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Marketers love their numbers, especially ROAS. It’s neat, flattering and easy to justify. But return on ad spend has become a comfort metric: a figure that appears to validate spend and invites pouring more money into the same ad campaign tactics. But it doesn’t tell the full story behind advertising efforts and their impact.
The danger of ROAS isn’t the number itself; it’s how easily organizations use it as a shield from harder truths. As Dr. Grace Kite, founder of Magic Numbers, shared in Funnel’s Marketing Measurement Matters podcast, the real barrier to better measurement isn’t technical; it’s organizational courage.
Numbers only matter if people are willing to act on them. But too often, organizations hide behind metrics that look good rather than those that tell the truth. Even with better measurement models, what’s missing is the bravery to face uncomfortable numbers and make bold decisions.
The problem with marketing ROAS
ROAS (return on ad spend) tracks how much ad revenue each campaign generates for every ad dollar spent. This metric has a place on almost every marketing dashboard because it’s simple and flattering. One straightforward equation calculates ROAS: revenue ÷ ad spend. Plug in your numbers, generate your result and crack the code to marketing success.
But marketing rarely fits into neat equations. While the ROAS formula promises a window into what’s working, that view is distorted.
Why is ROAS misleading?
When the focus is on ROAS, marketing teams use the revenue-to-ad-spend ratio to guide where the budget goes next.
Sounds logical, right? High ROAS should mean higher efficiency. If an ad campaign spends $1,000 and generates $5,000 in attributed sales, you’ve got yourself a 5x ROAS. Easy math and instant validation.
The numbers feel objective and data-driven, but that same logic only rewards short-term, easily attributable channels.
The truth is, a high ROAS doesn’t always mean your ads are effective. It doesn’t take external factors into account, such as seasonality, market changes or pricing shifts. ROAS tells you how revenue is generated from ad spend, but it can’t tell you whether that growth is sustainable or circumstantial.
So, your numbers may look good, even though something outside of a seemingly high-performance channel is causing the revenue lift. It's a comfort metric that's easy to measure, easy to defend, but rarely reflects the true impact of your advertising efforts. Here’s an example: let’s say you start running ads for your ice cream brand in July. Sales surge and ROAS looks incredible. But the driver wasn’t necessarily marketing; it was the weather.
ROAS also favors ad campaigns targeting users who were going to convert anyway. Retargeting, branded search and cart-recovery ads often show great efficiency because they capture existing intent, not new demand. Optimizing for a target ROAS might look efficient in the short term, but it can lead to overvaluing channels that convert easily and underinvesting in campaigns that drive new demand.
Meanwhile, upper-funnel marketing campaigns, like video, display or top-of-funnel content, which focus on building awareness and reaching a target audience, appear expensive and inefficient. That’s dangerous because marketers can fall into the trap of whack-a-mole decision-making, where you’re chasing misleading signals rather than building digital marketing strategies that expand reach or profitability.
That’s why some marketers are shifting focus from revenue to profit by replacing ROAS with a truer metric: POAS (profit on ad spend). Unlike ROAS, POAS factors in marketing expenses and profit margins. It provides a more realistic view of profitability, one that connects marketing performance to business outcomes and long-term growth.
But POAS alone won’t give you a complete picture either. There are more marketing measurement techniques worth employing if you want to make really confident decisions, but knowing what to use and when can be a puzzle in itself.
MMM vs. attribution and the “triangulation trap”
Both marketing mix modeling (MMM) and attribution aim to answer the same question: “What’s working?” But they approach this marketing effectiveness question differently.
Attribution focuses on digital touchpoints, tracking clicks, impressions and interactions that happen within platforms. It’s designed to help you understand short-term performance and optimize advertising campaigns on the fly. For example, for an ecommerce marketing team deciding where to shift budget between Google Ads and Meta next month, attribution cuts straight to the point.
But Grace argues that platform-owned attribution models are inherently biased. They’re grading their own homework. She believes that’s not confusion; it’s design. Attribution systems were built to favor the platforms that run them. Facebook’s model will always show Facebook ads as critical, just as Google’s model will show Google’s ads as necessary.
For larger or more complex advertisers, this makes triangulated approaches like MMM more reliable. Marketing mix modeling blends online and offline data, seasonality, pricing and competition to find what drives incremental sales. So, the campaign returns may look smaller, but they’re real.
That doesn’t mean attribution has no place, though. As Funnel’s VP of Measurement, János Moldvay explains, “For mid-market teams working mostly within Google and Meta, data-driven attribution remains the most practical way to gauge short-term performance — even if it only tells part of the story.”
What is the triangulation trap?
While attribution is useful for teams relying on only a couple of channels, especially data-driven attribution, once marketing spans multiple channels, you need something independent — a model that tells the truth about what’s actually driving results.
