Platform · Rental and purchase storefronts

Rental and Purchase Storefront Data

A new film can rent for one price, buy for another, and cost more in 4K. That is three offers.

Rental and purchase storefront data covers stores that rent and sell films and series: Apple TV, Google TV, Prime Video's store and Fandango (formerly Vudu). Each title has several offers — rent or buy, by quality — that change with release windows and sales. Storefronts also rename and close: Vudu became Fandango at Home in 2024 and Fandango in July 2026. Offers and store events are recorded per country, with dates. Every record carries its country, no account, stream or geo-restriction workaround is ever used, and viewership is never collected because no platform publishes it.

A sale price is an offer with an end date. Recording it without one turns a promotion into a price cut.

Get a free sample See sample data

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

tvod_sample.jsonl LIVE FEED

{"title_key":"tk-9001","offer_type":"rent","quality":"4K","price":5.99} {"offer_type":"buy","quality":"HD","price":9.99,"regular_price":14.99,"promo_end":"2026-09-29"} {"store_event":"renamed to Fandango","event_date":"2026-07"}

illustrative sample rows · per countryoffers, not titles · schema v1.0

Independence and trademarks. Actowiz Solutions is not affiliated with, endorsed by or connected to Rental and purchase storefronts or its owners. Rental and purchase storefronts 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

Rental and purchase storefronts at a glance

How we handle Rental and purchase storefronts specifically

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

Source: Rent and buy storefronts
Offers: Rent or buy, by quality
So: one record per offer
Sales: Promotional prices with windows
Early windows: Premium rentals soon after cinema, as offered
Renames: Vudu → Fandango at Home → Fandango
Country: Mandatory
Refresh: Daily

Platform specifics

Offers, not titles

These are the reasons a Rental and purchase storefronts dataset needs its own handling rather than a shared retail schema.

Offer records

A title's page may list several rent and buy options at different qualities, some on sale.

offer_type — rent or buy — and quality — SD, HD, 4K.
price, currency, regular_price and promo_end where shown.
rental_period as stated.
store_event — rename, closure — dated.

Device stores, and what we do not do

Device stores

Console and TV-maker stores are on our device store page.

Licensing windows

Titles are licensed per country and period, so arrivals, departures and returns are dated, as the OTT hub describes.

What we do not do

Make purchases or rentals, sign in, access streams, bypass geo-restrictions, or estimate sales.

Scope

What we collect on Rental and purchase storefronts, 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

Offer type and quality
Price and regular price
Promotion end dates
Rental periods as stated
Store events dated
Country on every offer
Title matched across stores
Offer changes dated
Daily refresh

❌ What we do not, and why

Purchases or rentals
Sign-ins
Streams or downloads
Geo-restriction workarounds
Sales estimates

Core Rental and purchase storefronts fields

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

Field	What it is on this platform
store / country	Mandatory
title_key	Cross-store
offer_type	rent or buy
quality	SD, HD, 4K
price / currency	As shown
regular_price / promo_end	Where shown
rental_period	As stated
first_offered_at	Observed
store_event	Dated
observed_at	Timestamp
viewership	not_collected

Use cases

What teams do with Rental and purchase storefronts data

Digital price benchmarking

By title, quality and country.

Release window tracking

When films reach rental and purchase.

Promotion analysis

Sales and their lengths.

Store landscape

Renames and closures.

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

Send us a Rental and purchase storefronts 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.

Rental and purchase storefronts is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. Rental and purchase storefronts 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 ott & entertainment data covers, and a Rental and purchase storefronts-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

Rental and purchase storefronts data scraping: frequently asked questions

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

Yes, per quality.

With the regular price and the promotion end date where shown.

It became Fandango at Home in 2024 and Fandango in July 2026.

Never.

No.

It depends on stores and countries.

Talk to us.

See real Rental and purchase storefronts 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