Scalable web, app and AI-powered collection across 40+ countries.
All services →Repricing driven by competitor moves rather than a weekly review.
Explore →
Pricing & Promotions
Brand & Intelligence
Digital Shelf & Search
Assortment
For RetailersWhich solution fits?
Talk to ExpertTalabat, Careem Quik and Noon Minutes across 18 GCC cities.
Launch Demo →Tesco, Sainsbury's, Asda, Morrisons and Aldi, daily.
Get Early Access →
E-Commerce Dashboard
Flipkart Insights (Live) #1
Grocery Intelligence
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Grocery Price (U.S.)
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Quick Commerce (India) HOT
Food & Restaurant
Fashion Intelligence
Automotive
Travel & Hospitality
Real Estate
Food Delivery Intelligence NEW
OTT & Streaming NEW
By Use Case
Pricing Intelligence
Digital Shelf Analytics
MAP Monitoring HOT
Cross-Border Price Parity NEW
Share of Search
Review Sentiment
Kitchen Market Gaps NEW
Dynamic Pricing / AI Repricing NEW
Promotions & Deals Alerts NEW
B2B / POI & Lead Data NEW
By Region
Flipkart Real-Time Insights
Quick Commerce — Zepto · Blinkit
Pincode Price Tracker
GCC Q-Commerce — Talabat · Noon NEW
UK Grocery — Tesco, Sainsbury's, Asda
AU Grocery — Coles & Woolworths NEW
Sources we already run pipelines against. Download a real sample from any of them.
Browse coverage →
Marketplace Scrapers
Developer APIs
Universal APIs
Delivery & SDKsFixed monthly retainer, named engineer, no per-request metering.
Managed Data API →
Knowledge Center
Guides & Playbooks
Downloads & Tools
Trust & Company
Scalable web, app and AI-powered collection across 40+ countries.
All 58 services →Repricing driven by competitor moves rather than a weekly review.
Explore → Pricing & Promotions
Brand & Intelligence
Digital Shelf & Search
Assortment For RetailersWhich solution fits?
Talk to ExpertSources we already run pipelines against. Download a real sample from any of them.
Browse coverage →Talabat, Careem Quik and Noon Minutes across 18 GCC cities.
Launch Demo →Tesco, Sainsbury's, Asda, Morrisons and Aldi, daily.
Get Early Access → E-Commerce Dashboard
Flipkart Insights (Live) #1
Grocery Intelligence
Grocery Price (U.S.)
Quick Commerce (India) HOT
Food & Restaurant
Fashion Intelligence
Automotive
Travel & Hospitality
Real Estate
Food Delivery Intelligence NEW
OTT & Streaming NEW
By Use Case
Pricing Intelligence
Digital Shelf Analytics
MAP Monitoring HOT
Cross-Border Price Parity NEW
Share of Search
Review Sentiment
Kitchen Market Gaps NEW
Dynamic Pricing / AI Repricing NEW
Promotions & Deals Alerts NEW
B2B / POI & Lead Data NEW
By Region Flipkart Real-Time Insights
Quick Commerce — Zepto · Blinkit
Pincode Price Tracker
GCC Q-Commerce — Talabat · Noon NEW
UK Grocery — Tesco, Sainsbury's, Asda
AU Grocery — Coles & Woolworths NEW
Marketplace Scrapers
Developer APIs Universal APIs Delivery & SDKsFixed monthly retainer, named engineer, no per-request metering.
Managed Data API → Knowledge Center
Guides & Playbooks
Downloads & Tools
Trust & CompanyCatalogue and pricing, with licensing windows tracked by country.
A title on a streaming service is not a product with a price. It is a licence with a country and an expiry date. Catalogues that ignore the country and the expiry describe a library that exists nowhere and lasts forever.
Free pilot on your own sources, returned in 48 hours. No card, no trial clock — and you keep the sample data either way.
Last verified 5 August 2026 by the Actowiz Solutions Data Engineering team.
OTT and entertainment data scraping is the automated collection of what streaming platforms publish: which titles are in the catalogue, in which country, under what access type, with what technical and language options, alongside subscription plan pricing per market.
