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Service · Automotive data

Automotive Data Scraping Services

With days-on-lot and reduction history, not just today's asking price.

Automotive data scraping is the automated collection of vehicle listing data from marketplaces, dealer websites and manufacturer configurators — asking price, specification, mileage, condition, trim, dealer identity and listing lifecycle — with price reductions and days on lot tracked over time so market softness becomes visible.

Two identical cars at the same asking price are not the same asset. One listed yesterday; the other has sat ninety days and reduced twice. That difference is invisible in a snapshot and it is the entire basis of a buying decision.

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

Listing lifecycle, not snapshots VIN-level where published Free pilot sample in 48 hours
vehicle_listings_2026-08-05.jsonl LIVE FEED
{"listing_key":"aw-veh-GB-8841207", "source":"marketplace", "make":"Volkswagen","model":"Golf", "trim":"1.5 TSI Life","year":2022, "fuel":"petrol","transmission":"manual", "mileage_km":41200,"owners":2, "asking_price":17495,"currency":"GBP", "price_history":[{"date":"2026-05-08","price":19250}, {"date":"2026-07-02","price":17495}], "reductions":1,"days_on_lot":89, "dealer_id":"aw-dlr-GB-2210","dealer_type":"franchised", "lat":53.4013,"lon":-2.1595, "image_count":42,"status":"available"} {"listing_key":"aw-veh-GB-8712004", "fuel":"bev","battery_kwh":77, "range_wltp_km":520, "status":"sold_or_withdrawn","days_on_lot":23}
2 of 3,208,400 tracked listings · run 2026-08-05T05:00Zspec match 96.1% · schema v4.7
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 new and used vehicle listing data with lifecycle tracked over time
Sources
Marketplaces, franchised and independent dealer sites, auction listings and manufacturer configurators
Lifecycle
Price reductions, days on lot, relisting detection and sold-or-withdrawn status transitions
Specification
Make, model, trim, year, fuel, transmission, mileage and EV-specific battery and range fields
Dealer data
Dealer identity, franchised versus independent classification and geolocation
Trim matching
Listings matched to a normalised trim taxonomy so like-for-like pricing is possible
Refresh
Daily standard; sub-daily for new-listing alerting on tracked inventory
Who it's for
Dealers, marketplaces, OEMs, lenders, remarketers, fleet and leasing, and investors
Days on lottracked per listingnot inferred
96.1%trim match ratenormalised taxonomy
Reduction historyreconstructed from our runswith dates
EV fieldsbattery, range, chargingas first-class

Key takeaways

  • What it is: Managed collection of new and used vehicle listing data with lifecycle tracked over time
  • Sources: Marketplaces, franchised and independent dealer sites, auction listings and manufacturer configurators
  • Lifecycle: Price reductions, days on lot, relisting detection and sold-or-withdrawn status transitions
  • Specification: Make, model, trim, year, fuel, transmission, mileage and EV-specific battery and range fields
  • Dealer data: Dealer identity, franchised versus independent classification and geolocation
  • Trim matching: Listings matched to a normalised trim taxonomy so like-for-like pricing is possible

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

Definition

What is automotive data scraping, and why is lifecycle the whole value?

Automotive data scraping is the automated collection of vehicle listing information from online marketplaces, dealer websites, auction platforms and manufacturer sites: asking price, specification, mileage, condition, trim level, dealer and location.

The category has a familiar shape to real estate, and for the same reason: a vehicle listing is not a static product record, it is an asset with a selling history. That history carries most of the commercial signal.

What only lifecycle tracking reveals

  • Days on lot. The single strongest indicator of pricing accuracy. A car at ninety days is mispriced or misrepresented, and the dealer knows it.
  • Reduction history. A vehicle at £17,495 today that launched at £19,250 tells you about negotiation room and about how the segment is moving.
  • True days on lot across relistings. Dealers relist to reset the visible counter. Linking identity across relists reveals actual elapsed time.
  • Sell-through velocity by segment. How quickly listings disappear by make, model, trim and price band is the closest public proxy for real demand.
  • Dealer pricing behaviour. Which dealers price aggressively and which sit on stock is only visible longitudinally.

Why trim matching matters more than in most categories

The same model at two trim levels can differ 20% in value. Listings describe trim inconsistently, with abbreviations, optional packs folded into the title, and market-specific naming. Matching on make and model alone produces price comparisons across genuinely different vehicles.

