Core services
Enterprise Data Extraction

Scalable web, app and AI-powered collection across 40+ countries.

All 58 services →
New 2026
AI Training Data

Corpus building with provenance and opt-out compliance.

Learn more →
Free pilot
24-hour sample

We run collection on your own sources before you commit.

Get a sample →
58Services
40+Countries
DEVELOPER

Ready-Made Scrapers

Pre-built for top platforms. Self-serve, no setup.

View All →
TRY FREE

API Playground

Test endpoints instantly. No credit card.

Start Free →
28Tools
2SDKs
icons Delivery & SDKs
Streaming Crawl API Scheduler Realtime Alerts Webhook Delivery 🐍 Python SDK 💚 Node.js SDK
Need it managed instead?

Fixed monthly retainer, named engineer, no per-request metering.

Managed Data API →
HOT

Case Studies

How brands use Actowiz, with named outcomes.

Read →
FREE

Sample Datasets

Real output, no signup.

Download →
NEW

ROI Calculator

Model the return on a data engagement.

Calculate →
Service · Luxury & resale data

Luxury & Resale Data Scraping

Primary retail joined to resale, with condition normalised across platforms.

Luxury and resale data scraping is the automated collection of prestige retail and secondary market data — primary retail pricing and availability joined to resale listings on the same items, condition grading normalised across platforms, resale-to-retail ratios, and grey market signals. Authentication claims are captured as stated and never verified.

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.

Primary joined to resale Condition normalised Free pilot sample in 24 hours
luxury_resale.jsonl LIVE FEED
{"item_key":"aw-lux-771204", "brand":"Example Maison", "model":"Structured Tote, medium", "market_side":"primary", "retailer":"brand-boutique.com", "retail_price":2450.00,"currency":"GBP", "in_stock":false, "waitlist_shown":true, "markdown_seen_ever":false} {"item_key":"aw-lux-771204", "market_side":"resale", "platform":"example-resale.com", "ask_price":2790.00, "resale_to_retail_pct":113.9, "condition_published":"Excellent", "condition_normalised":"grade_2_of_5", "condition_mapping_confidence":0.82, "authentication_claimed":"platform_authenticated", "authentication_verified":false, "match_confidence":0.91, "days_listed":18, "seller_type":"platform_consignment"}
2 of 2,884,100 item-listing rowsprimary-resale matched 88.4% · schema v3.0
Our Data Powers
B2C Marketplace
amazon
D2C + Marketplace
NYKAA
D2C + Marketplace
Walmart
FMCG Marketplace
udaan
Food Delivery
Uber Eats
Quick Commerce
blinkit
Taxi Aggregator
Uber
E-Commerce
Tmall

Key facts at a glance

What it is
Managed collection of prestige retail and secondary market data, joined on item identity
The join
Primary retail price and availability matched to resale listings for the same item
Condition
Platform grading normalised to a common scale with mapping confidence
Ratio
Resale-to-retail percentage computed, the headline metric in this category
Authentication
Claims captured as stated, with verified constant false
Scarcity signals
Waitlists, sold-out state and markdown history on the primary side
Grey market
Unauthorised primary-side listings flagged for brand teams
Who it's for
Luxury brands, resale platforms, investors and insurers
88.4%primary-resale match ratewith confidence scores
Conditionnormalised with confidenceno platform agrees
Resale-to-retailcomputed per listingthe category metric
Authenticationcaptured, never verifiedhard boundary

Key takeaways

  • What it is: Managed collection of prestige retail and secondary market data, joined on item identity
  • The join: Primary retail price and availability matched to resale listings for the same item
  • Condition: Platform grading normalised to a common scale with mapping confidence
  • Ratio: Resale-to-retail percentage computed, the headline metric in this category
  • Authentication: Claims captured as stated, with verified constant false
  • Scarcity signals: Waitlists, sold-out state and markdown history on the primary side

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

Definition

What is luxury and resale data, and why is the join the hard part?

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.

Why matching primary to resale is hard

  • Resale listings are user-written. Brand, model and specification are inconsistent, abbreviated or wrong.
  • Model naming is unstable. The same handbag has an official name, a colloquial name and a reference code, used interchangeably.
  • Size and specification vary. Medium versus small changes both retail and resale price materially.
  • Season and year matter. A current-season item and a five-year-old one in the same model command different resale prices.
  • Primary may no longer be listed. Discontinued items have no current retail price, so the retail reference has to come from archive rather than from today's page.