The structural fact that shapes the whole dataset is that streaming rights are territorial and time-limited. A title is not stocked, it is licensed — for a country, for a period.
We put country on every record and track added_at, removed_at, days_in_catalogue and previously_removed, so window behaviour is measurable rather than inferred from snapshots.
Viewership, watch time, completion rates and revenue. No streaming platform publishes these. Figures circulating in the market are either modelled by panel providers or come from licensed measurement services. We deliver what is observable in the catalogue and let you join licensed measurement data if you have it.
Catalogue availability and window churn are the core. Ad-tier pricing is the fastest-growing request.
What is watchable, where.
The dimension most datasets miss.
Plans by market, including ad tiers.
As published by the platform.
Localisation depth as a signal.
Where a title can be found.
A managed engagement, not a tool licence. We own the pipeline and everything that breaks in it.
Every engagement delivers a documented schema. These are the core fields; the full dictionary runs to 90+ and is agreed during scoping.
| Field | Type | What it captures | Refresh |
|---|---|---|---|
title_key |
string | Cross-platform title identity, so the same title is joinable across services | Every run |
platform / country |
string | Service and territory, both mandatory since availability is territorial | Every run |
access_type |
enum | subscription, ad_supported, rental or purchase, which varies by country | Daily |
added_at / removed_at |
date | When the title entered and left the catalogue, from continuous observation | Daily |
days_in_catalogue |
int | Elapsed days, the basis for window length analysis | Daily |
previously_removed |
int | How many times the title has cycled out and back, so returns are not read as new | Daily |
is_original |
boolean | Platform original versus licensed content, which drives different strategy questions | Weekly |
audio_langs / sub_langs |
int / array | Language availability counts and lists, a proxy for localisation investment | Weekly |
max_quality_listed |
string | Highest streaming quality listed for the title | Weekly |
plan_name / price_month / ad_supported |
string / decimal / boolean | Plan tier name, price in local currency and ad-supported status | Daily |
viewership |
constant | Always not_collected, so the exclusion is visible in the data itself | Every run |
viewership is a constant field reading not_collected. It exists so anyone auditing the dataset can see the exclusion is structural, and so nobody downstream assumes a missing column means the figure was simply unavailable that day.
Coverage is built to your platform and country set. Catalogue size varies enormously by territory.
We collect publicly visible catalogue and plan pages without accounts. We do not access video streams, do not circumvent DRM or geo-restrictions, and do not collect subscriber-only content. Request a source we don't list →
We deliver into 40+ countries. These are the markets where this particular service is requested most, and the reason demand concentrates there.
| Market | Why demand concentrates here |
|---|---|
| United States | The deepest platform set and the most competitive ad-tier pricing activity, which makes plan monitoring most valuable here. |
| United Kingdom & European Union | Territorial licensing is most fragmented across EU markets, so per-country window tracking matters most. |
| India | Rapid regional-language service growth with very different plan structures and price points from Western markets. |
| Southeast Asia & GCC | Fast-growing subscriber bases with heavy catalogue churn as licensing deals are established and lapse. |
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 →
Content strategy and licensing teams dominate, with distribution and investors following.
Competitor catalogue composition and window behaviour are invisible without continuous per-country tracking.
Catalogue size and composition by country with original share, plus window length distribution by platform.
Catalogue competitiveness
You cannot see where your titles are available, under what access type, or when windows lapse across markets.
Per-title availability across platforms and countries with added and removed dates and repeat-appearance counts.
Window utilisation
Territory-level distribution gaps and unexpected availability are found by accident rather than monitored.
Title presence by country and platform with exclusivity detection, so gaps and overlaps surface as data.
Territory coverage
Competitor plan structures and ad-tier pricing differ by market and change without announcement.
Plan names, prices, stream limits and quality caps per country, with change detection and dates.
ARPU
Catalogue churn is continuous and manual tracking cannot keep pace across platforms and territories.
Continuous catalogue monitoring with added and removed events across your competitive platform set.
Response time
Streaming theses need observable catalogue and pricing signals rather than modelled viewership.
Longitudinal catalogue size, original share, window churn and plan pricing panels by platform and country.
Signal lead time
Four patterns, with the outcome each is judged on.