We match to a normalised trim taxonomy with a confidence score, treat fuel type and transmission as hard constraints, and capture EV-specific fields — battery capacity, quoted range, charging speed — as first-class rather than buried in description text, because in electric vehicles those fields drive price more than trim does.

What we cannot tell you

Transacted prices. Asking prices are public; what a vehicle actually sold for is not, and a listing disappearing does not prove a sale. We report sold_or_withdrawn rather than sold, because those are different outcomes and conflating them corrupts every velocity metric built on top.

What we collect

Six categories of automotive data

Used vehicle pricing is the largest use case. New vehicle configurator pricing is the most technically awkward.

Used vehicle listings

The core dataset, with lifecycle attached.

  • Asking price and reduction history
  • Mileage, age and owner count
  • Condition and service history mentions
  • Days on lot with relist linking
  • Status transitions with dates

Specification & trim

The attributes that determine value.

  • Make, model and normalised trim
  • Fuel type and transmission
  • Engine, power and drivetrain
  • Optional packs and equipment mentions
  • EV battery, range and charging fields

Dealer & inventory

Who is holding what, where.

  • Dealer identity and geolocation
  • Franchised versus independent classification
  • Inventory size and mix
  • Stock turn by dealer
  • New listing and delisting events

New vehicle pricing

List pricing and configurator structure.

  • Manufacturer list prices by trim
  • Option and pack pricing
  • Published finance representative examples
  • Discount and campaign mentions
  • Configurator availability changes

Market structure & velocity

The demand signal layer.

  • Listing volume by segment and region
  • Sell-through velocity by trim and price band
  • Inventory ageing distribution
  • Price movement by segment over time
  • EV versus ICE mix shift

Finance & leasing offers

Published payment-led pricing.

  • Published monthly payment examples
  • APR and term where disclosed
  • Deposit and mileage allowance
  • Balloon and residual assumptions where published
  • Lease offer change detection
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

  • Relist detection with days_on_lot continued from original first-seen
  • Trim normalisation with confidence scoring and hard fuel and transmission constraints
  • EV battery, range and charging captured as fields rather than free text
  • Dealer versus private classification delivered as a field
  • Status reported as sold_or_withdrawn rather than asserted as sold
  • 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

  • Transacted prices, which are not publicly published anywhere
  • Registration or DVLA-type lookup service queries
  • Dealer management system or trade-only auction platform access
  • Private seller names, phone numbers or contact details
  • 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

Automotive data fields you receive

Every engagement delivers a documented schema. These are the core fields; the full dictionary runs to 130+ and is agreed during scoping.

Deliverable schema — v4.7 core fields (full dictionary: 130+ fields)
Field Type What it captures Refresh
listing_key string Stable listing identity that survives relisting and source ID changes Every run
make / model / trim / trim_confidence string / decimal Normalised specification with a confidence score on trim matching Weekly
year / mileage / owners int Registration year, mileage in local units and owner count where published Weekly
fuel / transmission enum Fuel type including BEV and PHEV, and transmission, treated as hard match constraints Weekly
battery_kwh / range_wltp decimal / int EV battery capacity and quoted range, extracted as fields not description text Weekly
asking_price / currency decimal / string Current asking price in local currency Daily
price_history / reductions array / int Every observed price change with dates, and a reduction count Daily
days_on_lot int Elapsed days from first observation, linked across relistings Daily
dealer_id / dealer_type string / enum Stable dealer identity and franchised versus independent classification Weekly
lat / lon decimal Dealer or listing geolocation, enabling regional pricing analysis Weekly
status enum available, sold_or_withdrawn or relisted — never asserted as sold Daily

Status is sold_or_withdrawn rather than sold because a listing disappearing does not prove a transaction. Treating disappearance as a sale inflates every sell-through metric built on the data.

Coverage

Sources and markets we collect from

Automotive marketplaces are intensely national. Coverage is built market by market with dealer site depth scoped separately.

