Your streaming campaigns are running. Platform dashboards show healthy completion rates, respectable CPMs, and ROAS that look acceptable on paper. You’re making budget decisions based on these metrics, scaling what appears to work and cutting what doesn’t. But the uncomfortable truth is those platform metrics are almost certainly misleading you about actual efficiency, and the optimization decisions you’re making based on them are likely costing you revenue.
The problem isn’t that platforms are lying, it’s that they’re measuring the wrong things in ways that systematically obscure real efficiency. When you optimize for platform-reported metrics, you’re making decisions based on what’s easiest to measure rather than what actually matters for your business. This article examines why standard OTT efficiency measurement fails, how platform attribution bias distorts budget decisions, and what’s required to actually understand whether your streaming advertising is efficient or wasteful.
Key takeaways
- Platform metrics like CPM, completion rates, and reported ROAS measure visibility and engagement but systematically fail to capture true advertising efficiency or incremental business impact
- OTT platform reporting creates attribution bias by claiming credit for conversions that would have happened without your ads, making inefficient campaigns appear successful
- Traditional measurement approaches miss the halo effects where streaming campaigns drive branded search, direct traffic, and purchases through other channels that platform metrics cannot see
- Incrementality testing fails to solve OTT efficiency measurement because point-in-time tests miss ongoing performance dynamics and geo-matched designs don’t work with streaming audiences
- Accurate OTT efficiency measurement requires system-level modeling that evaluates streaming performance against all marketing channels simultaneously, revealing true incremental contribution
The fundamental problem with platform efficiency metrics
Let’s start with what most marketers rely on: platform-reported metrics. Your streaming campaign shows a 3.5x ROAS in the Roku dashboard. Hulu reports 70% completion rates. Amazon Prime Video shows your CPM dropped 20% month-over-month. These all sound like efficiency wins, and that’s precisely the problem; they’re designed to sound good whether or not your advertising is actually efficient.
Platform metrics measure visibility and engagement, not efficiency. A high completion rate tells you people watched your ad, not whether seeing it changed their behavior. A low CPM tells you impressions were cheap, not whether those impressions drove any business value. Even ROAS is problematic when the platform calculating it is the same one selling you the ad space. They have every incentive to attribute conversions generously.
The deeper issue is that these metrics ignore opportunity cost entirely. Your campaign might deliver a 3x platform-reported ROAS while your other marketing channels deliver 5x true incremental returns. The platform metric makes you think you’re being efficient when you’re actually leaving money on the table by not reallocating budget to higher-performing channels. This is more than a technical measurement error. It’s a structural flaw in how platform metrics define efficiency.
How platform attribution bias makes bad campaigns look good
Platform attribution works by connecting ad exposure to subsequent conversions, but the methodology creates systematic bias toward over-crediting advertising. When someone converts after seeing your OTT ad, the platform counts that as an ad-driven conversion. What it cannot tell you is whether that person would have converted anyway without seeing your ad.
This matters enormously for efficiency measurement. Imagine you’re running streaming campaigns targeting people already searching for your brand name. These are high-intent audiences who are probably going to convert regardless of whether they see your OTT ads. When they do convert, the platform reports strong performance and calculates impressive ROAS. You conclude the campaign is efficient and scale spending. But you’re not buying conversions, you’re buying credit for conversions that would have happened anyway.
The attribution windows platforms use compound this problem. Most OTT platforms attribute conversions that happen within 7–30 days after ad exposure, capturing anyone who saw your ad and later converted for any reason. Someone sees your streaming ad on Monday, forgets about it completely, then searches for your product category on Friday and happens to choose your brand. The platform counts that as an ad-driven conversion even though the ad had zero influence on the actual purchase decision.
This creates a vicious cycle where inefficient campaigns appear successful, leading to increased investment in advertising that isn’t actually driving incremental business. The platforms aren’t being dishonest, they’re just measuring correlation, and marketers are treating correlation as proof of efficiency.
The awareness efficiency gap nobody talks about
Upper-funnel OTT campaigns face an even more severe measurement problem: the value they create often appears nowhere in platform metrics. Your streaming awareness campaign introduces someone to your brand. They don’t click, don’t immediately convert, and don’t do anything the platform can track. By platform metrics, this looks like wasted ad spend with no return.
Three weeks later, that same person searches your brand name on Google, clicks your paid search ad, and converts. Google Search gets credit for the conversion. Your streaming campaign shows zero return despite creating the awareness that made everything else work. This isn’t a hypothetical scenario. This is how most awareness advertising actually functions.
The result is that marketers systematically underspend on awareness-focused OTT campaigns because the efficiency appears poor in platform dashboards. You’re optimizing budget allocation based on metrics that literally cannot see the value these campaigns create. Every budget model that uses platform-reported ROAS as an input will recommend cutting awareness spend and shifting to conversion-focused campaigns, even when awareness is your most efficient marketing investment.
This is what we call the halo effect gap. OTT advertising drives branded search volume, increases organic traffic, makes your retargeting more efficient, and influences purchases through indirect pathways. None of this shows up as platform-reported ROAS, so standard efficiency measurement treats it as if it doesn’t exist. For a deeper look at how these spillover effects work, see our article on how awareness campaigns show up across channels.
Why incrementality testing doesn’t fix the problem
When marketers recognize that platform metrics might be misleading, many turn to incrementality testing as a solution. Run a geo-matched test or audience split, measure lift in the exposed group versus the control group, and you’ll know the true incremental impact. In theory, this sounds perfect. In practice, it fails for streaming advertising in predictable ways.
