Introduction

TL;DR: Actowiz analyzed catalog metadata — 45,000+ title listings — and plan pricing across Netflix, Prime Video, and JioHotstar in India and the US. Findings: Prime Video listed the largest raw catalog while Netflix led in originals share; JioHotstar's regional-language depth (X% of its Indian catalog) is its structural moat; and per-title-value (catalog ÷ plan price) varies X× between markets for the same platform.

Why OTT Catalog Data Matters

Streaming competition is fought on three measurable axes: catalog breadth, content mix, and price architecture. Studios deciding licensing strategy, platforms benchmarking content gaps, and analysts modelling churn all need the same thing — structured, current catalog metadata. Platforms don't publish it; their public catalog pages reveal it.

Methodology

Parameter	Coverage
Platforms	Netflix, Prime Video, JioHotstar
Markets	India, USA
Title listings captured	45,000+ (movies + series, metadata only)
Fields	Title, type, genres, release year, language, audio/subtitle availability, maturity rating, original flag, add/remove dates
Pricing	All plan tiers, ad-supported variants, mobile-only plans, bundle pricing
Window	90 days with weekly catalog deltas

We capture listing metadata only — no media content — and track adds/removals to measure catalog churn.

Finding 1: Catalog Size vs Catalog Depth

Raw listed titles (India): Prime Video X > Netflix Y > JioHotstar Z.
Originals share: Netflix X%, Prime Y%, JioHotstar Z%.
Catalog churn: X% of titles rotated (added/removed) within 90 days — licensing-driven turnover is measurable weekly, and it's the signal behind "what's leaving" consumer anxiety.

Finding 2: Regional Language Is the Indian Battlefield

JioHotstar's catalog skews X% non-Hindi/non-English (Tamil, Telugu, Malayalam, Bengali...), versus Y% on Netflix and Z% on Prime. Combined with sports streaming, this explains its plan architecture: reach over ARPU. For content acquirers, the gap analysis by language × genre is a direct licensing-opportunity map.

Finding 3: Price Architecture & Per-Title Value

Metric (India)	Netflix	Prime Video	JioHotstar
Entry plan	₹XXX	₹XXX	Titles per ₹100/month (entry)
Top plan	₹XXX	₹XXX	24
Ad tier available	Y/N	Y/N	12
Titles per ₹100/month (entry)	X	X	12

US-vs-India comparison: the same platform's per-title value differs X× across markets — quantifiable evidence of regional price discrimination strategy.

Who Uses OTT Catalog Data

Studios & content owners: find genre/language gaps per platform to target licensing pitches.
Streaming platforms: benchmark catalog freshness, originals ratio, and pricing position.
Analysts & investors: catalog churn + pricing moves as leading indicators ahead of subscriber disclosures.
EPG/discovery apps: clean, current title metadata feeds.

FAQs

What exactly is in an OTT catalog dataset?

Listing metadata: titles, type, genres, languages, release years, maturity ratings, original flags, availability windows, and plan pricing — no media files or copyrighted content, only publicly visible catalog information.

How do you track titles leaving a platform?

Weekly catalog snapshots are diffed: titles present last week and absent this week are flagged as removals, building an add/remove history that measures licensing churn.

Can you cover other platforms and countries?

Yes — Disney+, Apple TV+, SonyLIV, Zee5, Max, Hulu, Crunchyroll and others, in any market where catalogs are publicly browsable; multi-country availability matrices are a common deliverable.

How current is plan pricing data?

Plan pages are monitored continuously; price changes, new ad tiers, and bundle changes are captured within 24 hours of going live.

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