Primary retail
The reference price everything hangs off.
- Retail price and currency by market
- Availability and sold-out state
- Waitlist and made-to-order indications
- Markdown history where it occurs
- Authorised retailer coverage
Primary retail joined to resale, with condition normalised across platforms.
A resale price only means something next to the primary retail price of the same item. Getting them onto one record is the work, and condition grading — which no two platforms define the same way — is what makes it hard.
Free pilot on your own sources, returned in 24 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.
Luxury and resale data scraping is the automated collection of two connected markets: prestige primary retail — brand boutiques, luxury department stores, authorised retailers — and the secondary market of resale and consignment platforms.
Either alone is of limited use. The analytical value is in the relationship between them, and producing that relationship reliably is where the difficulty sits.
We match on brand, model, specification, reference codes where present and image-derived signals where available, and deliver match_confidence on every link. Where retail price comes from our archive rather than a live listing, that is recorded rather than presented as current.
Every resale platform uses its own condition vocabulary, and the same words mean different things. We map published condition to a normalised five-grade scale with condition_mapping_confidence, and retain the published wording. The mapping is documented per platform in the scope document so it is inspectable rather than a black box.
Assess authenticity. Where a platform states an item is authenticated, we capture the claim with authentication_verified constant false. Authentication requires physical examination by qualified specialists. A data vendor implying otherwise would be creating serious liability for a client.
The primary-to-resale join is the core. Condition normalisation is what makes it usable.
The reference price everything hangs off.
The secondary market side.
Normalised across incompatible scales.
The metric the category runs on.
Captured, never judged.
For brand protection.
A managed engagement, not a tool licence. We own the pipeline and everything that breaks in it.
Every engagement delivers a documented schema. These are the core fields; the full dictionary runs to 100+ and is agreed during scoping.
| Field | Type | What it captures | Refresh |
|---|---|---|---|
item_key / brand / model |
string | Cross-market item identity with normalised brand and model | Every run |
market_side |
enum | primary or resale, so the two sides remain separable | Every run |
retail_price / retail_price_source |
decimal / enum | Retail reference and whether it is live or from archive | Daily |
ask_price / price_changes |
decimal / array | Resale ask price with its change history | Daily |
resale_to_retail_pct |
decimal | Computed ratio, the headline metric in this category | Daily |
condition_published / condition_normalised |
string / enum | Platform wording retained plus a normalised five-grade scale | Per listing |
condition_mapping_confidence |
decimal | Confidence in the condition mapping, which varies by platform | Per listing |
authentication_claimed / authentication_verified |
string / boolean | Claim as stated, with verified permanently false | Per listing |
match_confidence |
decimal | Confidence in the primary-to-resale item match | Per listing |
days_listed / relisted |
int / boolean | Listing duration and whether the item has been relisted | Daily |
waitlist_shown / sold_out |
boolean | Primary-side scarcity signals | Daily |
authentication_verified is a constant false. Authentication requires physical examination by qualified specialists, and a data vendor implying it had performed one would be creating serious liability for the client relying on it.
Both sides of the market, since either alone answers little. Coverage is built to your brand and category set.
We collect publicly visible listings only. Auction results are collected where publicly published; we do not access subscriber-only price databases, which are licensed products. 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 Kingdom, France & Italy | Primary luxury retail heartland with the deepest authorised retailer coverage for the retail reference side. |
| United States | Largest resale market by volume with the most mature consignment platforms and richest condition grading. |
| Japan & Hong Kong | Highly developed secondary markets with distinct condition conventions and strong watch specialisation. |
| United Arab Emirates & Singapore | High-value primary retail with growing resale activity and significant cross-border price gaps. |
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 and resale platform teams dominate, with investors and insurers following.
Grey market and unauthorised listings appear across borders and platforms with no systematic visibility.
Unauthorised primary-side listings and below-retail current-season offers, with cross-border reference price gaps.
Grey market volume
Resale performance is the clearest external signal of desirability and nobody tracks it against retail systematically.
Resale-to-retail ratios by model, condition and market with trend, joined to primary availability and waitlists.
Resale premium retained
Pricing consignment inventory requires the primary retail reference and competing resale asks on the same item.
Primary retail joined to competing resale listings with condition normalised, so pricing is comparable.
Sell-through at ask
Which models to acquire depends on resale ratio and days-listed by condition, not on brand reputation.
Ratio and days-listed distributions by model and condition across platforms.
Inventory turn
Brand desirability is observable in resale ratios and primary scarcity signals ahead of reported results.
Longitudinal resale-to-retail panels with waitlist and sold-out frequency by brand and category.
Signal lead time
Valuing luxury items needs current market evidence across condition grades.
Ask price distributions by model and normalised condition with days-listed, plus published auction results.
Valuation confidence
Four patterns, with the outcome each is judged on.
Resale asks are joined to primary retail prices on matched items with condition normalised, producing ratios by model, condition and market with trend over time.
Outcome: Brand desirability measured from secondary market evidence rather than inferred from sentiment.
Primary-side listings are compared against authorised retailer lists, and below-retail current-season offers are flagged with cross-border reference gaps.
Outcome: Grey market activity quantified by platform and geography rather than reported anecdotally.
Primary retail reference and competing resale asks on the same item and condition grade support pricing decisions on incoming consignment.
Outcome: Ask prices set against comparable listings rather than against a brand-level rule of thumb.
Primary-side sold-out state, waitlist presence and markdown absence are tracked alongside resale premium.
Outcome: Scarcity-driven resale premium observed as it develops rather than after it peaks.
