Platform · Enterprise

Enterprise Data Scraping

Most rental data is airport data. This operator's strength is everywhere else, which makes the usual panel design miss the point.

Enterprise data scraping collects rental rates, class availability and fee structures with an emphasis on neighbourhood and downtown locations rather than airports. That matters because almost all rental rate data is airport-only, and airport pricing carries concession fees and a different demand pattern — so an airport-only panel is a coverage hole rather than a sampling choice.

Every other rental page in this set implicitly assumes airports. This one is where that assumption costs you something.

Get a free sample See sample data

Free pilot on your own Enterprise list, returned in 24 hours. No card, no trial clock — and you keep the sample data either way.

enterprise.jsonl LIVE FEED

{"company":"enterprise","location_type":"neighbourhood", "location_hours":"as published, retail hours","location_open":true, "normalised_class":"compact", "base_rate":142.00,"fee_airport":0.00, "total_estimated":158.40} {"location_type":"airport","base_rate":164.00, "fee_airport":41.80,"total_estimated":228.90, "note":"the airport premium is 45% on the TOTAL, not on the base rate"} {"location_open":false,"class_available":"null", "network_share_by_location_type":{"neighbourhood":0.78,"airport":0.22}, "caution":"a closed branch is NOT a class being unavailable"}

3 of 3,884,110 location-class-date rows airport and neighbourhood are SEPARATE panels · schema v1.0

Independence and trademarks. Actowiz Solutions is not affiliated with, endorsed by or connected to Enterprise or its owners. Enterprise and related marks belong to their respective owners, used here only to name the publicly accessible source this service collects from.

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

Enterprise at a glance

How we handle Enterprise specifically

Platform-specific handling, not a generic retail template pointed at a different domain.

Company: Enterprise — strong neighbourhood network
The point: Most rental data is airport-only
Why that matters: Airport pricing is structurally different
Fees: Airport concession and facility fees do not apply off-airport
Demand: Different pattern — local and replacement rather than travel
So: location_type is a required field
Panel design: Airport and neighbourhood are separate panels
Refresh: Daily per pickup date; lead time matters less off-airport

Platform specifics

Off-airport is a different market

These are the reasons a Enterprise dataset needs its own handling rather than a shared retail schema.

Airport and neighbourhood rental price differently, for structural reasons

Airport locations carry costs and demand patterns that neighbourhood locations do not.

Concession and facility fees apply at airports and materially raise the total.
Demand is travel-driven at airports and more local at neighbourhood sites — including replacement rental, which follows a different cycle entirely.
Lead time behaves differently. Airport bookings are made further ahead; neighbourhood bookings frequently are not.
Competitive sets differ. A neighbourhood branch competes with other neighbourhood branches.

So location_type is a required field and airport and neighbourhood are separate panels, not one panel with a flag. Pooling them produces a rate series that moves with location mix.

What an airport-only panel misses

Most of this operator's network, and the part of the rental market that does not involve a flight. For anyone analysing rental pricing as a whole rather than as a travel input, that is the larger share.

network_share_by_location_type ships per market so the coverage picture is visible.

Operator mechanics, and what differs off-airport

Mechanics

Applied as our Hertz page sets out: class not car, per-day derived from a total, each fee its own field, cover as an option, no fleet or utilisation data.

What differs off-airport

No airport concession or facility fees, so the fee stack is shorter and the base rate is a larger share of the total.
Opening hours matter. Neighbourhood branches keep retail hours rather than airport hours, so availability has a daily pattern airports do not.
One-way availability is narrower, and drop fees behave differently.
Weekend and weekday pricing diverge on a different pattern.

location_hours is captured where published, and location_open is distinct from a class being unavailable — the same distinction our Uber Eats Japan page draws for retail-partner fulfilment.

What we do not collect

Fleet composition, utilisation, replacement-rental volumes, insurer arrangements, renter or booking data. The replacement market runs on commercial arrangements that are not published, and we do not infer them.

Scope

What we collect on Enterprise, and what we do not

The right column matters more than the left. Anyone can list fields; the limits are what tell you whether the dataset will hold up.