Title availability is tracked per platform and country with added and removed dates and repeat-appearance counts, so window starts, lapses and renewals are observable rather than reconstructed from contracts.
Outcome: Window behaviour measured across territories instead of tracked manually per deal.
Catalogue size, genre mix, release-year distribution and original share are computed per country, showing how competitor libraries differ by market.
Outcome: Content strategy informed by measured competitor catalogue shape per territory.
Plan structures including ad-supported tiers are collected per country with change detection, revealing pricing moves as they happen rather than at the next review.
Outcome: Pricing decisions made against observed competitor plan structures by market.
A content owner's titles are tracked across platforms and countries, surfacing territories where a title is absent and platforms where it appears unexpectedly.
Outcome: Distribution gaps and unexpected availability identified as data rather than discovered anecdotally.
Clients rarely permit naming. These are real engagement shapes with identifying detail removed, so you can judge whether the work resembles your situation.
Distribution monitored where its titles appeared using manual checks per platform and country, which lagged reality by weeks and missed short windows entirely.
Continuous per-country catalogue collection with added and removed dates, access type and repeat-appearance counts across the platform set.
Window starts, lapses and unexpected availability surfaced as data rather than being discovered anecdotally.
Monthly catalogue snapshots counted every returning title as a new addition, overstating competitor acquisition activity.
Daily collection with previously_removed counts, distinguishing genuine additions from licences cycling back in.
Measured catalogue growth fell substantially once returns were separated from new acquisitions.
Examples are anonymised at client request. Named references are available on request under NDA. See published case studies →
Before you commit to anything, we run this service against your own sources and send you the output. If the coverage isn't there, the sample will show you that too — which is the point. We would rather lose the deal at the pilot than at month three.
Same collection pipeline and same QA underneath. The difference is who holds the schedule and how the data reaches you.
We own the collection, the QA and the delivery. You receive clean data on a schedule and never touch a scraper.
Best fit: Teams who need the data, not the infrastructure.
The same collection pipeline exposed as an authenticated REST endpoint your systems query directly.
Best fit: Product and engineering teams building on live data.
A defined pull for a specific question — market sizing, diligence, a pitch, a one-off audit.
Best fit: Research, strategy and diligence work with a deadline.
Every engagement is quoted individually, because the honest answer depends on your scope: how many sources, how many records, how often, and how the data reaches you. We scope it with you, run a free pilot on your own sources, and then quote a fixed monthly figure — no per-request metering and no overage billing when volumes move. Request a quote and you will have a number after one call.
Per-country collection multiplies volume fast, and catalogue churn means gaps in continuity destroy the window data.
| 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 |
In most datasets, a missed collection day is an inconvenience. In OTT catalogue data it permanently destroys information, and it is worth understanding why before choosing a collection cadence.
added_at is known only to within seven days. Window length analysis inherits that error permanently.Daily collection per platform per country as the baseline, with the archive start date reported so you know what the window data actually covers. For titles present before our collection began, added_at is recorded as archive-limited rather than presented as a licence start date.
That distinction matters commercially: a title showing 277 days in catalogue when our archive is 277 days old has not been there 277 days, it has been there at least that long. We flag those records rather than letting them silently understate window lengths across the dataset.
The most common request on this service is viewership data, and it is the one thing we will not supply. The reasoning is the same as on our other pages where we decline modelled figures.
We deliver viewership as a constant field reading not_collected, so the exclusion is visible in the data rather than looking like a gap. Where a platform publishes a top-ten or trending list, we collect it as a rank observation with its platform and country, clearly labelled as platform-published ranking rather than measured viewership.
If you need audience measurement, licence it from a measurement provider and join it to our catalogue data on title_key. Catalogue availability plus licensed measurement is a far stronger combination than either alone — and it is honest about which number came from where, which matters when the analysis reaches an investment committee.
Platforms, territories and whether episode-level granularity is needed are scoped first, since those three multiply volume.
You send us target sites, regions, SKUs or keywords. We return a field-level schema proposal, coverage estimate and refresh recommendation — usually within two working days.
We extract a real sample from your actual targets so you can inspect field fill rates, edge cases and match quality before any commitment.
Our engineers build extractors, then wire validation rules: type checks, range checks, duplicate detection and golden-record comparison against a manually verified subset.
