Most OTT teams can tell you exactly how many hours of content were watched last month, which titles had the highest completion rates, and how many new app installs they drove. Ask them which marketing channel actually delivers subscribers who stick around past the first billing cycle, or what their unit economics look like by acquisition source, and you'll get a long pause followed by "we're still building that dashboard."

The metrics that separate growing streaming platforms from ones burning cash on content and ads aren't the engagement numbers in your content analytics tool,they're the unit economics, attribution data, and retention cohorts that show whether your business model actually works. This guide covers the core acquisition, content, engagement, and monetization metrics that OTT and streaming apps track to make faster decisions, prove campaign ROI, and scale profitably.

Why Most Streaming Metrics Miss the Business Impact

Content teams track watch time, completion rates, and title popularity. Marketing teams track app installs and campaign impressions. Finance teams track MRR and subscriber counts. None of these dashboards talk to each other, so when your CEO asks "Which marketing channel brings subscribers who actually stay?", nobody has a clean answer.

The difference comes down to vanity metrics versus actionable metrics. Vanity metrics look impressive in a board deck but don't connect to profitability. Actionable metrics tell you exactly where to spend more and where to cut.

Here's what separates them:

• Vanity metrics: Total app installs, content hours uploaded, social media followers,numbers that feel good but don't inform budget decisions

• Actionable metrics: CAC by channel, subscriber LTV by acquisition source, churn rate by cohort, ROAS from specific campaigns,numbers that tell you what to do next

If you're running a multi-platform streaming app, your users move between mobile, web, smart TVs, Meta ads, Google campaigns, and influencer partnerships. When your data lives in six different dashboards,app analytics, content CDN reports, ad platform metrics, payment processor exports,you're guessing at attribution instead of measuring it.

Subscription vs Ad-Supported: Different Metrics for Different Models

A metric is any measurable data point. Total installs is a metric. Hours watched is a metric. Free trial signups is a metric.

A KPI,key performance indicator,is a metric tied to a specific business goal. "Cost per subscriber from Meta ads who remain active after 90 days" is a KPI. It tells you whether that channel is worth scaling.

The best streaming KPIs answer questions like "Which channel brings subscribers who watch regularly and don't churn?" and "How much can I afford to spend to acquire one paying subscriber?" They ladder up to profitability, not just volume.

Your monetization model determines which metrics matter most:

• SVOD (Subscription Video on Demand): Focus on CAC, subscriber LTV, churn rate, retention cohorts, and subscription conversion rate

• AVOD (Ad-Supported Video on Demand): Track DAU, watch time per user, ad load tolerance, CPM rates, and content discovery

• Hybrid/Freemium: Monitor free-to-paid conversion, tier upgrade rates, ad revenue per free user, and premium feature adoption

Tracking KPIs across Meta, Google, TikTok, and other channels requires unified attribution. Without it, you're reconciling screenshots and spreadsheets instead of making decisions.

Customer Acquisition Cost (CAC) for Streaming Apps

CAC is total marketing and sales spend divided by new subscribers acquired. If you spent ₹5 lakhs last month and acquired 2,000 paying subscribers, your CAC is ₹250.

This is the foundation of streaming economics. If you don't know what a subscriber costs, you can't know if you're profitable,especially when content licensing and infrastructure costs are fixed.

How to Calculate CAC Across All Marketing Channels

Take your total ad spend, add creative production costs, agency fees, and platform fees, then divide by the number of new paying subscribers (not just app installs or free trial signups). The formula is straightforward. The challenge is attribution.

If a user sees a YouTube ad, then clicks a Meta retargeting ad, then installs your app via Google Play, who gets credit? If you're relying on each platform's self-reported numbers, you're double-counting conversions and inflating performance. Mobile measurement partners (MMPs) unify this data so CAC is accurate, not guessed.

They stitch together click, install, free trial signup, and subscription events across every channel. You get one source of truth for what a paying subscriber actually costs, not what each ad network claims they delivered.

The CAC Trap: Installs vs Subscribers

Most streaming apps make a critical mistake: they track CAC as "cost per install" instead of "cost per paying subscriber." This hides the real economics.

You might drive 10,000 installs at ₹20 each (₹2 lakh total spend), but if only 1,000 convert to paying subscribers, your true CAC is ₹200,10x higher than the install number suggests. The remaining 9,000 users might browse your catalog, watch free content, and churn without ever paying,burning server and CDN costs with zero revenue contribution.