✅ What we collect

location_type as a required field, with airport and neighbourhood as separate panels
network_share_by_location_type per market
location_hours where published, with location_open distinct from class unavailability
Vehicle class, location pair and date range on every record
Per-day derived from the total, never collected
Each fee as its own labelled field, with the shorter off-airport stack visible
Cover options with prices, never added into the rate
normalised_class with confidence, flagged where it does not map
Lead time derived, with the different off-airport pattern visible

❌ What we do not, and why

Airport and neighbourhood rates pooled into one series
An airport-only panel presented as rental market coverage
A closed branch recorded as a class being unavailable
Replacement-rental volumes or insurer arrangements
Fleet composition, utilisation, renter or booking data

Core Enterprise fields

The full dictionary is agreed during scoping. These are the fields specific to this platform.

Field	What it is on this platform
company / location_id / location_type	Required. Airport, neighbourhood or other
location_hours / location_open	Retail hours matter off-airport
network_share_by_location_type	Per market. The coverage picture
operator_class_raw / normalised_class / class_map_confidence	As published, mapped, and how sure
pickup_location / dropoff_location / is_one_way	The location pair
pickup_date / rental_days / observed_at / lead_time_days	The range, and both dates
base_rate / currency / rate_per_day_derived	Base, and per-day derived
fee_airport / fee_licensing / fee_oneway / fee_driver	Shorter stack off-airport
total_estimated / total_basis	Total, and what it includes
cover_options	Options with prices. Not in the rate
class_available	Bookable, not a count

Use cases

What teams do with Enterprise data

Off-airport rental market coverage

Neighbourhood and downtown locations, which most rental datasets omit entirely — and which are the larger share of the market for anyone analysing rental rather than travel.

Airport premium measurement

Airport and neighbourhood as separate panels with the fee stacks visible, so the airport premium is measured rather than assumed.

Branch availability patterns

Location hours with open state distinct from class unavailability, since neighbourhood branches keep retail hours and airports do not.

Cross-company class comparison

Normalised class with confidence and unmappable classes flagged, as across all rental operators.

The 24-hour sample — run on your sources, not ours

Send us a Enterprise item or category list. We run real collection against it and return the output within 24 hours, with the platform-specific fields populated so you can check them yourself rather than take our word for it.

Real extraction from your actual sources
Returned within 24 hours
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.

Enterprise is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. Enterprise data becomes useful when it sits next to the competitor set on one schema, refreshed on one schedule, so a price index or availability comparison is genuinely like-for-like.

That is what travel & hospitality data covers, and a Enterprise-only engagement can be expanded into it without rebuilding. If you already know you need several platforms, start there instead — it is the same pipeline and usually the better scoping conversation.

FAQ

Enterprise data scraping: frequently asked questions

Platform-specific questions, including what cannot be collected here.

Because airport and neighbourhood rental price differently for structural reasons. Concession and facility fees apply at airports and materially raise the total, demand is travel-driven rather than local, and lead time behaves differently.

Pooling them produces a rate series that moves with location mix rather than with pricing.

Most of this operator's network, and the part of the rental market that does not involve a flight. For anyone analysing rental pricing as a whole rather than as a travel input, that is the larger share.

We ship network share by location type per market so the coverage picture is visible.

Yes — the stack is shorter, with no concession or facility fees, so the base rate is a larger share of the total.

That is worth knowing because the usual warning that base rate is a minority of the total applies less strongly off-airport.

Because neighbourhood branches keep retail hours rather than airport hours, so availability has a daily pattern airports do not have.

A closed branch is not the same as a class being unavailable, and we keep the two distinct.

Only what is publicly quoted. Replacement rental runs on commercial arrangements with insurers and repairers that are not published, and we do not infer them.

What we collect is the publicly quoted rate at a location, which is a different thing.

We quote individually. Location count is the driver, and a neighbourhood panel has far more locations than an airport one — which is the point, and also the cost.

One scoping call, a free pilot within 24 hours including network share by location type, then a fixed monthly quote. Request a quote.

See real Enterprise data before you commit to anything

Send us an item or category list. We return the output within 24 hours with the platform-specific fields populated.

Get a free sample Talk to an engineer

Free pilot, no card, no obligation. If we cannot collect a field you need on this platform, the sample shows you that too.

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!"

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."

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."

Febbin Chacko

-Fin, Small Business Owner