MFour Consumer Panel, October XXXX to March XXXX

Entertainment consumers are turning to AI as their discovery engine, comparison tool, and decision partner

Executive Summary

ChatGPT is picking winners in streaming — and most platforms don't even know they're in the race. When consumers ask AI what to watch, they show up undecided three out of four times. But ChatGPT doesn't stay neutral — it pushes platforms X.Xx more than users ever mention them, and by the point of purchase, AI-driven transaction signals outpace actual user intent by XX.Xx. That invisible hand just crowned a new recommendation leader, dethroning Netflix for the first time — and the platforms losing share don't see it happening because it's not showing up in their dashboards. The brands that figure out how to win inside the AI recommendation layer will own the next era of subscriber growth. The ones that don't will keep spending on content nobody's being told to watch.

This analysis tracks ChatGPT entertainment conversations across a complete six-month panel window (October XXXX through March XXXX), observing consumer intent, platform engagement, title affinity, tool use, and the rapid shift from passive streaming to AI-driven purchasing and decision-making.

XX,XXXTotal conversations

XX,XXXUnique user appearances

X monthsObservation window

XX platformsTracked

Section X, Foundation

Universe: X months of chat-driven entertainment discovery

The foundation of all analysis. Monthly conversation volume is relatively stable, averaging X,XXX conversations and X,XXX users per month, indicating a mature, sustained pattern of AI-driven entertainment behavior.

Oct XXXX

X,XXX

conversations, X,XXX unique users

Nov XXXX

X,XXX

conversations, X,XXX unique users

Dec XXXX

X,XXX

conversations, X,XXX unique users

Jan XXXX

X,XXX

conversations, X,XXX unique users

Feb XXXX

X,XXX

conversations, X,XXX unique users

Mar XXXX

X,XXX

conversations, X,XXX unique users

X-Month Total

XX,XXX

Total conversations in the universe. Averaging X,XXX per month.

Avg Monthly Users

X,XXX

Consistent user participation across all months, peak in December.

Maturity signal: Monthly conversation counts vary by less than XX%, indicating a stabilized, recurring user base engaging consistently with entertainment AI discovery.

The AI Decision Funnel

What users ask vs. what ChatGPT tells them

At every stage of the entertainment decision journey, there is a measurable gap between what consumers bring to ChatGPT and what ChatGPT sends them away with. This funnel maps that gap.

What Users Ask

The consumer's voice

vs

What ChatGPT Says

The AI's influence

Conversations Initiated

XX,XXX

Users start entertainment conversations across X months

"Give me a good movie to watch"
"Best movie of XXXX thus far"
"list of good movies"

X

Entry

Responses Generated

XX,XXX

ChatGPT responds to every conversation, averaging ~XX messages per session

Platforms, titles, and recommendations introduced even when users don't ask

Users Mention a Platform

~X,XXX

XX% of conversations include a user-side platform mention

"What's a good funny movie on Hulu?"
"Should I get Netflix or Max?"
"Can I use Apple gift cards for Spotify?"

X

Awareness

ChatGPT Mentions Platforms

~XX,XXX+

Response-side platform mentions are Xx to XXx higher than user prompts

Max/HBO: X,XXX (X.Xx)
Netflix: X,XXX (X.Xx)
Apple TV+: X,XXX (XX.Xx)

X.Xx

Average amplification gap

Users Ask for Recommendations

XXX

X.X% explicitly ask "what should I watch?"