Track both install-level and subscriber-level CAC. The gap between them reveals your conversion funnel efficiency and tells you whether your onboarding, free trial experience, and content catalog are strong enough to justify paid acquisition.

Benchmarks for Profitable Subscriber Acquisition

CAC varies by content type and target market. Premium sports streaming platforms typically spend more per subscriber than regional entertainment platforms. International content catalogs fall somewhere in between.

The key isn't hitting a specific number. It's comparing CAC to subscriber lifetime value (LTV). Streaming apps often have higher CAC than other mobile apps because conversion funnels are longer,users browse content before committing to a subscription,but strong retention can justify the investment.

Subscriber Lifetime Value (LTV)

LTV is the total revenue a subscriber generates over their relationship with your platform. It's the counterweight to CAC,you can afford high acquisition costs if LTV is higher.

Calculating LTV for Subscription Streaming

The formula is: monthly subscription price × average subscriber lifespan (in months). If your subscription costs ₹499/month and the average subscriber stays for 18 months, their LTV is ₹8,982.

For ad-supported models, LTV = (average monthly ad revenue per user × average user lifespan in months). If each user generates ₹150/month in ad revenue and stays for 24 months, LTV is ₹3,600.

Hybrid models combine both: subscription revenue + ad revenue (if applicable to tier) × subscriber lifespan.

Mobile MMPs can track this more precisely than web-only analytics because you see in-app signups, viewing behavior, subscription renewals, and cancellations in one place. Accurate LTV requires tracking installs, events, and revenue in a single platform,not stitching together App Store reports, payment processor data, and CDN logs.

Why Content Catalog Depth Impacts LTV

Subscribers churn when they've "watched everything" or when content updates slow down. Platforms with deep catalogs and consistent content releases see longer subscriber lifespans because users always have something new to watch.

Track content engagement rates by subscriber cohort. If users acquired in January watch an average of 8 hours per week but users acquired in June watch only 3 hours per week, your content strategy or catalog depth may have weakened,directly impacting LTV.

5 Ways to Increase Subscriber Lifetime Value

Here's what moves the needle:

• Personalized content recommendations based on viewing history to keep users discovering titles they'll finish

• Release schedules and content drops that create anticipation and keep subscribers engaged between new seasons

• Multi-profile support that increases household utility and makes cancellation harder (entire family is using it)

• Exclusive or original content that can't be found on competing platforms

• Watch parties or social features that add network effects and community engagement

Executing on any of this depends on event tracking and deep linking. If you can't see which users are about to churn or send them directly to a new release notification screen, you're running retention campaigns blind.

The CAC to LTV Ratio That Makes or Breaks Unit Economics

The CAC:LTV ratio is the single most important streaming performance metric. It tells you whether you're acquiring subscribers profitably.

A healthy ratio means LTV is multiple times higher than CAC. That gives you room to scale spend without burning cash. If your CAC is ₹250 and your LTV is ₹500, you have a 1:2 ratio. That barely covers content licensing, CDN costs, and platform fees,not enough margin for sustainable growth.

If LTV is ₹3,000, you have a 1:12 ratio. Now you can afford to experiment with new channels, invest in creative, and outbid competitors for user attention.

Streaming platforms need real-time visibility into this ratio across channels, not monthly spreadsheets that arrive two weeks after the campaign ends. When you can see CAC:LTV by Meta campaign, Google UAC cohort, and influencer partnership, you know exactly where to scale and where to cut.

Churn Rate: The Silent Profit Killer

Churn rate is the percentage of subscribers who cancel within a given period. For subscription streaming, churn is often more important than acquisition,acquiring 1,000 new subscribers doesn't matter if 1,200 existing subscribers cancel.

Measuring Churn by Subscriber Segment

Monthly churn rate is: (subscribers who canceled this month ÷ subscribers at start of month) × 100. If you started the month with 50,000 subscribers and 5,000 canceled, your churn rate is 10%.

Churn varies dramatically by segment. Track it by:

• Acquisition channel: Users from brand campaigns may retain better than users from performance ads

• Content affinity: Sports fans might churn after tournament season ends; drama watchers may stick around longer

• Subscription tier: Annual subscribers typically churn less than monthly subscribers because of commitment friction

• Geographic market: Regional markets with limited content catalogs often see higher churn

Unified analytics reveal which segments are at risk. If Meta-acquired subscribers churn at 15% while Google subscribers churn at 8%, you know where to adjust targeting or creative.