First, incrementality tests capture point-in-time lift rather than ongoing efficiency. Your test might show 15% lift during a two-week period, but what happens in week three? Week eight? The efficiency of OTT campaigns changes based on creative fatigue, audience saturation, competitive activity, and seasonality. A single test result doesn’t tell you whether current spending is efficient, it tells you whether spending was efficient during one specific window under one specific set of conditions.
Second, geo-matched testing fundamentally doesn’t work with streaming audiences the way it works with traditional TV. Someone might live in your “control” geography but stream content on their phone while traveling through your “test” geography. Or they might see your OTT ads on one device at home and convert on a different device at work. The geographic boundaries that make geo-testing viable for broadcast TV are porous and meaningless for streaming advertising.
Third, even well-designed incrementality tests can yield the “right” answer from a poorly executed test design, a problem highlighted in research showing that tests can show positive lift even when the testing methodology is flawed. This creates false confidence where you think you’ve validated efficiency when you’ve actually just measured noise. For more on why incrementality testing struggles with establishing true causality, see our analysis of incrementality test limitations.
The fundamental issue is that incrementality testing tries to solve an attribution problem with isolated experiments when OTT efficiency is actually a system-level question. Your streaming campaigns don’t exist in a vacuum, they interact with every other marketing activity you’re running, and their efficiency depends on that broader context.
What real OTT efficiency measurement requires
Accurate efficiency measurement needs to answer a different question than platform metrics or incrementality tests typically address. Instead of “did people convert after seeing our ads?” or “did ads cause lift during our test window?”, you need to answer “how much incremental revenue did OTT contribute relative to alternative uses of the same budget?”
This requires system-level modeling that evaluates streaming performance against all your marketing channels simultaneously. Marketing mix modeling does this by analyzing the statistical relationship between OTT spending and business outcomes while accounting for seasonality, competitive activity, other marketing channels, and baseline demand. The model reveals how much revenue you’d expect to lose if you cut OTT spending, or how much you’d expect to gain if you increased it.
The advantage of this approach is that it captures the complete picture of OTT efficiency including halo effects that platform metrics miss. When your streaming campaigns drive branded search volume, the MMM attributes that indirect impact back to OTT. When awareness advertising makes your conversion campaigns more efficient, the model accounts for that interaction. You get a true view of incremental contribution rather than platform-reported correlation.
Prescient’s approach specifically addresses the efficiency measurement gap by validating performance across all channels simultaneously. Our models reveal when platform metrics are overstating efficiency by taking credit for conversions that would have happened anyway, and when they’re understating efficiency by ignoring cross-channel impact. This lets you optimize based on actual incremental returns rather than the biased signals that lead most marketers astray.
For example, Saatva was investing heavily in TV advertising but lacked clarity on how their streaming and linear TV campaigns were actually performing relative to their digital channels. Our modeling revealed that their TV advertising—including OTT—was driving substantial halo effects through branded search and direct traffic that platform metrics couldn’t see. By understanding true incrementality across all channels, they optimized their media mix and increased overall revenue by 20% while improving efficiency. That’s the difference between optimizing for platform efficiency versus real efficiency.
The cost of measuring efficiency wrong
Getting OTT efficiency measurement wrong means you’re actively making decisions that hurt business performance. When you scale campaigns that look efficient in platform metrics but aren’t actually incremental, you’re wasting budget. When you cut campaigns that look inefficient in platform metrics but are driving substantial halo effects, you’re eliminating your most valuable marketing.
The compounding effect over time is significant. A 20% efficiency gap—spending on campaigns that deliver 20% less incremental return than you think—translates to thousands or millions in wasted ad spend depending on your budget scale. Worse, it creates strategic misallocation where you chronically underspend on awareness and overspend on conversion-focused campaigns, limiting growth because you never adequately fill the top of your funnel.
The marketers who win with OTT advertising are those who recognize that platform metrics are useful for tactical optimization but terrible for efficiency evaluation. They validate performance through independent measurement that accounts for incrementality and cross-channel effects rather than trusting the dashboards provided by platforms selling them ad inventory. This doesn’t mean platform metrics are useless; completion rates and CPMs matter for creative testing and targeting refinement. They just don’t tell you whether your advertising is efficient.
Moving toward honest efficiency measurement
If you’re currently making OTT budget decisions based primarily on platform-reported metrics, you’re almost certainly misallocating spend. The fix isn’t to find better platform metrics or run more incrementality tests, it’s to fundamentally change how you evaluate efficiency.
Start by acknowledging that platform metrics measure correlation, not cause and effect. Use them to understand campaign delivery and engagement, but stop treating ROAS calculations from platforms as evidence of efficiency. They’re evidence of correlation between ad exposure and conversions, which is related to efficiency…but not the same thing.
Build or buy access to measurement that evaluates OTT performance in the context of your complete marketing system. This typically means marketing mix modeling, though the specific implementation matters. Models need to account for cross-channel effects, nonlinear response curves, and varying decay rates across channels.
Validate your efficiency assumptions by comparing platform-reported performance against modeled incrementality. When they align, you can have more confidence in your conclusions. When they diverge—platform metrics showing strong efficiency but modeling showing weak incrementality, or vice versa—investigate why before making major budget shifts.The goal isn’t perfect measurement precision. It’s directionally accurate understanding of whether your OTT advertising is genuinely efficient or just appears efficient in metrics designed to make platforms look good. That difference determines whether you’re growing profitably or burning money while your dashboards congratulate you on a job well done. Book a demo to see how Prescient helps connect metrics to key business outcomes.