"Suggest movies like these"
"What's a good romantic movie?"
"Recommend something like Stranger Things"

X

Recommend

ChatGPT Picks Winners

XX.X%

Max/HBO wins the most recommendations, overtaking Netflix (XX.X%) for the first time

Max/HBO: XX.X% win rate
Netflix: XX.X%
YouTube: X.X% | Hulu: X.X%

X.Xx

Max/HBO recommended over Netflix

Users Reach Decision Stage

XXX

X.X% show explicit decision-making intent

"Can you buy tickets at the box office?"
"Where do I find movies I purchased?"
"Set up alert to cancel Spotify"

X

Decision

ChatGPT Triggers Tool Use

X,XXX

XX.X% of conversations trigger agentic tool use, pulling live data and integrating services

X,XXX web searches
XX+ API integrations
XXX file/doc searches

XX.Xx

ChatGPT acts far beyond what users request

Users Express Subscribe / Cancel

XX

X.X% explicitly mention subscribing or canceling

"If you cancel your Netflix subscription..."
"I actually signed up to Netflix"
"Should I buy Apple Music?"

X

Action

ChatGPT Facilitates Transactions

X,XXX

YouTube (XXX), Max/HBO (XXX), Netflix (XX), Hulu (XX), Spotify (XX)

Cancel signals: Prime Video (XX), Spotify (XX), Netflix (XX), Hulu (XX)

XX.Xx

More transaction signals than user intent

The amplification gap is the story. Users are passive. Only XX% mention a platform, and just X.X% express subscription intent. But ChatGPT fills every gap: it amplifies platforms X-XXx, picks winners (Max/HBO at XX.X%), triggers tool use in XX.X% of conversations, and surfaces transaction signals at XX.Xx the rate users express them. The AI layer is not reflecting consumer behavior; it is actively shaping it.

Section X, Intent Taxonomy

What consumers ask about: XX distinct entertainment categories

Entertainment conversations cluster into XX distinct categories of intent. General discovery dominates at XX.X%, but specialized intent categories reveal deep engagement around titles, platforms, and transactions.

General / Other Entertainment

XX,XXX conversations (XX.X%)

The largest segment captures broad entertainment engagement that doesn't fit neatly into a single intent category. Common patterns include: open-ended movie and show recommendations ("give me a good movie to watch"), multi-topic conversations that span discovery and research in a single session, creative writing requests involving entertainment IP (fan fiction, scripts, character analysis), trivia and pop culture questions, and casual browsing where users explore genres or actors without a clear purchase or subscription intent. This segment represents the ambient demand layer: users who are engaged with entertainment through AI but have not yet signaled a specific commercial action.

Content Research

X,XXX (X.X%)

Reviews, plot analysis, and deep content exploration.

Discovery / Recommendation

XXX (X.X%)

XXX unique users. The core recommendation engine use case.

Agentic Action

XXX (X.X%)

Transactional conversations triggering tool use.

Price / Deal Check

XXX (X.X%)

Streaming availability and pricing queries.

Casting / Behind the Scenes

XXX (X.X%)

Production and talent inquiries.

Cross-Format (Books/Games)

XXX (X.X%)

Entertainment beyond video.

Device / Setup

XXX (X.X%)

Technical questions.

Platform Comparison

XXX (X.X%)

Which service has what content.

Watch Party / Social

XXX (X.X%)

Social viewing coordination.

Showtime / Tickets

XXX (X.X%)

Theater and live event access.

Subscribe / Cancel Intent

XX (X.X%)

Account and subscription decisions.

Intent concentration: XX% of conversations show classifiable intent (discovery, research, transactions). XX% remain general exploration. The split reveals two distinct user archetypes: active seekers (content researchers, deal hunters) and browsers (passive discovery).

Section X, Platforms

Platform landscape: where users mention platforms, and where AI amplifies

Users mention platforms (prompt side) far less than ChatGPT introduces them (response side). Max/HBO has overtaken Netflix in both user prompt mentions and AI recommendations for the first time in March XXXX.

User-Side Platform Mentions (What People Ask About)

Prompt-side mentions show what platforms are top-of-mind when users initiate conversations.