Clients rarely permit naming. These are real engagement shapes with identifying detail removed, so you can judge whether the work resembles your situation.
Distribution knew unauthorised listings existed but had no measurement by platform or geography, so enforcement was unfocused.
Primary-side listing collection compared against the authorised retailer list, with below-retail current-season offers and cross-border gaps flagged.
Grey market activity was quantified by platform and market, letting enforcement target concentration.
Incoming items were priced against internal rules of thumb rather than against primary retail and competing resale asks at the same condition.
Primary retail joined to competing resale listings with condition normalised across platforms and mapping confidence delivered.
Ask prices were set against comparable evidence, improving sell-through at ask.
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 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.
The primary-to-resale match and condition normalisation are continuous modelling work, not a one-time build.
| 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 24 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 |
Resale price is meaningless without condition, and condition is where this category's data quality problems concentrate. Every platform defines its own scale and the words overlap without matching.
Published condition wording is retained exactly, and mapped to a normalised five-grade scale with condition_mapping_confidence. Mapping is defined per platform and documented in the scope document, so you can inspect and disagree with it rather than inheriting an opaque scale.
Where a platform's grading is seller-declared rather than assessed, that is recorded, because a seller-declared "Excellent" and a platform-assessed one are different evidential quality. And we do not average across grades to produce a single item price, because a price without condition is not a price in this market.
This is the firmest boundary in this service, and it is worth explaining because clients do ask.
Assess whether an item is authentic, or whether an authentication process is adequate. authentication_verified is a constant false on every record.
Authentication requires physical examination by qualified specialists with reference materials. There is no data signal that substitutes for it, and any vendor suggesting there is has not thought about the consequence. If a client acted on an implied authenticity assessment and a buyer received a counterfeit, the liability chain would run through the data vendor's claim, and rightly so.
Signals that are legitimately observable and useful: below-market ask prices for the stated condition, sellers with unusual volume concentration, listings with missing or inconsistent reference details, and cross-platform duplicate listings of ostensibly unique items. These are flagged as anomalies for investigation, in the same way we flag review anomalies without labelling reviews fake in our review service.
They are starting points for your authentication and brand protection teams, not conclusions from us.
Brands, categories, platforms and whether the primary-to-resale join is required are scoped first, since the join drives most of the work.
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 publicly visible primary retail and resale listings. Authentication and provenance claims are captured as stated with authentication_verified constant false, and we provide no authenticity assessment. Seller personal details are not part of the deliverable, and we do not access subscriber-only price databases.
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 24 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 brand, resale platform and valuation teams ask during evaluation.
No, and this is the firmest boundary in the service. We capture the authentication claim as stated with authentication_verified permanently false.
Authentication requires physical examination by qualified specialists. No data signal substitutes for it, and if a client acted on an implied assessment and a buyer received a counterfeit, the liability chain would run through the vendor's claim. We flag anomalies — below-market asks for stated condition, unusual seller volume, inconsistent reference details — as starting points for your team.
Published wording is retained exactly and mapped to a normalised five-grade scale with a mapping confidence per platform. The mapping is documented in the scope document so you can inspect and disagree with it.
We also record whether grading is seller-declared or platform-assessed, because that distinction matters more than the grade itself. And we never average across grades to produce a single item price — a price without condition is not a price in this market.
About 88%, with confidence on every link. Resale listings are user-written with inconsistent brand and model naming, and the same item often has an official name, a colloquial name and a reference code used interchangeably.
Where the retail reference comes from our archive rather than a live listing — common for discontinued models — that is recorded rather than presented as a current price.
Yes, on the primary side: listings from sellers not on your authorised list, below-retail offers on current-season items, and cross-border price gaps on the same reference.
What we provide is quantified activity by platform and geography. Whether a specific seller is unauthorised depends on your distribution agreements, which we do not see — you supply the authorised list and we compare against it.
Yes, and watches behave differently enough to be worth flagging. Reference numbers are more standardised, which helps matching, but condition and completeness — box, papers, service history — affect price more sharply.
We capture accessory and papers presence where declared, since on watches that can move price by a substantial margin, more than on most other luxury categories.
Ask prices, plus sold and delisted signals where a platform publishes them. Many resale platforms do not publish achieved prices, in which case we do not have them.
Where a listing disappears without a sold indication, we record it as unconfirmed rather than as a sale — the same discipline as our property service. Publicly published auction results are collected where available; subscriber-only price databases are licensed products we do not access.
The resale ask as a percentage of primary retail price for the same item and condition. It is the headline metric in this category because it is the clearest external signal of brand desirability.
Above 100% means an item resells above retail, which happens on scarce models and is a strong desirability signal. Tracking the ratio's trend by model and condition shows desirability developing or fading before it appears in reported results.
Seller type — consignment, platform-owned or peer — and volume concentration signals, as business characteristics. Seller personal details are not part of the deliverable.
For brand protection, the useful unit is the selling operation and its volume pattern, not an individual's identity. The same boundary applies across all our services.
We quote individually. The main driver is whether the primary-to-resale join is required, since matching and condition normalisation are where the work concentrates, followed by brand and platform count.
Resale-only or primary-only collection sits at the lighter end. Full join across many brands and platforms with condition normalisation sits higher. One scoping call, a free pilot on your own brands within 24 hours, then a fixed monthly quote. Request a quote.
Send us brands and categories. We return matched primary and resale records with condition normalised and ratios computed within 24 hours.
Free pilot, no card, no obligation. We never assess authenticity — claims are captured as stated.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.
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