Autotrader UKMotors.co.ukCazoo listingsCinchCarwowMobile.deAutoScout24La CentraleLeboncoin autoCoches.netSubito autoMarktplaats autoBlocketBilbasenOtomotoCars.comCarGurusCarvana listingsAutoTrader USKijiji AutosCarsalesCarsGuideCars24SpinnyCarDekhoDubizzle MotorsYallaMotorFranchised dealer sitesIndependent dealer sitesManufacturer configuratorsAuction platform listingsLeasing and PCP offer pages

Vehicle identification numbers are collected only where a source publishes them openly. We do not query registration or DVLA-type lookup services to enrich listings, since those carry their own terms and often personal data linkage. 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 & Germany The deepest used vehicle marketplaces with rich published specification, which makes trim matching and lifecycle tracking unusually reliable.
United States Enormous dealer base and strong online retail penetration, with heavy demand for residual value evidence from lenders.
France, Spain & Netherlands Well-developed marketplaces plus fast EV transition, driving demand for battery and range field coverage.
India & United Arab Emirates Rapidly growing organised used car retail with high listing churn, where relist detection matters most.

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 automotive data

Dealers and marketplaces dominate, with lenders, remarketers and OEM pricing teams close behind.

Pricing / Stock Manager

Dealer groups
The problem

Stock pricing decisions need to know how comparable vehicles are priced locally and how long they are actually sitting.

What we deliver

Comparable vehicle pricing by trim and mileage band within a radius, with days on lot and reduction history on every comparable.

Metric that moves

Stock turn days

Head of Buying

Used vehicle retailers and online dealers
The problem

Sourcing decisions need to know which segments are moving and where vehicles are mispriced enough to buy.

What we deliver

Segment sell-through velocity plus listings with high days on lot and reduction history, filtered to your buying criteria.

Metric that moves

Gross per unit

Marketplace Product Lead

Automotive marketplaces
The problem

Your pricing guidance and search ranking need comprehensive competitor listing coverage with normalised specification.

What we deliver

Normalised listing data across competing marketplaces with trim matching and lifecycle fields, delivered to your systems.

Metric that moves

Listing quality score

Residual Value / Risk Analyst

Lenders, lessors and OEM captives
The problem

Residual value models need current asking price evidence by trim and age, not lagged guide book values.

What we deliver

Longitudinal asking price panels by make, model, trim, age and mileage band with reduction behaviour included.

Metric that moves

RV forecast accuracy

Remarketing Manager

Fleets and leasing companies
The problem

Defleet timing and channel choice depend on current retail pricing and how quickly segments are selling.

What we deliver

Retail asking price and sell-through velocity by segment and region, so defleet timing is evidence-based.

Metric that moves

Disposal proceeds

Investment Analyst

Consumer and mobility funds
The problem

Automotive retail theses need observable inventory, pricing and velocity data rather than quarterly commentary.

What we deliver

Longitudinal inventory, pricing and velocity panels by retailer, segment and market for direct modelling.

Metric that moves

Signal lead time

Use cases

How automotive data gets used in practice

Four patterns, with the outcome each is judged on.

Comparable-based stock pricing

For each vehicle in stock, comparables are assembled by trim, age and mileage band within a configurable radius, with days on lot and reduction history on every comparable so pricing reflects what is actually selling rather than what is merely listed.

Outcome: Pricing set against genuinely comparable local stock instead of guide book values alone.

Sourcing from ageing and reduced inventory

Listings with high days on lot and repeated reductions are surfaced against your buying criteria, since that population carries the most negotiation leverage.

Outcome: Buying pipelines built from evidenced dealer pressure rather than from asking prices alone.

Residual value evidence for lenders and lessors

Asking prices are tracked by make, model, trim, age and mileage band over time, including reduction behaviour, producing current market evidence ahead of published guide revisions.

Outcome: Residual assumptions tested against observed retail asking prices rather than lagged guides.

Segment velocity and EV mix tracking

Listing volume, sell-through velocity and inventory ageing are tracked by segment, including BEV versus ICE mix shift with battery and range fields available for EV-specific analysis.

Outcome: Demand shifts visible by segment months before registration data reflects them.

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.

Dealer group · UK

Ageing stock looked fresh because competitors kept relisting it

Situation

Comparable pricing analysis used marketplace days-on-lot figures, which reset whenever a dealer relisted, so genuinely stale competitor stock appeared newly listed.

What we ran

Relist detection using specification, mileage, dealer and image fingerprinting, with days_on_lot continued from original first-seen and reduction history joined.

Result

Comparable analysis began reflecting real market age, changing pricing decisions on slow-moving segments.

Lender · EU

Residual value assumptions lagged the market by months

Situation

Residual modelling relied on published guide values, which trailed observable retail movement during a period of rapid EV price change.