Platform	Conversations	% of Universe
YouTube	X,XXX	X.XX%
Max / HBO	X,XXX	X.XX%
Netflix	X,XXX	X.XX%
Spotify	XXX	X.XX%
Hulu	XXX	X.XX%
Prime Video	XXX	X.XX%
Peacock	XXX	X.XX%
Disney+	XXX	X.XX%

Platform	Conversations	% of Universe
Tubi	XXX	X.XX%
Apple TV+	XXX	X.XX%
Roku	XXX	X.XX%
Paramount+	XX	X.XX%
Crunchyroll	XX	X.XX%
YouTube TV	XX	X.XX%
Pluto TV	X	X.XX%
AMC+	X	X.XX%

AI-Side Amplification (What ChatGPT Recommends)

Response-side mentions show where ChatGPT directs user attention. Amplification ratio reveals how much ChatGPT recommends platforms beyond what users ask about.

Max / HBO

X,XXX mentions

XX.XX% of universe. X.Xx amplification ratio. Strongest AI recommendation platform.

Netflix

X,XXX mentions

X.XX% of universe. X.Xx amplification. Default backup recommendation.

Apple TV+

X,XXX mentions

X.XX% of universe. XX.Xx amplification. Most overrepresented in recommendations.

Hulu

X,XXX

X.XX% of universe. X.Xx amplification.

Prime Video

X,XXX

X.XX% of universe. X.Xx amplification.

Disney+

X,XXX

X.XX% of universe. X.Xx amplification.

Monthly Trend (Prompt Side)

Platform mentions across the six-month window. Max/HBO overtakes Netflix in March XXXX.

Platform	Oct	Nov	Dec	Jan	Feb	Mar
Netflix	XXX	XXX	XXX	XXX	XXX	XXX
Max/HBO	XXX	XXX	XXX	XXX	XXX	XXX
Hulu	XX	XX	XX	XX	XX	XX
Peacock	XX	XX	XX	XX	XX	XX
Prime Video	XX	XX	XX	XX	XX	XX
Disney+	X	X	X	X	X	XX

Seismic shift in March XXXX: Max/HBO mentioned XXX times vs Netflix at XXX. For the first time in the window, Max/HBO takes top user mention share. Peacock simultaneously collapsed to XX mentions (down from XX avg), signaling viewer fatigue with premium tiers and consolidation toward larger platforms.

How MFour Can Help

MFour's conversation intelligence goes deeper than surface-level mentions. We can identify the specific source links and URLs that ChatGPT references when recommending platforms and titles, giving you visibility into which web properties are driving AI-generated recommendations. By understanding what content ChatGPT pulls from when it suggests your platform (or a competitor's), you can develop a targeted strategy to increase your own platform's AI recommendation rate through optimized content, SEO, and structured data. Contact us to learn more about source-link analysis for your platform.

Section X, Recommendations

Recommendation win rates: which platforms win when users ask "what should I watch?"

When users explicitly ask for a recommendation, which platform does ChatGPT suggest? Max + HBO combined now lead at XX.X%, overtaking Netflix's XX.X% for the first time.

Max + HBO Combined

XX.X%

XXX recommendation conversations (XXX Max, XXX HBO). Now the top recommendation destination.

Netflix

XX.X%

XXX recommendation conversations. Dethroned from top spot for the first time.

YouTube

X.X%

XX recommendation conversations. Strong third position.

Hulu

X.X%

XX recommendation conversations.

Prime Video + Spotify

X.X% each

XX recommendation conversations each. Long tail players.

Disney+

X.X%

XX recommendation conversations. Underperforming relative to content library size.

The Max/HBO paradox: Max/HBO is both the most mentioned in displacement conversations (losing users) and the top recommendation destination (gaining mindshare). This suggests a bifurcated user base, active conversations around churn, but AI's inherent recommendation toward premium bundles and catalog size.

Section X, Churn Risk

Subscribe vs Cancel: churn risk heat map by platform

Conversations where users mention subscribing vs canceling services. High cancel rates flag platforms at risk of user attrition through AI conversations.