Why Streaming Apps Struggle with Involuntary Churn

Involuntary churn happens when payments fail,expired cards, insufficient funds, payment gateway issues,not because users actively cancel. For many streaming platforms, involuntary churn represents 20-40% of total churn.

Track payment success rates by payment method and geography. If UPI payments succeed 98% of the time but international card payments only succeed 85%, you have a gateway or fraud detection issue eroding revenue.

Reducing involuntary churn through dunning management (automated retry logic), payment method updates, and better payment routing can improve net revenue retention by 5-10% without acquiring a single new subscriber.

The Reactivation Opportunity

Churned subscribers already know your product and content. Reactivating them costs far less than acquiring new users,often ₹50-100 to win back a subscriber versus ₹200-400 to acquire a fresh one.

Track reactivation rate (percentage of churned subscribers who return within 90 days) and segment win-back campaigns by churn reason: users who left due to price sensitivity respond to discount offers, users who left due to content gaps respond to new release announcements, users who left due to technical issues respond to "we fixed it" messaging.

Deep links and deferred deep links can re-engage churned subscribers by sending them directly to new content or a discounted resubscribe flow with a personalized offer.

Content Engagement Metrics That Drive Retention

Content metrics bridge the gap between what users watch and whether they stay subscribed. High engagement predicts low churn; low engagement predicts cancellation.

Watch Time and Viewing Frequency

Watch time measures hours watched per user per week or month. Viewing frequency tracks how many days per week users open your app and watch content.

Both matter, but viewing frequency is often the better predictor of retention. A subscriber who watches 2 hours spread across 5 days per week is more engaged,and less likely to churn,than one who watches 8 hours in a single binge session and disappears for two weeks.

Track viewing patterns by cohort. If newly acquired subscribers average 3 viewing sessions per week in Month 1 but drop to 0.8 sessions by Month 3, your content recommendation engine or release schedule isn't maintaining engagement.

Content Completion Rate

Completion rate is the percentage of a title (movie, episode, series) that users watch. High completion rates signal strong content-market fit; low completion rates signal users aren't finding the content compelling or discoverable.

Track completion rate by content type:

• Movies: Aim for 70%+ completion; anything lower suggests poor content quality or misleading metadata

• Series: Track both episode completion (did they finish the episode?) and series completion (did they watch through to the finale?)

• Short-form content: 80%+ completion is standard for content under 15 minutes

Platforms with strong completion rates see better retention because users finish content and want more, rather than abandoning mid-title and forgetting about the platform.

Title Discovery and Catalog Utilization

Title discovery rate measures what percentage of your content catalog users actually engage with. If you have 5,000 titles but 80% of watch time concentrates on 200 titles, you have a discovery problem,or a content quality problem.

Low catalog utilization means you're paying for content licensing that doesn't drive engagement or retention. High-performing platforms typically see 40-60% of their catalog generating meaningful watch time over a 90-day period.

Improve discovery through personalized recommendations, better search, curated collections, and content marketing (social campaigns highlighting hidden gems).

Binge Rate for Series Content

Binge rate measures how many episodes of a series users watch in a single session. High binge rates (3+ episodes) signal compelling content that hooks viewers.

Track binge behavior by genre and release strategy:

• Full-season drops (Netflix model) often see higher initial binge rates

• Weekly releases (Disney+ model) create habit formation and spread engagement over time

Neither approach is universally better,choose based on content type and business goals. What matters is measuring how release strategy impacts engagement and retention for your specific audience.

Conversion Rate Optimization for Streaming Funnels

Conversion rate is the percentage of users who complete a desired action,install to free trial signup, free trial to paid subscription, monthly to annual upgrade. Improving conversion rate lowers CAC and improves unit economics because you're getting more subscribers from the same traffic.

The Streaming Conversion Funnel

Track conversion at every stage:

• Click-to-install rate: How many ad clicks become app installs

• Install-to-signup rate: How many installs create accounts and browse content

• Signup-to-free-trial rate: How many accounts start a free trial

• Free-trial-to-paid rate: How many trial users convert to paying subscribers

• Monthly-to-annual upgrade rate: How many monthly subscribers upgrade to annual plans

Drop-offs at any stage reveal friction points. If 10,000 users install your app but only 3,000 sign up and only 800 start free trials, your onboarding flow or value proposition is weak,you're losing 92% of users before they even try your content.

