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
That name who is actually selling your products.
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.
Last verified 5 August 2026 by the Actowiz Solutions Data Engineering team.
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.
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.
Profile-level and offer-level data are delivered as joinable tables, so you can analyse sellers as entities or track individual offers over time.
Everything the marketplace publicly discloses about who the merchant is.
Trust signals and operating tenure, which reveal churn and new-account patterns.
What the seller carries, which is the strongest fingerprint for identifying returning accounts.
How effectively the seller competes for the sale, tracked through the day.
The pattern of how a seller prices, not just their current price.
The layer that turns seller data into enforcement action.
Detection, evidence capture and repeat-offender fingerprinting as one managed workflow.
Every engagement delivers a documented schema. These are the core fields; the full dictionary is agreed during scoping.
| 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.
Seller transparency varies by marketplace and jurisdiction. These are live extractors with seller-level parsing already built.
B2B distributor and wholesaler directories are also supported for supplier discovery and vendor due diligence use cases. Request a source we don't list →
We deliver into 40+ countries. These are the markets where this particular service is requested most, and the reason 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. |
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 →
Brand protection is the largest buyer, but marketplace operators and investors use the same underlying feed differently.
Unauthorised sellers appear, undercut MAP, and disappear before enforcement completes — and returning accounts under new names restart the cycle.
Persistent seller profiles matched against your authorised list, with inventory-fingerprint links surfacing likely returning accounts and evidence attached.
Unauthorised seller count
Authorised partners complain about being undercut, but you have no data showing who is doing it or from which region the goods originate.
Complete seller list per SKU with pricing behaviour, fulfilment type and operating country, so channel conflict conversations start from evidence.
Channel price integrity
You need to understand supply-side concentration in a category, but internal data doesn't show how the same sellers behave on rival marketplaces.
Cross-marketplace seller profiles showing which merchants operate where, their inventory breadth and their pricing posture on each platform.
Category supply health
You cannot see which competing sellers hold the Buy Box, how often it rotates, or what pricing behaviour actually wins it.
Per-SKU Buy Box win rates by seller with rotation timing and offer counts, so repricing strategy is informed by observed outcomes.
Buy Box win rate
Takedown and cease-and-desist actions need documented evidence of infringing offers, captured at a specific time.
Timestamped evidence capture per offer including seller identity, price, listing content and screenshots, packaged for enforcement filings.
Enforcement cycle time
Marketplace GMV concentration and 3P seller dynamics are material to retail theses, but no clean seller-level panel exists.
Seller-level panels showing count, churn, inventory breadth and category concentration over time, delivered as modelling-ready series.
Signal lead time
Four patterns, with measured outcomes.
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.
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.
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.
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.
Clients rarely permit naming. These are real engagement shapes with identifying detail removed, so you can judge whether the work resembles your situation.
Enforcement removed unauthorised listings, but the same operators re-registered within weeks and relisted, so the problem never actually reduced.
Continuous seller monitoring with fingerprinting across marketplace re-registrations, so returning accounts were flagged on first listing rather than treated as new sellers.
Repeat offenders identified at relisting instead of after months of renewed damage.
The brand suspected significant grey-market selling but had no baseline count, which made it impossible to prioritise enforcement or measure progress.
Full seller mapping across five marketplaces with authorisation status classified against the official reseller list, refreshed weekly.
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 →
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.
Same collection pipeline and same QA underneath. The difference is who holds the schedule and how the data reaches you.
We own the collection, the QA and the delivery. You receive clean data on a schedule and never touch a scraper.
Best fit: Teams who need the data, not the infrastructure.
The same collection pipeline exposed as an authenticated REST endpoint your systems query directly.
Best fit: Product and engineering teams building on live data.
A defined pull for a specific question — market sizing, diligence, a pitch, a one-off audit.
Best fit: Research, strategy and diligence work with a deadline.
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.
Evidence quality and repeat-offender detection are what separate a usable service from a scraper.
| 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 |
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:
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.
Seller identity work sits close to a privacy and legal boundary, so it is worth being explicit about where we stop.
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.
Your authorised distributor list is loaded during the pilot, so unauthorised flagging works from the first production run.
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.
We extract a real sample from your actual targets so you can inspect field fill rates, edge cases and match quality before any commitment.
Our engineers build extractors, then wire validation rules: type checks, range checks, duplicate detection and golden-record comparison against a manually verified subset.
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.
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.
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.
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.
These are contractual, not marketing copy. They appear in the engagement document.
| 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. |
Plain definitions of the terms used on this page, so procurement and legal reviewers are working from the same vocabulary as your data team.
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.
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.Our web scraping expertise is relied on by 4,000+ global enterprises including Zomato, Tata Consumer, Subway, and Expedia — helping them turn web data into growth.
Watch how businesses like yours are using Actowiz data to drive growth.
From Zomato to Expedia — see why global leaders trust us with their data.
Backed by automation, data volume, and enterprise-grade scale — we help businesses from startups to Fortune 500s extract competitive insights across the USA, UK, UAE, and beyond.
We partner with agencies, system integrators, and technology platforms to deliver end-to-end solutions across the retail and digital shelf ecosystem.
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.
How Actowiz Solutions built a daily Top-200 medicines price & availability tracker across Indian epharmacies architecture, effective pricing, alerts & outcomes.
Extract Superdrug Products Data to analyze pricing, product trends, promotions, and inventory for smarter retail market intelligence.
Whether you're a startup or a Fortune 500 — we have the right plan for your data needs.