A brand protection case built on properly sourced public evidence survives challenge; one built on questionable collection does not. Our written collection methodology is available for your legal team to review before you sign anything.

How it works

How a seller data engagement goes live in 5 to 10 business days

Your authorised distributor list is loaded during the pilot, so unauthorised flagging works from the first production run.

Scope the sources and fields

You send us target sites, regions, SKUs or keywords. We return a field-level schema proposal, coverage estimate and refresh recommendation — usually within two working days.

Pilot sample, free

We extract a real sample from your actual targets so you can inspect field fill rates, edge cases and match quality before any commitment.

Production build and QA harness

Our engineers build extractors, then wire validation rules: type checks, range checks, duplicate detection and golden-record comparison against a manually verified subset.

Scheduled delivery into your stack

Feeds run at your chosen cadence and land in the warehouse or bucket you already use. Schema changes are versioned and announced before they ship.

Ongoing monitoring and SLA support

We watch coverage drift, fill rates and source changes daily. A named engineer owns your account, and layout breaks are fixed by us — not queued for you.

Formats & destinations

JSON, JSONL, CSV, Parquet or XLSX, delivered to Amazon S3, Google Cloud Storage, Azure Blob, SFTP, Snowflake, BigQuery, Databricks or a REST/GraphQL endpoint. Webhooks fire on completion, and every batch ships with a manifest containing row counts, schema version and QA results so your pipeline can fail loudly instead of silently ingesting a bad file.

Compliance & data ethics

We collect only publicly accessible information, respect robots directives and rate limits, never bypass authentication or paywalls, and never scrape personal data outside a documented lawful basis. Each engagement includes a written collection methodology, source list and retention policy your legal and procurement teams can review before signature.

Service commitments

What we commit to, in writing

These are contractual, not marketing copy. They appear in the engagement document.

Service level commitments written into every managed engagement
Commitment What we hold ourselves to
Pilot turnaround A real sample from your own sources within 48 hours of scoping, at no cost.
Go-live Production collection running within 5–10 business days of sign-off.
Delivery punctuality 99.5% on-schedule delivery, measured monthly and reported to you.
Breakage response Source layout changes triaged same business day; critical sources inside 4 hours.
Data quality Schema validation on every run plus sampled human QA before any delivery leaves us.
Escalation A named engineer and an account owner, not a shared ticket queue.
Change requests Field additions and source changes handled inside the retainer, not re-quoted.
Exit Your historical data exported in full on request. No lock-in, no export fee.

Why teams pick Actowiz for this work

  • Engineers, not a dashboard. You get people who fix breakages, not a self-serve tool you maintain yourself.
  • We tell you what we can't do. Scope limits and coverage gaps are stated before you sign, not discovered in month three.
  • QA is part of the service. Schema validation and sampled human review run before delivery, every run.
  • Compliance is documented. Sources, method and lawful basis written down so your legal team can review them.
  • Fixed monthly cost. No per-request metering, no surprise overage on a month when a competitor adds SKUs.
  • Six years, 40+ countries. Long-running production pipelines across retail, travel, mobility and finance.
Definitions

Terms used on this page

Plain definitions of the terms used on this page, so procurement and legal reviewers are working from the same vocabulary as your data team.

Unauthorised seller
A seller listing your products without being part of your approved reseller network. Classification requires comparing observed sellers against your authorised list, which changes over time.
Account fingerprinting
Recognising that a newly registered marketplace seller is the same operator as a previously removed one, based on observable listing and behavioural patterns rather than on marketplace-internal identity data.
Evidence capture
Timestamped, reproducible records of an infringing listing suitable for use in an enforcement filing. A screenshot without provenance is generally not sufficient for formal action.
FAQ

Seller and vendor data: frequently asked questions

What buyers ask during evaluation.

Where the marketplace publishes it, yes. Seller transparency requirements in the EU, UK and increasingly elsewhere mean many marketplaces now display a registered business name, address and sometimes a tax identifier on the seller profile. We extract those fields wherever they appear.

Where a marketplace publishes nothing beyond a storefront name, we cannot supply a legal identity, and we will not attempt to obtain one through other means. What we can supply is a behavioural profile — inventory footprint, tenure, pricing pattern, fulfilment method — which is often sufficient for enforcement prioritisation even without a legal name.