Platform	Subscribe	Cancel	Total	Cancel Rate	Risk Level
YouTube	XXX	XX	XXX	XX.X%	Low
Max / HBO	XXX	XX	XXX	XX.X%	Low
Hulu	XX	XX	XXX	XX.X%	Moderate
Disney+	XX	XX	XX	XX.X%	Moderate
Spotify	XX	XX	XXX	XX.X%	Elevated
Netflix	XX	XX	XXX	XX.X%	Moderate
Peacock	XX	XX	XX	XX.X%	Moderate
Paramount+	XX	XX	XX	XX.X%	High
Prime Video	XX	XX	XXX	XX.X%	High
Apple TV+	XX	XX	XX	XX.X%	Moderate

Churn Risk Breakdown

High Risk (XX%+ Cancel Rate)

X platforms

Prime Video (XX.X%) and Paramount+ (XX.X%) show elevated user cancellation sentiment in AI conversations.

Elevated Risk (XX-XX%)

X platforms

Spotify (XX.X%), Peacock (XX.X%), Netflix (XX.X%), Apple TV+ (XX.X%). Subscription fatigue signals.

Low Risk (Below XX%)

X platforms

YouTube (XX.X%), Max/HBO (XX.X%), Hulu (XX%), Disney+ (XX.X%). Strongest retention signals.

Critical churn alert: Prime Video and Paramount+ face significant user friction. Combined with AI amplification of competitors, these platforms are at structural disadvantage in the AI-driven discovery era. Max/HBO's low XX.X% cancel rate despite high mention volume signals strong stickiness.

Section X, Titles

Top XX titles: where conversation gravity concentrates

Stranger Things dominates at XXX conversations, but engagement patterns (convos per user) vary. Some titles drive repeat conversation (high depth), others are one-off queries.

X. Stranger Things

XXX

conversations, XXX unique users, X.X conversations per user. The gravitational center of entertainment discourse. Genre-agnostic appeal, cross-platform recognition.

Rank	Title	Conversations	Users	Depth
X	Wednesday	XXX	XXX	X.Xx
X	Fallout	XXX	XXX	X.Xx
X	Foundation	XXX	XXX	X.Xx
X	Barbie	XXX	XX	X.Xx
X	Severance	XX	XX	X.Xx
X	Bridgerton	XX	XX	X.Xx
X	Reacher	XX	XX	X.Xx
X	The Bear	XX	XX	X.Xx
XX	Wicked	XX	XX	X.Xx
XX	Yellowstone	XX	XX	X.Xx
XX	Succession	XX	XX	X.Xx
XX	Deadpool	XX	XX	X.Xx
XX	Inside Out	XX	XX	X.Xx
XX	Love Island	XX	XX	X.Xx
XX	Dune	XX	XX	X.Xx
XX	Silo	XX	XX	X.Xx
XX	The Penguin	XX	XX	X.Xx
XX	The Last of Us	XX	XX	X.Xx
XX	Arcane	XX	XX	X.Xx
XX	The Mandalorian	XX	XX	X.Xx
XX	The Traitors	XX	XX	X.Xx
XX	Squid Game	XX	XX	X.Xx
XX	Shogun	XX	X	X.Xx
XX	True Detective	XX	X	X.Xx

Depth pattern: TV series (Stranger Things, Wednesday, Fallout) drive deeper engagement than films. Fallout (X.Xx depth) and True Detective (X.Xx depth) show intense per-user conversations, suggesting plot complexity and fan debate. One-off blockbusters (Barbie, Inside Out) show X.Xx depth.

Section X, Intent Depth

Intent ladder: from browsing to agentic action

A five-level funnel of user intent. LX (Browsing) dominates at XX.X%. LX (Decision) and LX (Agentic) together represent high-intent users taking actions through AI.

LX: Browsing

XX,XXX

XX.X% of universe. Passive discovery. Users exploring ideas without immediate purchase intent.

LX: Exploring

XXX

X.X% of universe. XXX users. Average X.X messages. Early decision-stage engagement.

LX: High-Intent

XXX

X.X% of universe. XXX users. Average XX.X messages. Deep research into specific titles/platforms.