What we ran

Longitudinal asking price panels by make, model, trim, age and mileage band, including reduction behaviour and EV battery and range fields.

Result

Residual assumptions were tested against current retail evidence ahead of guide revisions.

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 automotive data collection in-house or hire it as a service?

Trim normalisation and lifecycle identity across relistings are the parts in-house builds consistently lack.

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 relisting detection decides whether days-on-lot means anything

Days on lot is the most useful field in automotive data and the easiest to get wrong. The reason is behavioural: dealers relist ageing stock to reset the visible counter and regain search prominence.

What happens without relist linking

  • Ageing stock looks fresh. A vehicle at 120 real days appears as 5, which removes exactly the signal you were buying the data for.
  • Sell-through looks faster than it is. Each relist reads as one listing ending and another beginning, inflating apparent velocity.
  • Negotiation leverage disappears. The dealers under most pressure are precisely the ones whose pressure the data now hides.
  • Reduction history fragments. A vehicle reduced three times across two relists shows as one reduction, understating movement.

How we link identity

Listings are matched across relists on specification, mileage, dealer and image fingerprinting. Mileage is a strong signal here: a relisted vehicle usually carries the same or near-identical mileage, which distinguishes it from a genuinely different unit of the same trim.

Image fingerprinting matters because dealers often reuse the same photography. Where a relist is detected, days_on_lot continues from the original first-seen date, and the reduction history is joined rather than restarted.

Confidence is scored, and where linking is uncertain we flag it rather than merging two vehicles that might be different. In a category where mileage and trim can genuinely coincide, a wrong merge is worse than an acknowledged uncertainty. The same reasoning applies in our real estate service, where relisting behaviour is near-identical.

Asking price is not transacted price, and we will keep saying so

Every automotive data buyer eventually asks for transacted prices. It is the right thing to want and it is not publicly available.

What is and is not observable

  • Observable: asking price, price reductions, how long a listing was live, when it disappeared, and the dealer holding it.
  • Not observable: the price agreed, whether a trade-in was involved, what finance terms applied, and whether the vehicle sold at all.

A listing disappearing can mean sold, withdrawn unsold, moved to auction, or moved to another platform. We report sold_or_withdrawn and refuse to assert a sale, because inflating sell-through is the single easiest way to make this dataset misleading while looking more useful.

What asking price data is genuinely good for

  • Relative pricing. Where your stock sits against comparable local stock, which is a real and actionable comparison.
  • Direction and velocity. Asking prices move with the market, and time-to-disappear by segment is a strong demand proxy even without confirmed sales.
  • Negotiation evidence. Reduction history on comparable vehicles is a concrete input to both buying and selling conversations.
  • Residual direction. Current asking evidence leads published guide revisions, which is why lenders use it despite the absence of transacted data.

If your model genuinely requires transacted prices, the routes are auction data licences, dealer management system partnerships or registration-linked datasets — all licensed, none obtainable by scraping. We will point you there rather than sell a proxy dressed up as the real thing.

How it works

How an automotive data engagement goes live in 5 to 10 business days

Markets, sources and whether dealer site coverage is needed are scoped first, since dealer sites are more work than marketplaces.

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. Geocoded listings support radius-based comparable analysis directly in PostGIS or BigQuery GIS.

Compliance & data ethics

We collect publicly accessible vehicle listing pages from marketplaces, dealer sites and manufacturer configurators. We do not query registration lookup services, access dealer management systems, or collect private seller personal contact details. VINs are collected only where a source publishes them openly.

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.

Days on lot
Elapsed days since a vehicle first appeared, linked across relistings. Dealers relist ageing stock to reset the visible counter, so unlinked figures systematically understate real age.
Trim normalisation
Mapping inconsistently described trim levels to a standard taxonomy. It matters because the same model across two trims can differ 20% in value, so matching on model alone compares different vehicles.
Sold or withdrawn
The status we report when a listing disappears. It can mean sold, withdrawn unsold, moved to auction or moved platforms, and asserting a sale inflates every velocity metric built on the data.
FAQ

Automotive data scraping: frequently asked questions

What dealer, marketplace and lending teams ask during evaluation.

No, and no scraping vendor can. Transacted prices are not publicly published, and a listing disappearing does not prove a sale — it can equally mean withdrawn unsold, moved to auction, or moved to another platform.