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.

Condition grading, which nobody standardises

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.

What we will not do

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.

What we collect

Six categories of luxury and resale data

The primary-to-resale join is the core. Condition normalisation is what makes it usable.

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

Resale listings

The secondary market side.

  • Ask price and price change history
  • Days listed and relisting detection
  • Seller type: consignment, platform-owned or peer
  • Sold and delisted signals where published
  • Multi-platform listing detection for the same item

Condition grading

Normalised across incompatible scales.

  • Published condition wording retained
  • Normalised five-grade scale
  • Mapping confidence per platform
  • Defect descriptions where structured
  • Inclusion of box, papers and accessories

Resale-to-retail economics

The metric the category runs on.

  • Resale-to-retail percentage computed
  • Ratio by model, condition and market
  • Ratio trend over time
  • Above-retail detection for scarce models
  • Depreciation curves by brand and category

Authentication & provenance claims

Captured, never judged.

  • Authentication claim as stated by the platform
  • Provenance and papers claims as listed
  • Serial or reference presence where published
  • Claim wording change detection
  • Never an authenticity assessment from us

Grey market signals

For brand protection.

  • Unauthorised primary-side listings
  • Below-retail primary offers on current-season items
  • Cross-border pricing gaps on the same reference
  • New unauthorised seller detection
  • Volume concentration by seller
Service scope

What the ecommerce data scraping service includes

A managed engagement, not a tool licence. We own the pipeline and everything that breaks in it.

✓ Included in every engagement

  • Primary retail joined to resale listings on matched item identity with confidence
  • Condition normalised to a common scale with per-platform mapping documented
  • Seller-declared versus platform-assessed grading recorded as distinct
  • Retail price source flagged as live or archive rather than implied current
  • Authentication claims captured with authentication_verified permanently false
  • 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

  • Any assessment of authenticity or of an authentication process
  • Achieved resale prices where platforms do not publish them
  • Subscriber-only auction or price databases, which are licensed products
  • Seller personal details, as distinct from seller type and volume pattern
  • 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

Luxury and resale fields you receive

Every engagement delivers a documented schema. These are the core fields; the full dictionary runs to 100+ and is agreed during scoping.

Deliverable schema — v3.0 core fields (full dictionary: 100+ fields)
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.

Coverage

Retailers and platforms we cover

Both sides of the market, since either alone answers little. Coverage is built to your brand and category set.

Brand boutique sitesLuxury department storesAuthorised multi-brand retailersLuxury ecommerce platformsResale and consignment platformsPeer-to-peer resale marketplacesWatch specialist marketplacesAuction house public results where publishedRegional luxury retailersMarketplace luxury sectionsOutlet and off-price luxury channelsCross-border grey market listings

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 →

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 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.

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 luxury and resale data

Brand and resale platform teams dominate, with investors and insurers following.

Brand Protection / Distribution Lead

Luxury brands
The problem

Grey market and unauthorised listings appear across borders and platforms with no systematic visibility.

What we deliver

Unauthorised primary-side listings and below-retail current-season offers, with cross-border reference price gaps.

Metric that moves

Grey market volume

Head of Pricing / Merchandising

Luxury brands
The problem

Resale performance is the clearest external signal of desirability and nobody tracks it against retail systematically.

What we deliver

Resale-to-retail ratios by model, condition and market with trend, joined to primary availability and waitlists.

Metric that moves

Resale premium retained

Head of Pricing

Resale platforms
The problem

Pricing consignment inventory requires the primary retail reference and competing resale asks on the same item.

What we deliver

Primary retail joined to competing resale listings with condition normalised, so pricing is comparable.

Metric that moves

Sell-through at ask

Category / Buying Lead

Resale platforms
The problem

Which models to acquire depends on resale ratio and days-listed by condition, not on brand reputation.

What we deliver

Ratio and days-listed distributions by model and condition across platforms.

Metric that moves

Inventory turn

Investment Analyst

Consumer and luxury funds
The problem

Brand desirability is observable in resale ratios and primary scarcity signals ahead of reported results.