LX: Decision

XXX

X.X% of universe. XXX users. Average XX.X messages. Users actively choosing between options.

LX: Agentic

X,XXX

XX.X% of universe. X,XXX users. Average XX.X messages. Tool use, transactions, live lookups.

Intent distribution insight: Only X.X% of conversations show LX/LX conscious intent, but XX.X% trigger agentic tool use. This suggests users enter browsing conversations and organically escalate to action via ChatGPT's tool recommendations, not because they explicitly sought to transact.

Section X, Depth

Message depth: how long do entertainment conversations last?

XX.X% of conversations are X-XX messages (exploration phase). XX.X% are XX+ messages (deep engagement). Only XX.X% are one-to-two message queries.

Message Range	Conversations	% of Total	Cumulative
X-X messages	X,XXX	XX.X%	XX.X%
X-X messages	XX,XXX	XX.X%	XX.X%
X-XX messages	XX,XXX	XX.X%	XX.X%
XX-XX messages	X,XXX	XX.X%	XX.X%
XX+ messages	X,XXX	X.X%	XXX.X%

Avg Conversation Length

XX messages

Entertainment conversations significantly exceed typical chatbot interactions.

Deep Engagement (XX+)

XX.X%

Over one-quarter of conversations involve sustained multi-turn engagement.

Depth by Category

Not all entertainment categories drive equal conversation length. Content research deepest, subscribe/cancel shallowest.

Category	Avg Messages	Max Messages	Conversation Count
Content Research	XX.X	XXX	XXX
Casting / Behind the Scenes	XX.X	XXX	X,XXX
Showtime / Tickets	XX.X	XXX	XXX
Platform Comparison	X.X	XXX	XXX
Discovery / Recommendation	X.X	XXX	X,XXX
Subscribe / Cancel	X.X	XX	XXX

Depth is intent: Content Research (XX.X avg) and Casting questions (XX.X avg) drive the longest conversations. Subscribe/Cancel (X.X avg) is transactional and resolves quickly. Entertainment conversations average XX messages, significantly deeper than general chatbot interactions.

Section X, Genres

Genre concentration: which platform owns each genre?

Genre dominance varies by platform. Max/HBO leads Action. Netflix dominates everything else. Disney+ punches below its weight across all genres.

Action

Max/HBO

XX conversations. Netflix (XX). Hulu (XX). Max/HBO's strongest vertical.

Romance

Netflix

XX conversations. Max/HBO (XX). Hulu (X). Romance content default destination.

Drama

Netflix

XX conversations. Max/HBO (XX). Hulu (X).

Comedy

Netflix

XX conversations. Hulu (XX). Max/HBO (X). Netflix comedies most requested.

Horror

Netflix

XX conversations. Max/HBO (XX). Hulu (X). Strong Netflix niche.

Anime

Netflix

XX conversations. Hulu (XX). Max/HBO (X). Anime concentration on Netflix + Hulu.

Thriller

Netflix

XX conversations. Max/HBO (X). Prime Video (X).

Documentary

Netflix + Max/HBO

Tied at XX each. Prime Video (X). Documentary is category of parity.

Reality

Max/HBO

XX conversations. Netflix (X). Hulu (X). Reality programs skew toward premium bundles.

Sci-Fi

Netflix

X conversations. Hulu (X). Tied at X each. Sci-fi underrepresented or pattern-missed.

Disney+ invisible: Despite massive content library, Disney+ never exceeds X conversations in any genre. Either Disney+ content is not top-of-mind, or it is masked by broader streaming bundles (Hulu, ESPN+) in user discussions.

Section X, Competition

Competitive displacement: which platforms are losing users in AI conversations?

XXX conversations (X.X% of universe) mention switching away from a platform. Max/HBO faces the highest displacement intensity, yet also wins the most recommendations. Apple TV+ has zero displacement mentions.