Through behavioural fingerprinting. A returning seller typically relists a substantially overlapping inventory set, concentrates in the same categories, prices with a similar pattern, and appears shortly after the previous account disappeared. We compute overlap across these dimensions and surface candidate links with the supporting evidence shown.

We present these as candidates for your team to assess, not as conclusions. The evidence is usually persuasive, but attributing two accounts to one legal entity is a determination for you and your counsel, not for a data provider.

Rotation, on priority SKUs. Hourly polling captures each Buy Box observation with a timestamp, so we can report win rate per seller over any window, rotation frequency, and time-of-day patterns. A snapshot showing one winner tells you almost nothing when the box rotates between six sellers through the day.

Polling frequency is set per SKU tier, because hourly Buy Box tracking across an entire catalogue is expensive and rarely necessary. Most clients run hourly on their top revenue SKUs and daily elsewhere.

You tell us. During onboarding you supply your authorised distributor and reseller list — storefront names, seller IDs, or business names. We match every observed seller against it and flag non-matches as unauthorised.

Matching is fuzzy by necessity, since storefront names rarely match legal names exactly. Ambiguous cases are surfaced for your confirmation rather than auto-classified, and confirmed decisions are retained so the same seller isn't re-flagged every run.

Yes. Evidence capture is a standard option: for each flagged offer we retain the seller identity, price, listing content, offer URL and a full-page screenshot, all with a UTC timestamp and a hash for integrity. Packages can be exported per seller or per incident.

This is what makes the difference between a takedown request that gets actioned and one that gets queued. We can't advise on the legal sufficiency of evidence in your jurisdiction — that's your counsel's call — but we provide the documented chain they'll ask for.

European marketplaces generally publish the most, driven by regulatory transparency requirements: registered name, address and often a company identifier are commonly visible. Amazon's European marketplaces, Otto, Kaufland, Bol.com, Allegro and Cdiscount are relatively rich.

US marketplaces publish less by default, though Amazon's US seller profiles now often include a business name and address. Asian marketplaces vary widely. During scoping we tell you field-by-field what is realistically obtainable per marketplace, rather than promising a uniform schema and delivering nulls.

Yes. The same extraction approach applies to B2B directories, wholesaler platforms and distributor catalogues — IndiaMART, Alibaba, ThomasNet and vertical trade directories among them. Fields shift toward product range, stated capacity, certifications, tenure and reputation signals rather than Buy Box metrics.

Sourcing teams use this for supplier discovery and initial due diligence shortlisting. It complements rather than replaces verification, since directory self-reported claims need independent checking.

Marketplace-published fields like feedback count and account age are captured as shown, which sometimes reaches back years. Our own observational history — first-seen dates, inventory changes, pricing behaviour, Buy Box win rates — begins when collection begins for your specific SKU and marketplace set.

Where we already monitor a marketplace for other clients, some historical depth may be available; we'll tell you during scoping. But behavioural history is the one thing that genuinely cannot be backfilled, which is the strongest argument for starting collection before you think you need it.

We quote every seller monitoring engagement individually, because a real number depends on scope: source count, record volume, refresh frequency and delivery method. Anyone quoting you a price before understanding those four things is guessing.

Marketplace count and how many brands or SKUs you are protecting drive the number, along with whether returning-account fingerprinting is included.

The process is short: one scoping call, a free pilot on your own sources within 48 hours, then a fixed monthly quote. No per-request metering, no overage billing, and field or source additions are handled inside the retainer rather than re-quoted. Request a quote.

Test the service on your own brand

Name your brand and the marketplaces that concern you. We return a live seller map with authorisation status within 48 hours, at no cost.

Free pilot, no obligation, no card. You'll have a fixed monthly quote after one scoping call.
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Blog

UK Supermarket Price Comparison: How Tracking Works in 2026

Learn how UK supermarket price comparison works in 2026. Track prices, promotions, product availability, assortments, and competitor activity across leading grocery retailers to optimize pricing and retail strategies.

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Case Study

Building a Top-200 Medicines Price & Availability Tracker Across India

How Actowiz Solutions built a daily Top-200 medicines price & availability tracker across Indian epharmacies architecture, effective pricing, alerts & outcomes.

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Report

Extract Superdrug Products Data for Competitive Pricing, Product Assortment, and Category Insights

Extract Superdrug Products Data to analyze pricing, product trends, promotions, and inventory for smarter retail market intelligence.

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Free 500-row sample · No credit card · Response within 2 hours