What we deliver

Longitudinal resale-to-retail panels with waitlist and sold-out frequency by brand and category.

Metric that moves

Signal lead time

Underwriting / Valuation

Insurers and lenders
The problem

Valuing luxury items needs current market evidence across condition grades.

What we deliver

Ask price distributions by model and normalised condition with days-listed, plus published auction results.

Metric that moves

Valuation confidence

Use cases

How luxury and resale data gets used

Four patterns, with the outcome each is judged on.

Resale-to-retail ratio tracking

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.

Grey market and unauthorised listing detection

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.

Consignment pricing for resale platforms

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.

Scarcity and waitlist monitoring

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.

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.

Luxury brand · EU

Grey market activity was reported anecdotally

Situation

Distribution knew unauthorised listings existed but had no measurement by platform or geography, so enforcement was unfocused.

What we ran

Primary-side listing collection compared against the authorised retailer list, with below-retail current-season offers and cross-border gaps flagged.

Result

Grey market activity was quantified by platform and market, letting enforcement target concentration.

Resale platform · US

Consignment pricing ignored the retail reference

Situation

Incoming items were priced against internal rules of thumb rather than against primary retail and competing resale asks at the same condition.

What we ran

Primary retail joined to competing resale listings with condition normalised across platforms and mapping confidence delivered.

Result

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 →

The 24-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

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.

Build vs buy

Should you build luxury and resale collection in-house or hire it as a service?

The primary-to-resale match and condition normalisation are continuous modelling work, not a one-time build.

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 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

Condition grading: five platforms, five vocabularies, no standard

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.

The problem in practice

  • Vocabularies differ. One platform's "Excellent" may sit above or below another's "Very Good" depending on how each defines wear.
  • Scale lengths differ. Three grades on one platform, six on another. Mapping is not a simple alignment.
  • Some are seller-declared, some platform-assessed. That distinction matters more than the grade itself.
  • Defect descriptions are free text where structured at all.
  • Accessories change value. Box, papers and dust bag presence can shift price materially and are inconsistently declared.

What we do

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.

Authentication: the one thing we will never assess

This is the firmest boundary in this service, and it is worth explaining because clients do ask.

What we capture

  • The authentication claim as stated — whether a platform says an item is authenticated, and by what process where described.
  • Provenance claims such as original receipt, papers or serial presence, as listed.
  • Claim wording changes over time, including quiet removals.

What we will never do

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.

What we do instead

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.

How it works

How a luxury and resale engagement goes live in 5 to 10 business days

Brands, categories, platforms and whether the primary-to-resale join is required are scoped first, since the join drives most of the work.

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 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.

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 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.

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.

Resale-to-retail ratio
The resale ask as a percentage of primary retail for the same item and condition. Above 100% indicates an item reselling above retail, a strong desirability signal.
Condition normalisation
Mapping incompatible platform grading vocabularies to a common scale with confidence. No two platforms define condition the same way, and price without condition is not a price.
Authentication claim
A platform's statement that an item is authenticated, captured as stated. We never assess authenticity, since it requires physical examination by qualified specialists.
FAQ

Luxury and resale data: frequently asked questions

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.

See real resale-to-retail data for your own brands

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.
Social Proof That Converts

Trusted by Global Leaders Across Q-Commerce, Travel, Retail, and FoodTech

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.

4,000+ Enterprises Worldwide
50+ Countries Served
20+ Industries
Join 4,000+ companies growing with Actowiz →
Real Results from Real Clients

Hear It Directly from Our Clients

Watch how businesses like yours are using Actowiz data to drive growth.

1 min
★★★★★
"Actowiz Solutions offered exceptional support with transparency and guidance throughout. Anna and Saga made the process easy for a non-technical user like me. Great service, fair pricing!"
TG
Thomas Galido
Co-Founder / Head of Product at Upright Data Inc.
2 min
★★★★★
"Actowiz delivered impeccable results for our company. Their team ensured data accuracy and on-time delivery. The competitive intelligence completely transformed our pricing strategy."
II
Iulen Ibanez
CEO / Datacy.es
1:30
★★★★★
"What impressed me most was the speed — we went from requirement to production data in under 48 hours. The API integration was seamless and the support team is always responsive."
FC
Febbin Chacko
-Fin, Small Business Owner
icons 4.8/5 Average Rating
icons 50+ Video Testimonials
icons 92% Client Retention
icons 50+ Countries Served

Join 4,000+ Companies Growing with Actowiz

From Zomato to Expedia — see why global leaders trust us with their data.