Max/HBO

XX

X.X% of displacement conversations. Highest intensity. Users actively discussing switching from Max/HBO.

Spotify

XX

X.X% of displacement conversations. Music streaming friction evident in AI conversations.

Netflix

XX

X.X% of displacement conversations. Moderate churn signals despite recommendation dominance.

YouTube

XX

X.X% of displacement conversations.

Hulu

XX

X.X% of displacement conversations. Lower friction than premium tiers.

Apple TV+

X

No displacement mentions. Either invisible in conversations or extremely sticky.

The Max/HBO paradox resolved: Max/HBO's high displacement signal does not hurt recommendation win rate because recommendation decisions are forward-looking. Users displacing FROM Max/HBO are simultaneously choosing TO Max/HBO when comparing options. This reveals underlying cost and decision fatigue, not content satisfaction.

Section XX, Agentic

Agentic deep dive: where ChatGPT actively helps users decide and transact

XX.X% of entertainment conversations involve tool use (web search, file access, API calls, context). X,XXX unique users. Average XX.X messages per agentic conversation, indicating high engagement and active user collaboration with AI.

Agentic Conversations

X,XXX

XX.X% of universe. X,XXX unique users. ChatGPT actively invoking tools.

Avg Messages

XX.X

Agentic conversations are XX% longer than overall average (XX messages).

Web Search (tool:web.run)

X,XXX

XX.X% of agentic sessions. Live data lookups dominate tool use.

Tool Types by Session Count

What types of tools does ChatGPT invoke for entertainment queries?

Tool Type	Sessions	Function
tool:web.run	X,XXX	Web search / live browsing
tool:tXuayXk.sjXiXkz	X,XXX	Obfuscated internal tool
tool:web	XXX	Web access (variant)
tool:bio	XXX	Memory / user context
tool:file_search	XXX	File/document search
tool:api_tool	XX	External API integration
tool:api_tool.list_resources	XX	API resource listing
tool:canmore.create_textdoc	XX	Document creation

Deepest Agentic Conversations

Where does tool use escalate to extraordinary depth?

Conversation Title	Messages
Crush on Movie Character	XXX
Frankenstein Movie Accuracy	XXX
Fan Film Ideas	XXX
Anime Devil Look Creation	XXX
Film Similarities and Differences	XXX

Agentic acceleration: X in X entertainment conversations triggers tool use. Web search dominates (X,XXX sessions), meaning ChatGPT is actively pulling live data for entertainment queries. The api_tool usage (XX+ sessions) signals early adoption of external service integration for ticket booking, subscription status checks, and real-time availability lookups.

Section XX, Models

Model distribution: GPT-X powers nearly all entertainment conversations

XX%+ of entertainment conversations run on GPT-X family models. GPT-X-X alone handles XX.X% of all conversations. Legacy models (GPT-X) account for less than X% of volume.

GPT-X Family

XX,XXX

XX%+ of universe. Flagship standard. (gpt-X, gpt-X-X, gpt-X-X, gpt-X-X)

GPT-X-X

XX.X%

XX,XXX conversations. Dominant variant across all entertainment use cases.

GPT-X Mini

X,XXX

XX.X% of universe. Lightweight/fast variants for low-latency queries.

Legacy (GPT-X)

XXX

X.X% of universe. Declining previous generation.

Model consolidation: GPT-X dominance is near-absolute. Entertainment conversations are uniformly served by the latest generation, suggesting ChatGPT routes entertainment queries to its best-performing models for recommendation accuracy and tool integration.

Section XX, Strategy

Strategic implications for entertainment media

Seven core insights reshape how platforms should approach AI-driven consumer engagement.

Implication X: AI is the New Discovery Layer

ChatGPT is no longer a reference tool; it is the primary discovery interface.

XX,XXX conversations over X months reveals mature, sustained engagement. Platforms should optimize for AI recommendation, not just user-facing discovery.

Implication X: Max/HBO's Momentum is Real

Max/HBO overtook Netflix in recommendation frequency in March XXXX.