Mobile App vs Web Conversion Benchmarks

Mobile apps typically convert better than mobile web for streaming because of faster load times, offline viewing capabilities, and persistent login state. You don't have users bouncing because video playback stuttered or getting distracted by browser tabs.

Streaming apps also benefit from push notifications to re-engage trial users and drive conversion. Web users who don't convert during the session often never return.

Tracking app conversion requires SDKs and attribution platforms that capture in-app events,not just installs, but trial starts, payment completions, and subscription renewals.

Reducing Free Trial Drop-Off

Free trials are critical for streaming apps, but they're also massive conversion choke points. Industry averages show 20-40% of free trial users convert to paid,meaning 60-80% churn before the first billing cycle.

Common trial drop-off reasons include: content catalog doesn't match expectations, users forget about the trial and don't engage, payment friction at trial end, better content found on competing platform, or trial duration too short to evaluate value.

Reduce trial drop-off through:

• Onboarding flows that showcase your best content immediately

• Mid-trial engagement campaigns (email, push) that highlight new releases or personalized picks

• Payment capture at trial start with clear reminder before billing (reduces surprise churn)

• Trial duration optimization,7-day trials work for content-rich platforms, 30-day trials better for niche content that takes time to discover

Daily, Weekly, and Monthly Active Users (DAU/WAU/MAU)

DAU counts unique users who watch content each day. MAU counts unique users per month. The DAU/MAU ratio measures stickiness,how often monthly users return.

For streaming apps, a DAU/MAU ratio above 25-30% signals strong habit formation. Users are checking in frequently, not just subscribing and forgetting. Lower ratios mean your platform is occasional-use, which correlates with higher churn risk.

Ad-supported platforms care more about DAU than subscription platforms because ad revenue depends on daily impressions. Subscription platforms care more about monthly engagement and content completion,as long as users watch regularly enough to see value, exact daily usage matters less.

Session Length and Viewing Sessions Per Week

Session length measures time between app open and close. Session frequency counts how many times per week a user opens your app and watches content.

Streaming platforms want both: long sessions (users are watching multiple episodes or full movies) and frequent sessions (users return consistently). Track session patterns by content type:

• Binge-worthy series drive long sessions but less frequent returns (users watch 3 episodes then don't come back for a week)

• Short-form or variety content drives short, frequent sessions (users check in daily for new clips or episodes)

Neither pattern is wrong,what matters is matching session behavior to content strategy and subscriber expectations.

Revenue Metrics That Prove Sustainable Growth

Revenue metrics show whether your streaming platform is building a scalable, repeatable business model. Investors ask for MRR, ARPU, and revenue growth by cohort in every pitch deck.

Monthly Recurring Revenue (MRR)

MRR is predictable revenue from subscriptions. It's easier to forecast and value than one-time revenue sources because it compounds month over month.

MRR formula: number of paying subscribers × average monthly subscription price. If you have 50,000 subscribers paying an average of ₹399/month, your MRR is ₹1,99,50,000 (₹2 crores).

Track MRR by cohort to see if newer subscribers are more or less valuable than earlier cohorts. If January subscribers generate ₹450/month in average revenue while June subscribers generate only ₹320/month, either your pricing changed or you're acquiring lower-value users.

Average Revenue Per User (ARPU)

ARPU is total revenue divided by total active users,including both paying subscribers and free/ad-supported users. It shows blended monetization across your entire user base.

ARPU formula: (subscription revenue + ad revenue) ÷ total active users. For hybrid models, ARPU reveals the combined value of your freemium and subscription tiers.

Subscription platforms typically see higher ARPU (₹300-600/user) than ad-supported platforms (₹50-150/user), but ad-supported platforms can compensate through volume,10x the user base at 1/4 the ARPU can deliver higher total revenue.

Net Revenue Retention (NRR)

NRR measures revenue retained from existing subscribers, including upgrades and add-ons, minus churn. An NRR above 100% means you're growing revenue without needing new subscribers,a sign of strong product-market fit and monetization efficiency.