We report sold_or_withdrawn and refuse to assert a sale. If your model requires transacted data, the routes are auction data licences, dealer management system partnerships or registration-linked datasets. All are licensed, and we will point you there rather than sell you a proxy presented as the real thing.

Relists are detected and linked, so days_on_lot continues from the original first-seen date rather than restarting. Matching uses specification, mileage, dealer and image fingerprinting — mileage is especially strong, since a relisted vehicle carries near-identical mileage.

This is the field most clients build their analysis on, and without relist linking it is actively misleading: ageing stock appears fresh, which removes exactly the signal you bought the data for.

Around 96% to a normalised trim taxonomy, with a confidence score on every listing. Fuel type and transmission are treated as hard constraints rather than soft signals.

Trim matters enormously here — the same model across two trim levels can differ 20% in value — and listings describe it inconsistently with abbreviations and optional packs folded into titles. Below your chosen confidence threshold, records arrive flagged rather than force-matched.

Yes, as first-class fields rather than description text: battery capacity in kWh, quoted WLTP or EPA range, charging speed where published, and battery health or state-of-health claims where a listing includes them.

In electric vehicles these fields drive price more than trim does, so leaving them in free text makes the dataset far less useful. Where a listing does not publish them, the field is null rather than estimated from the model name.

Yes, and dealer sites are often more valuable because stock appears there before or instead of marketplace syndication, and finance offers are usually published there.

They are also considerably more work: thousands of individual sites on many different platforms. We scope dealer coverage separately from marketplace coverage and prioritise the dealer groups that matter to you, rather than attempting exhaustive coverage that would inflate cost for marginal benefit.

Only where a source publishes them openly, which some markets and platforms do. We do not query registration or lookup services to enrich listings, because those carry their own terms and frequently link to keeper or owner personal data.

Where VINs are absent, our listing identity is built from specification, mileage, dealer and image fingerprinting, which is sufficient for relist linking and comparable matching in practice.

Yes, including manufacturer list prices by trim, option and pack pricing, and published finance representative examples. Configurator collection is technically awkward because options are interdependent and pricing changes as selections are made.

We collect the published price structure rather than attempting to enumerate every possible configuration, which would produce combinatorial volume for little analytical gain. Where a manufacturer publishes campaign discounts, those are captured as separate fields with dates.

We collect the vehicle and pricing data, and we do not collect private seller names, phone numbers or contact details. Those are personal data belonging to individuals rather than business information.

Private listings are useful for market analysis because they price differently from dealer stock, so excluding them entirely would distort segment pricing. Dealer versus private classification is delivered as a field so you can separate them in analysis.

We quote individually. The drivers are market count, whether dealer site coverage is needed alongside marketplaces, listing volume, and refresh frequency — plus whether lifecycle tracking with history reconstruction is required, since that needs continuous coverage of the same listing population.

A defined market and segment across major marketplaces at daily refresh sits at the lighter end. Multi-market coverage including thousands of dealer sites with sub-daily new-listing alerting sits higher. One scoping call, a free pilot on your own market within 48 hours, then a fixed monthly quote. Request a quote.

See real vehicle data for your own market

Send us a market, segment or stock list. We return listings with trim matching, days on lot and reduction history within 48 hours.

Free pilot, no card, no obligation. We'll be clear that asking prices are not transacted prices.
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Co-Founder / Head of Product at Upright Data Inc.
2 min
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"Actowiz delivered impeccable results for our company. Their team ensured data accuracy and on-time delivery. The competitive intelligence completely transformed our pricing strategy."
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Iulen Ibanez
CEO / Datacy.es
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"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."
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Febbin Chacko
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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.

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Proven track record delivering enterprise-grade web scraping and data intelligence solutions.
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Blog

EU AI Act for Data Teams: What Scrapers Must Change in 2026

The EU AI Act impact on web scraping & AI training data GPAI transparency, copyright reservations, prohibited practices & a compliance checklist from Actowiz.

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Case Study

B2B Supplier Automates Government Tender Discovery from GeM & eProcure

How a B2B supplier replaced manual tender-portal checking with an automated, filtered feed of relevant government tenders from GeM and CPP/eProcure never missing a bid deadline again.

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Report

FIFA World Cup 2026 Aftermath: Hotel & Airfare Normalization in Host Cities (Data Study)

Actowiz Solutions tracks post–World Cup 2026 travel pricing — hotel ADR & airfare normalization across host cities, event-premium decay data & lessons for travel teams.

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