Feeds run at your chosen cadence and land in the warehouse or bucket you already use. Schema changes are versioned and announced before they ship.
We watch coverage drift, fill rates and source changes daily. A named engineer owns your account, and layout breaks are fixed by us — not queued for you.
JSON, JSONL, CSV, Parquet or XLSX, delivered to Amazon S3, Google Cloud Storage, Azure Blob, SFTP, Snowflake, BigQuery, Databricks or a REST/GraphQL endpoint. Webhooks fire on completion, and every batch ships with a manifest containing row counts, schema version and QA results so your pipeline can fail loudly instead of silently ingesting a bad file.
We collect publicly visible catalogue, title and plan pages without accounts or credentials. We do not access video streams, download content, circumvent DRM or geo-restrictions, or collect subscriber-only content. We do not provide viewership, watch time or revenue figures, none of which platforms publish.
These are contractual, not marketing copy. They appear in the engagement document.
| Commitment | What we hold ourselves to |
|---|---|
| Pilot turnaround | A real sample from your own sources within 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. |
Plain definitions of the terms used on this page, so procurement and legal reviewers are working from the same vocabulary as your data team.
What content, licensing and pricing teams ask during evaluation.
No. No streaming platform publishes it. Figures circulating in the market are either modelled by panel-based providers or come from licensed measurement services under stated methodologies, and scraping cannot reproduce either.
We deliver viewership as a constant field reading not_collected so the exclusion is visible rather than looking like a gap. If you need audience measurement, licence it and join it to our catalogue data on title_key.
Because streaming rights are territorial. The same platform has substantially different catalogues in different countries, the same title can be subscription in one market and rental in another, and plan structures differ by market beyond currency.
A catalogue collected in one country describes only that country. Treating it as the platform's catalogue is the most common error in OTT datasets.
Through continuous daily collection per platform per country, recording added_at, removed_at, days_in_catalogue and previously_removed.
That last field matters: licences get renewed, so titles cycle out and back. Without repeat-appearance counts, every return reads as a new acquisition and apparent catalogue growth is inflated.
Their added_at is recorded as archive-limited rather than presented as a licence start date. A title showing 277 days when our archive is 277 days old has been there at least that long, not exactly.
We flag those records explicitly. Letting them pass silently would understate window lengths across the whole dataset, and window length is one of the main things clients measure.
Yes, where the platform exposes it — series, season and episode availability, since seasons can be licensed separately and arrive or leave independently.
Episode-level collection multiplies record volume considerably, so we scope whether you need it. For catalogue composition analysis, title level is usually sufficient; for licensing work on series, episode level often is not optional.
Yes, and it is the fastest-growing request on this service. Plan names, monthly and annual prices, ad-supported status, concurrent stream limits and quality caps, per country with change detection.
Ad tiers vary considerably by market — availability, price and what is restricted all differ — so a single global view of a platform's plan structure is misleading.
No, categorically. We collect catalogue, title and plan pages only. We do not access video streams, download content, circumvent DRM or bypass geo-restrictions.
Per-country catalogue collection is done through legitimate territory access rather than by defeating geo-blocking. Where we cannot obtain a territory's catalogue legitimately, we say so rather than circumventing the restriction.
Yes, at about 93.7%, using title, release year, runtime and published credits. Matches carry a confidence score and uncertain ones are flagged rather than asserted.
The residual is concentrated in re-releases, regional retitling and content with inconsistent metadata. We report the unmatched population rather than dropping it, since a title that cannot be matched is often exactly the one worth investigating.
We quote individually, driven by platforms times countries times granularity. Episode-level collection across many platforms and territories is the expensive configuration.
A focused platform set across three or four countries at title level with daily refresh sits at the lighter end. Broad platform coverage across twenty territories at episode level sits considerably higher. One scoping call, a free pilot on your own platforms and territories within 48 hours, then a fixed monthly quote. Request a quote.
Send us platforms and countries. We return catalogue availability with window dates, access types and plan pricing within 48 hours.
Free pilot, no card, no obligation. No viewership estimates — those are not published by anyone.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.
Watch how businesses like yours are using Actowiz data to drive growth.
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