If you start a month with ₹1 crore in revenue from existing subscribers, add ₹15 lakhs from annual plan upgrades and multi-user tier adoptions, and lose ₹10 lakhs to churn, your NRR is 105%.

High NRR indicates low churn and effective upsell campaigns. Low NRR signals retention problems that no amount of new acquisition can fix,you're trying to fill a leaky bucket.

Ad Revenue Metrics for AVOD Models

Ad-supported streaming platforms track different monetization metrics:

• CPM (cost per thousand impressions): What advertisers pay per 1,000 ad views,higher CPMs mean better monetization

• Ad load tolerance: How many ads users will tolerate before dropping off (tracked by completion rate and churn)

• Fill rate: Percentage of ad inventory that gets sold (low fill rate means unsold impressions and lost revenue)

• ECPM (effective CPM): Actual revenue per 1,000 impressions after accounting for fill rate and pricing

Ad revenue depends on DAU and watch time more than subscriber count. A platform with 1 million DAU watching 2 hours per day generates more ad impressions than a platform with 5 million MAU but low engagement.

Attribution Metrics for Multi-Platform Streaming Apps

Attribution connects marketing spend to subscribers across mobile, web, smart TVs, and gaming consoles. Users might see an ad on Instagram, research on desktop, install your mobile app, and watch most content on a smart TV.

Cross-Device Journey Tracking

Cross-device tracking follows users across mobile app, web app, smart TV apps, and tablets. Without unified tracking via identity stitching, you credit the wrong channel and miss parts of the subscriber journey.

Mobile measurement partners connect user behavior across devices so you see the complete path: Meta ad click on mobile → web signup → smart TV activation → subscription. This reveals which channels drive awareness versus conversion, and whether your marketing mix is optimized for cross-device behaviors.

Install to Subscription Rate by Channel

Install-to-subscription rate tracks what percentage of app installs convert to paying subscribers within a specific window (7 days, 30 days, 90 days). Low conversion signals onboarding friction or poor targeting,you're acquiring users who browse but don't commit.

Compare install-to-subscription rate across channels:

• Meta campaigns might drive high installs but low conversion if targeting is broad

• Google UAC might bring fewer installs but higher conversion due to search intent

• Influencer partnerships often deliver strong conversion if the influencer's audience aligns with content

Track this in real time so you can pause underperforming campaigns and reallocate budget to channels that deliver paying subscribers, not just installs.

Return on Ad Spend (ROAS) by Channel

ROAS is subscriber revenue generated divided by ad spend. Tracking ROAS by channel (Meta, Google, TikTok, YouTube) reveals which sources are profitable versus which ones burn cash.

Blended ROAS hides underperforming channels. If Meta delivers 5x ROAS and TikTok delivers 1.2x, blending them shows 3.1x and masks the fact that TikTok is barely profitable. Unified attribution makes this visible in real time,you see true ROAS by channel without reconciling screenshots or waiting for end-of-month reports.

For streaming apps, ROAS must account for subscriber LTV, not just first-month revenue. A channel with weak Month 1 ROAS might deliver subscribers who stay for 2+ years, while a channel with strong Month 1 ROAS might bring subscribers who churn in Month 3.

Platform-Specific Performance Metrics

Different viewing platforms have different performance characteristics and monetization profiles. Track metrics by platform to optimize user experience and content delivery.

Mobile App Metrics

Mobile apps are often the primary acquisition channel but not the primary viewing platform. Track:

• Install-to-activation rate: How many installs actually sign up and watch content

• Mobile watch time: Total hours watched on mobile (usually lower than TV/web)

• Offline downloads: Feature adoption rate for offline viewing (signals high engagement)

• Push notification effectiveness: Open rates and conversion to viewing sessions

Mobile matters for acquisition, discovery, and on-the-go viewing. Optimize onboarding and push campaigns to drive mobile users toward content consumption.

Smart TV and Web Metrics

Smart TV apps and web platforms drive the majority of watch time for most streaming services because of larger screens and better viewing experience. Track:

• Activation rate: How many users who signed up on mobile actually activate on TV/web

• Viewing sessions per week: TV viewers typically watch 3-5x per week

• Content completion rates: TV viewers have higher completion rates due to lean-back viewing

Smart TV users are your most valuable segment,they watch more, churn less, and drive better retention. Invest in seamless cross-platform activation (mobile signup → TV login with simple code entry) and optimize TV apps for discovery and playback quality.

