Let’s call this what it is: AI has become the snake oil of the streaming industry.

Sold as the answer to every problem—churn, stagnation, bad UX, too many rom-coms and not enough retention—but under the hood, most companies are still running on duct tape, prayer, and dashboards built in 2017.

Every pitch deck now has a slide that screams “AI-powered personalization!” or “predictive churn prevention!”—meanwhile, half the platforms Kirby Grines surveyed in TheStreamingWars' study still can’t track a user across devices. You start watching The Bear on your tablet, finish it on your Roku, and your platform assumes two different people are watching—one into gritty chef dramas, and one into lifestyle comfort food. You can practically hear the AI short-circuiting.

But go ahead and spend $500K building a ChatGPT plug-in that can "summarize user behavior trends" before you've even figured out where your viewers are dropping off.

🤖 Here’s the uncomfortable truth: if your data stack is garbage, AI is just going to turn it into more organized garbage.

You know what “personalization” actually means on most platforms? You watch one season of Love Is Blind out of morbid curiosity, and now your homepage looks like Tinder and Bravo had a baby during a tequila bender. It’s not personalization. It’s pattern recognition for the terminally lazy.

And the problem isn’t the algorithm. The problem is the leadership that thinks AI is a magic wand, instead of what it really is: a very smart mirror. A mirror that’s going to show you exactly how busted your infrastructure is.

Grines’ report lays this bare. Most streaming companies are juggling three to six different analytics platforms, and not one of them is connected to anything resembling a central source of truth. Google Analytics, Mux, Adobe, JUMP, Tableau, NPAW, Conviva… it’s like the tech version of a potluck where everyone brought soup and no one brought spoons.

🎯 And what’s the result? Internal teams are basing million-dollar content decisions off conflicting dashboards. Data engineers are in therapy. UX designers are guessing. Executives are pointing at pie charts like it’s a magic 8-ball.

And the kicker? The people who need the insights the most—your monetization and UX teams—are the ones least likely to get access. The CPO and CEO are swimming in dashboards, while the person designing your onboarding flow is working off gut instinct and a vague memory of last quarter’s survey.

Then there’s the Wall Street Effect™. As in: internal analytics might be screaming “users are bouncing after the third click,” but if a sell-side analyst says your “brand awareness” is up 0.3%, that’s what gets the attention. Kirby Grines captured this disconnect perfectly: executives ignoring their own internal data because they’re too busy feeding investor narratives. It’s like rearranging the deck chairs on the Titanic because someone in a suit liked the symmetry.

And don’t even start with the predictive modeling fantasy. Everyone’s talking about AI like it's HAL from 2001, when most companies are closer to Clippy from Microsoft Word. 🧠💀

“Predictive churn modeling” sounds sexy until you realize your metadata tags are so inconsistent, your top-performing content is literally tagged as “drama” and “cooking show” at the same time. (This is not hypothetical.)

So, what do you do? You slap “AI” in your boardroom slide deck, say you’re building a “GPT-powered engagement system,” and hope no one asks for actual ROI. It's AI-washing, and it's happening everywhere.

✅ Can you track a user across devices?
✅ Do your internal dashboards all say the same thing?
✅ Do your teams actually use the data you collect?
✅ Do you know what your best-performing content is—and why?
✅ Are your recommendation engines actually driving engagement, or just... echoing?

If you answered “no” to more than two of those, AI is not your savior. It’s your smokescreen. And when the fog clears, your churn rate will still be rising and your viewers still won’t know what the hell to watch next.

TL;DR:
📉 AI won’t fix a broken product strategy
🧹 You can’t automate what you don’t understand
🛠️ And you sure as hell can’t personalize if your data is a dumpster fire

If you want AI to work for your streaming platform, start by doing the boring, unsexy work:

Because if your platform still thinks Die Hard is a holiday rom-com just because it’s December… that’s not AI being dumb.

That’s you. ❄️🎄💥

Stay Bold, Stay Curious, and Know More than You Did Yesterday.

Let’s stop playing nice: the streaming industry’s analytics stack is a dumpster fire held together with buzzwords, dashboards, and delusion. Everyone’s talking about AI as if it’s the second coming of Nielsen, but under the hood? It’s chaos.

Kirby Grines, in TheStreamingWars’ 2025 industry survey, lays it out plainly: services are collecting data like it’s candy on Halloween—but it’s all empty calories. Basic clicks? Sure. App behavior? Of course. But the moment you ask about preferences, content journeys, or actual churn signals? You’re met with awkward silence and a half-finished Tableau dashboard last updated during the Biden midterms.

Let’s break it down.

Most platforms are proudly logging session duration, navigation paths, and which thumbnails get clicked—but they still don’t know why users cancel, which content actually drives conversions, or how to map a user's journey across devices. It’s like a restaurant tracking fork usage and forgetting to ask if the food tasted like cardboard.

They’re flying blind. And that’s fine when you’re early-stage. But not when you’re managing millions of subs, ad dollars, and content bets bigger than your Q4 bonus.

Here’s where it gets tragic. According to the study, Google Analytics remains the most-used platform for Connected TV analytics. GA was built for bounce rates on WordPress blogs, not living room viewing behavior. It’s the horse-drawn carriage on the CTV autobahn, and the fact that we’re still relying on it in 2025 says everything about the industry’s fear of growing up.

Seriously—how are you building an ad strategy for Roku and Fire TV using tech that still calls visits “sessions”?

You’d think by now we’d have figured out that humans don’t just watch on one screen. But nope—half of services still can’t follow a viewer from mobile to tablet to smart TV. Which means they’re making assumptions on isolated sessions, not real behavior.