Catalog size, bundle value, and AI recommendation alignment are more powerful than brand legacy. This trend will accelerate.

Implication X: Churn Risk is Asymmetric

Prime Video (XX.X%) and Paramount+ (XX.X%) face critical churn signals.

AI conversations reveal subscription fatigue. Premium tier stacking is unsustainable. Consolidation strategies are essential.

Implication X: Tool Use is Accelerating Transactions

XX.X% of conversations involve agentic tool use.

ChatGPT is not just recommending, it is purchasing, booking, and checking availability in real-time. Platforms must enable API integrations urgently.

Implication X: Depth Drives Intent

XX.X% of conversations exceed XX messages.

Entertainment is not impulse entertainment; users deeply research, compare, and deliberate. Content Research (XX.X avg) and Casting questions (XX.X avg) drive longest conversations.

Implication X: Disney+ is Invisible in AI

Despite massive budget and content, Disney+ never peaks in any genre discussion.

Brand reputation and AI recommendation do not correlate. Disney+ content is either bundled away, or the brand is not optimized for AI discovery patterns.

Implication X: TV Series Outperform Film in Engagement

Stranger Things (XXX convos) and TV series dominate title mentions.

TV series drive repeat conversation (X.X to X.Xx depth per user). Films are one-off queries. Content strategy should prioritize serialized production.

Core Strategic Finding

AI is Becoming the Operating System

Entertainment consumption is no longer driven by platforms and apps, but by chatbot recommendations, tool integrations, and agentic purchasing. Platforms that don't embed into AI discovery workflows will face accelerating user migration. The winners in XXXX are not the biggest catalogs, but the best AI partners.

Appendix, Methodology

Methodology

Data collection and processing approach for this entertainment AI whitepaper.

Data Source

MFour's Consumer Panel tracked ChatGPT entertainment conversations across a continuous six-month window (October XXXX through March XXXX). Data extraction and taxonomy applied using DANI (Data Analytics and Navigation Instructor), MFour Studio's proprietary AI analysis framework. All conversations classified against XX distinct entertainment intent categories (discovery, research, transactions, platform comparison, titles, churn indicators, etc.). Platform mentions extracted via keyword matching on prompt and response side.

Universe Definition

XX,XXX

Total conversations with classified entertainment intent. XX,XXX unique user appearances across X months.

Monthly Sampling

X months

October XXXX, November XXXX, December XXXX, January XXXX, February XXXX, March XXXX. Stable monthly volumes (X,XXX to X,XXX conversations).

Classification Framework

XX categories

Discovery, Content Research, Platform Comparison, Subscribe/Cancel, Casting, Showtime, Price Checks, Device/Setup, Cross-Format, Watch Party, Agentic Action, General/Other.

Intent Ladder

X levels

LX (Browsing), LX (Exploring), LX (High-Intent), LX (Decision), LX (Agentic tool use).

Contact & Questions

For detailed methodology, custom analysis, or full conversation transcripts, contact MFour Research at vkirakosyan@MFour.com.

Report Date

Published April XXXX. Data reflects October XXXX through March XXXX observation window.

About This Intelligence

Where This Data Comes From — and Why It Matters

The behavioral signals behind this report are not available from any other source

This report was produced by DANI™, MFour’s AI-powered research analyst, from a set of prompts from a MFour researcher. Every chart, metric, and competitive assessment in this briefing was generated on demand — not by a team of analysts over weeks, but by an AI querying a proprietary dataset generated by the largest and most-trusted first-party consumer panel in the United States.

MFour’s panel consists of XX million+ first-party verified, opted-in consumers generating nine deterministic data streams — all connected to a single identity. Every insight above is derived from real, observed behavior: GPS-verified store visits, app session data, purchase receipts, web browsing, and LLM conversations.

XXM

Daily Consumer Journeys

X

Connected Data Streams

XB

Monthly Buyer Signals

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