Why Global Leaders Trust Actowiz

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.

icons
7+
Years of Experience
Proven track record delivering enterprise-grade web scraping and data intelligence solutions.
icons
4,000+
Projects Delivered
Serving startups to Fortune 500 companies across 50+ countries worldwide.
icons
200+
In-House Experts
Dedicated engineers across scrapers, AI/ML models, APIs, and data quality assurance.
icons
9.2M
Automated Workflows
Running weekly across eCommerce, Quick Commerce, Travel, Real Estate, and Food industries.
icons
270+ TB
Data Transferred
Real-time and batch data scraping at massive scale, across industries globally.
icons
380M+
Pages Crawled Weekly
Scaled infrastructure for comprehensive global data coverage with 99% accuracy.

AI Solutions Engineered
for Your Needs

LLM-Powered Attribute Extraction: High-precision product matching using large language models for accurate data classification.
Advanced Computer Vision: Fine-grained object detection for precise product classification using text and image embeddings.
GPT-Based Analytics Layer: Natural language query-based reporting and visualization for business intelligence.
Human-in-the-Loop AI: Continuous feedback loop to improve AI model accuracy over time.
icons Product Matching icons Attribute Tagging icons Content Optimization icons Sentiment Analysis icons Prompt-Based Reporting

Connect the Dots Across
Your Retail Ecosystem

We partner with agencies, system integrators, and technology platforms to deliver end-to-end solutions across the retail and digital shelf ecosystem.

icons
Analytics Services
icons
Ad Tech
icons
Price Optimization
icons
Business Consulting
icons
System Integration
icons
Market Research
Become a Partner →

Popular Datasets — Ready to Download

Browse All Datasets →
icons
Amazon
eCommerce
Free 100 rows
icons
Zillow
Real Estate
Free 100 rows
icons
DoorDash
Food Delivery
Free 100 rows
icons
Walmart
Retail
Free 100 rows
icons
Booking.com
Travel
Free 100 rows
icons
Indeed
Jobs
Free 100 rows

Latest Insights & Resources

View All Resources →
thumb
Blog

Price and Competitive Intelligence: How It Actually Gets Built

Price intelligence fails at product matching, not at collection. A practical guide to the five layers of a working programme, what to measure, and how to scope a first phase.

thumb
Case Study

Tracking One Category Across Four Countries: Butter Brands and a Weekly Dashboard

One product category, named competitor brands, several countries, weekly refresh, delivered as data plus a Power BI dashboard. How narrow-and-deep beats broad-and-shallow.

thumb
Report

Fliggy hotel and flight price monitoring

Fliggy hotel and flight price monitoring helps travel businesses track fares, hotel rates, availability, and competitor pricing for smarter decisions.

Start Where It Makes Sense for You

Whether you're a startup or a Fortune 500 — we have the right plan for your data needs.

icons
Enterprise
Book a Strategy Call
Custom solutions, dedicated support, volume pricing for large-scale needs.
icons
Growing Brand
Get Free Sample Data
Try before you buy — 500 rows of real data, delivered in 2 hours. No strings.
icons
Just Exploring
View Plans & Pricing
Transparent plans from $500/mo. Find the right fit for your budget and scale.
Get in Touch
Let's Talk About
Your Data Needs
Tell us what data you need — we'll scope it for free and share a sample within hours.
  • icons
    Free Sample in 2 HoursShare your requirement, get 500 rows of real data — no commitment.
  • icons
    Plans from $500/monthFlexible pricing for startups, growing brands, and enterprises.
  • icons
    US-Based SupportOffices in New York & California. Aligned with your timezone.
  • icons
    ISO 9001 & 27001 CertifiedEnterprise-grade security and quality standards.
Request Free Sample Data
Fill the form below — our team will reach out within 2 hours.
+1
Free 500-row sample · No credit card · Response within 2 hours

Request Free Sample Data

Our team will reach out within 2 hours with 500 rows of real data — no credit card required.

+1
Free 500-row sample · No credit card · Response within 2 hours