Imagine a viewer starts a documentary on their phone, picks it up on Apple TV, and then finishes on a laptop. Your platform thinks three people watched one-third of a film and marks it a failure. Then it kills your licensing renewal. Oops.

Grines writes that multiple platforms generating conflicting numbers has created a data civil war inside companies. Marketing says 10 million views. Product says 2 million. Finance wants to cry.

The result? Nobody trusts anything. Budgets are based on vibes. “Data-driven” decisions become a game of “choose the number that makes your slide look best.” It’s like horoscopes, but with pivot tables.

And this isn’t just internal chaos—it affects studios, agencies, advertisers, and investors who are all being sold different versions of the same ‘success story.’

That’s not hyperbole. That’s where we are.

And if you think I’m exaggerating, just look at how many platforms still measure “viewership” based on two minutes watched. Or how execs are making greenlight decisions using a blend of exit surveys, finger-crossing, and a Frankenstein’d data stack stitched together by three PMs and a WeWork intern.

The gap between what streaming platforms say they’re doing with data—and what they’re actually doing—is wide enough to drive a Netflix-branded NASCAR through.

For consumers: This is why your recommendations feel like they were made by a caffeinated hamster.

For studios: You’re overpaying for content based on skewed metrics. You think a show “worked” because of 10M starts—but no one finished it.

For advertisers: Your “targeting” is basically educated guessing sprinkled with GDPR disclaimers.

For tech folks: There’s still no real “Snowflake for Streaming.” Whoever builds it—one platform to unify, clean, and normalize all this data? That’s your gold mine.

Everyone’s throwing around phrases like “predictive churn modeling” and “behavioral segmentation via LLM,” but you can’t predict churn when your data doesn’t even recognize the same user across apps.

As Grines points out, “streaming analytics are still focused on current trends, with predictive adoption limited”—translation: we’re still reacting, not anticipating. You can’t be proactive when your best tool is a lagging indicator wrapped in a PowerPoint.

Will the industry finally admit its analytics are broken—and invest in actual measurement infrastructure?

Or are we going to keep pretending that dashboards, vendor reports, and LinkedIn buzzwords are enough to run a $200 billion industry?

Either way, next time you hear someone pitch “AI-powered streaming personalization,” ask them one thing:

“Do you even track cross-device behavior?”

🎤 (Mic drop.)

Here’s a fun riddle: If a viewer watches Die Hard on Christmas via The Roku Channel on their phone, then reboots it on a Roku stick, while muted, and a brand counts it as a conversion—did anything actually happen?

Welcome to Roku’s version of Schrödinger’s Metrics.

For all the platform’s chest-thumping about scale, ad-tech prowess, and AI-powered whatever, it’s still stuck duct-taping analytics together like a college intern building a data dashboard in PowerPoint. Call it what it is: a Frankenstack. Mux says one thing, Conviva another, Roku’s OS a third—and GA4 just quietly sobs in the corner.

Then there’s the cross-device black hole. Viewers hop from mobile to TV, but Roku treats them like they’re in witness protection. Personalized recommendations vanish, unique viewer counts balloon like a crypto market, and advertisers? They’re essentially buying ghost impressions.

Meanwhile, Roku is shouting “AI!” like a televangelist selling miracle soap. In reality? It’s tagging Die Hard as holiday content and pushing five-dollar true-crime knockoffs after one guilty binge.

The predictive modeling? Half-baked. The attribution? Let’s just say if muting a commercial counts as a “view,” I’ve been deeply moved by thousands of Cialis ads I never heard.

Bottom line: Roku isn’t just part of the industry’s broken analytics mess—it’s the poster child. Growth at all costs, data hygiene be damned, and enough fuzzy metrics to make a Nielsen exec drink before noon.

Behind every bloated content budget and chaotic earnings call, there’s a single, sacred lie the industry refuses to confront:

Spoiler: No, you don’t.

Here’s the ugly truth—and why it’s costing studios, advertisers, and shareholders more than they realize:

CEOs love boasting about their "robust analytics" and "AI-powered insights." But Grines’ survey exposes the reality:

🎯 Why it burns money: Misreading churn leads to band-aid fixes (free months! more sequels!) instead of solving root issues (content quality, pricing tiers).

Streaming’s obsession with vanity metrics creates fictional success:

💸 The cost: Studios overpay for IP (see: $200M for a Citizen Kane prequel series) based on self-reported rival metrics.

Result: A $300B industry running on Excel sheets, gut feelings, and hoping the next algorithm update will magically fix everything.

The coming year will force a reckoning:

The only way out:
✅ Standardize core metrics (e.g., "churn reason" tracking).
✅ Kill legacy tools (GA4 for CTV is like using a sundial to time a rocket launch).
✅ Audit third-party data (or keep getting scammed by "view farms").

📌 Bottom Line:
Streaming’s data lie is the biggest open secret in tech. The first CEO to admit it—and fix it—will dominate the next era.

Let’s be honest: you’ve spent more this week on a half-melted energy bar at the airport.

Meanwhile, inside today’s ADOTAT+:

Add in churn prediction fixes, device-level targeting tricks, ad-tolerance modeling, and a hit list of what not to feed your LLM—and suddenly that $6/month feels like the best decision you’ve made since switching off autoplay previews.

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Your dashboards are hallucinating. Maybe it’s time you stopped relying on vibes.

No strings. No creepy tracking. Just uncomfortable truths, unfiltered analysis, and enough witty insight to make you the smartest person in the marketing meeting.