Google promotes a “triangle of methods”: attribution, experiments and MMM. In theory, triangulating across methods should give you a bigger picture and more clarity, since you’re looking at the why behind your return on ad spend, not just how much.
But in practice, it often becomes the Bermuda Triangle.
According to MMA Global’s State of Attribution Report, 80% of marketers struggle to make sense of results from different measurement tools. Marketers are drowning in conflicting numbers. But better data alone won’t fix broken measurement, because the real challenge isn’t technical; it’s human.
Why bravery is the missing ingredient for driving revenue
Even with the best measurement tools, analytics only matter if people act on them. Too often, CMOs and leaders hesitate to follow where the data leads, not because it’s wrong, but because of organizational or political barriers.
Maybe the model shows you should cut advertising costs on a high-visibility channel or pull marketing budget from a senior executive’s “favorite” platform. But those are uncomfortable moves. It’s easier and more tempting to stick with an inflated ROAS because this marketing metric looks good in board meetings, even if it doesn’t drive long-term growth.
Grace’s philosophy is simple: be “data people with people skills.” Data only creates impact when it’s turned into action, and that requires communication, alignment and bravery. Her own story reflects this mindset. When she founded Magic Numbers, many advised against the name because it was too playful for a serious analytics firm. She chose it anyway. It was a bold signal that evidence, not convention, would define her brand.
When leaders choose bravery, when they use data not just to validate their decisions but to challenge them, they set the tone for the rest of the team. It creates a culture where truth matters more than comfort.
Progress doesn’t hinge on having the perfect model; it hinges on the courage to use it.
How accurate data empowers brave decisions
Good measurement is not just about the models you use. It’s about the quality and trustworthiness of your data. And brave choices require trust.
The confidence to act on advertising spend
No matter how smart your model is, if your foundation is shaky, your decisions won’t hold up to challenges. Reliable, trustworthy data gives marketers the confidence to act and defend their decisions.
You don’t have to hope your insights are valid, because they are. With clean, consistent, unified data, it’s easier to convince leadership to pivot the marketing strategy when something isn’t working.
Bold moves, such as reallocating ad spend to awareness ad campaigns or cutting specific channels, can be justified through incremental value. Finance and leadership have less hesitation and fewer objections when you can back your choices with evidence.
Anteriad’s Marketing Outlook Study found that 46% of marketers who are confident in their data strategy reported a significant increase in revenue.
Brave decision-making isn’t only about being fearless; it’s about being certain. When the data used to power your measurement models is trustworthy, you can take action knowing the numbers aren’t lying.
The foundation of courageous decisions
Nice numbers look good, but that's all they do. Vanity marketing metrics, like inflated ROAS, create a false sense of confidence. But you can’t move the needle with feel-good numbers that fall apart upon closer inspection.
Even small inconsistencies, like duplicate conversions, mismatched naming conventions or misaligned time zones, can create doubt. A single reporting error can spiral into confusion between marketing, finance and leadership. And suddenly, no one’s sure which number is right.
Progress stalls not because the strategy is wrong, but because the data foundation is weak. But when every source, from ad platforms to CRM, speaks the same language, there’s a single source of truth.
Clean, centralized data gives teams the visibility they need to understand what’s working, what isn’t and where to go next. It replaces patchwork reports with one connected story that’s reliable enough to make bold moves on.
How Funnel can help you measure smarter and act with confidence
With Funnel, marketers act with confidence because every decision rests on clean, consistent and trustworthy data. By centralizing marketing data from more than 500 connectors, including ad platforms, analytics tools, email and social, Funnel provides a single source of truth that’s accessible and easy to understand for all stakeholders.
There’s no need to manually prep your data. Funnel automatically standardizes metrics, harmonizes naming structures, validates data quality and uses Conventions to detect and manage inconsistencies in campaign or UTM naming. This helps teams enforce consistent structures and business-level breakdowns across platforms without overwriting the original data.
Funnel also combines data-driven attribution, marketing mix modeling (MMM) and incrementality testing to give you a complete view of performance across every channel. And every model is powered by data you can trust.
For instance, when Deuba, one of Europe’s largest ecommerce companies, partnered with Funnel, they moved beyond last-click attribution to deploy an AI-powered multi-touch attribution model and marketing mix modeling. As a result, they gained deeper insights into previously under-credited channels, optimized budget allocation and improved cost-per-acquisition and ROI.
With Funnel dashboards, campaign management becomes simpler and smarter. They help teams connect and visualize their marketing data, so everyone is on the same page. Transparency across departments builds trust, and trust fuels the kind of brave, cross-functional decisions that actually move growth forward.
With a powerful and scalable data infrastructure, you get the freedom and credibility to make bolder, smarter marketing decisions with numbers that back you up. Ready to move past comfort metrics? Build a foundation that makes brave decisions possible. Start for free now.