Building Your Streaming Analytics Stack

Most streaming teams start with fragmented tools,Google Analytics for web, Firebase for app, payment processor exports for revenue, CDN logs for content delivery metrics,and spend hours each week reconciling data. Building a unified analytics stack means connecting every data source into a single source of truth.

The Five-Layer Analytics Stack

Layer 1: Attribution and user acquisition Track where subscribers come from,Meta ads, Google campaigns, organic search, influencer partnerships. Attribute installs and subscriptions to specific campaigns so you know CAC and ROAS by source.

Layer 2: In-app events and behavior Capture signups, free trial starts, content views, playback errors, subscription conversions, and cancellations. Event tracking reveals conversion funnel drop-offs and engagement patterns.

Layer 3: Content analytics Track watch time, completion rates, title discovery, binge behavior, and content catalog utilization. Content metrics predict retention and inform content licensing/production decisions.

Layer 4: Revenue and monetization Connect subscription revenue, ad revenue (if applicable), payment success rates, and churn to user behavior and acquisition source. Revenue attribution shows which channels deliver profitable subscribers.

Layer 5: Retention and lifecycle Measure Day 1/7/30/90 retention, churn rates by segment, reactivation rates, and LTV by cohort. Retention metrics predict long-term profitability and identify at-risk segments.

Unified platforms integrate all five layers so you see the full subscriber journey,from first ad impression to content consumption to renewal,in one dashboard.

Why Streaming Apps Need Unified Attribution

Attribution is especially critical for streaming platforms because:

• Multi-platform complexity: Users discover on mobile, sign up on web, watch on TV,traditional analytics tools can't stitch these journeys together

• Long conversion windows: Users might install your app, browse for weeks, then subscribe,last-click attribution misses the awareness campaigns that drove discovery

• Cross-channel marketing: Running campaigns across Meta, Google, YouTube, TikTok, influencer partnerships, and CTV ads creates attribution chaos without unified tracking

Mobile measurement partners solve this by collecting user identity signals across devices and platforms, connecting marketing touchpoints to installs to subscriptions in one unified view.

Track What Matters and Scale with Confidence

Tracking the right metrics,CAC, LTV, churn rate, content engagement, ROAS,lets you make profitable decisions instead of guessing. Streaming platforms need unified attribution across mobile, web, and TV apps to see the full picture.

Spreadsheets and siloed dashboards don't scale when you're running dozens of campaigns and need to know which ones are working today, not two weeks from now.

Unify Your Streaming Metrics and Grow Profitably

Linkrunner unifies attribution, deep linking, and analytics so you can see CAC, LTV, ROAS, and retention by channel in one dashboard. No more reconciling screenshots from Meta, Google, and payment processors. Our platform auto-surfaces underperforming campaigns and suggests where to reallocate budget, so you move from manual reporting to always-on intelligence.

Request a demo to see how Linkrunner helps streaming platforms track what matters and scale subscriber acquisition profitably.

FAQs About Streaming App Metrics

What are the most important metrics for subscription streaming apps?

CAC, subscriber LTV, CAC:LTV ratio, churn rate, and free-trial-to-paid conversion rate are the core metrics for SVOD platforms. Each one ties directly to profitability and helps you decide where to spend and where to cut.

How do I track attribution across mobile, web, and smart TV platforms?

Mobile measurement partners use identity stitching to connect user behavior across devices,linking mobile app installs to web signups to smart TV activations. This reveals the complete subscriber journey and shows which marketing channels drive cross-platform conversions.

What is a healthy churn rate for streaming platforms?

Monthly churn rates vary by market and content type, but strong streaming platforms typically see 5-8% monthly churn for subscription tiers. Ad-supported platforms may see higher user churn (10-15%) but care more about DAU than retention since monetization is impression-based.

How often should streaming apps review their metrics?

Review CAC, ROAS, and conversion rate daily or weekly to catch issues early. Review churn rate, content engagement, and LTV monthly to assess long-term trends. Review content ROI and catalog performance quarterly to inform content licensing and production decisions.

Which metrics predict long-term streaming success?

Subscriber LTV, churn rate, content engagement (watch time and completion rates), and CAC:LTV ratio are the best predictors of sustainable growth. High engagement and strong unit economics mean you can scale acquisition without burning cash on subscribers who don't stick around.