TLDR: ROAS often looks inconsistent across platforms because each uses different attribution models. Treat ROAS as a directional signal, not a universal truth, and evaluate performance across the full funnel to make smarter growth decisions.
At a Glance:
- ROAS varies by platform because attribution rules vary. Meta, Google, Bing, and BigCommerce all credit conversions differently, so mismatched ROAS numbers are expected.
- Last-click reporting hides upper-funnel impact. Ecommerce platforms often underreport channels like paid social that drive awareness and consideration earlier in the journey.
- Attribution is a model, not reality. No platform captures the full customer journey. ROAS reflects how a system assigns credit, not absolute performance.
- Better decisions come from context, not one metric. ROAS works best alongside CPA, conversion rate, AOV, and incremental revenue to guide long-term growth.
Return on ad spend, or ROAS, is one of the most commonly referenced metrics in digital advertising.
It is also one of the most misunderstood.
At first glance, ROAS seems straightforward: ROAS = Revenue ÷ Ad Spend.
But in practice, ROAS is only as accurate as the attribution model behind it.
If you have ever noticed that Meta reports one ROAS number while BigCommerce reports another, you are not alone. This is a frequent point of confusion for ecommerce brands investing across Meta, Google, and Bing.
This guide explains why ROAS discrepancies happen, how attribution works, and how to use ROAS correctly for decision-making.
Why ROAS Numbers Differ Across Platforms
Many brands open their dashboards and see reporting that looks something like this:
- Meta shows a strong ROAS
- Google Ads reports something different
- BigCommerce shows lower attributed revenue
- Analytics platforms provide yet another view
At first glance, this can feel like something is wrong, or that one platform must be over- or under-reporting. In reality, this discrepancy is common and expected.
Each platform uses its own rules to decide which touchpoint gets credit for a conversion. Some prioritize the last click, others factor in views, assisted interactions, or longer lookback windows. As a result, the same purchase can be credited to different channels depending on where you’re looking.
So when ROAS numbers don’t line up perfectly, it doesn’t mean your data is broken. It means each platform is telling its version of the story.
That difference comes down to attribution, and understanding how attribution works is key to interpreting performance correctly and making smarter marketing decisions.
What Is Attribution?
Attribution is the method a platform uses to determine which marketing touchpoint receives credit for a sale.
In ecommerce, customers rarely purchase after a single interaction. They might:
- See a social ad
- Click a search ad later
- Return through an email campaign
- Purchase after multiple site visits
Attribution models define which of those interactions gets counted.
BigCommerce Attribution: Last-Click Reporting
BigCommerce typically uses a last-click attribution model, meaning the final traffic source a customer interacts with before completing a purchase receives 100 percent of the credit for that sale.
This approach is straightforward and easy to understand, which is why it’s commonly used in ecommerce platforms.
For example, a customer may first discover your brand through a Meta ad. Later, they search for your brand on Google, click the search result, and make a purchase right away. In BigCommerce reporting, Google is credited with the entire sale because it was the final click before conversion. Meta receives no attributed revenue, even though it played a role in introducing the customer to your brand earlier in the journey.
While this model keeps reporting clean and simple, it often underrepresents upper-funnel channels like paid social and display.
Those channels are designed to build awareness and consideration, but their impact is frequently invisible in last-click reporting, which can make them appear less effective than they actually are.
Meta Attribution: Click and View Windows
Meta uses a different approach.
By default, Meta attributes conversions based on a 7-day click window and a 1-day view window. This means Meta may count a conversion if someone clicked an ad days earlier, even if the final purchase happened through another channel.
That’s intentional. Meta is measuring influence, not just the final interaction.
Most people don’t see a Meta ad and convert immediately. They discover the brand, think about it, click or search later, and convert days afterward. A strict same-day or last-click model ignores that reality and undervalues Meta’s role as a discovery platform.
The 7-day click window reflects normal consideration cycles. For many products and services (especially B2B, higher-ticket, or trust-based offers) three to seven days is a reasonable decision window. If someone clicks an ad and converts within a week, that influence is valid.
The 1-day view window is intentionally conservative. Meta removed longer view-through windows years ago, limiting attribution to high-intent impressions and fast decisions. If someone converts within 24 hours of seeing an ad, it’s reasonable to count that exposure.
Google and Bing Attribution Differences
Search platforms like Google Ads and Microsoft Advertising (Bing) also use attribution models that differ from ecommerce platforms.
Their reporting is built to reflect how search contributes to conversions across the funnel, not just the final click.
Depending on account settings, these platforms may apply:
- Data-driven attribution, which distributes credit across multiple touchpoints rather than assigning 100% of the conversion to the last interaction.
- Cross-device tracking, allowing conversions that start on one device and finish on another to be credited back to search activity.
- Assisted conversion modeling, recognizing that search may play a supporting role even when it isn’t the final step before purchase.
- Lookback windows: Google and Bing allow configurable attribution windows that can extend weeks beyond the initial interaction, meaning a click today may still receive credit for a conversion that happens well into the future.
Ecommerce platforms, by contrast, often default to stricter last-touch or session-based logic.
The Reality: Platforms Tend to Over-Credit Themselves
Advertising platforms are not intentionally misleading, but they are designed to capture impact within their ecosystem.
As a result:
- Ecommerce platforms may understate influence
- Search may capture demand created elsewhere
This is why ROAS should be treated as directional, not absolute. No platform sees the full journey due to privacy limits, cross-device behavior, and offline steps.
Optimizations shouldn’t happen to please an attribution model. Effective ads teams optimize to drive revenue and then use the model that best reflects how people actually buy.
When ROAS Helps and When It Misleads
ROAS is valuable when it’s used in the right context. It works best helping teams track performance trends over time, compare campaigns within the same platform, identify clear winners and losers, and inform budget allocation decisions.
Used this way, ROAS becomes a practical signal for optimization rather than a definitive measure of success. It’s one tool in the decision-making toolkit, not the toolkit itself.
ROAS becomes less reliable when it’s treated as a measure of true profitability or used to compare performance across platforms with different attribution models, such as Meta versus Google.
It can also mislead when it drives reactive, short-term decisions or when upper-funnel campaigns are evaluated in isolation. In those cases, a low ROAS doesn’t automatically mean poor performance. It may simply reflect attribution blind spots, longer purchase cycles, or multi-touch customer journeys that ROAS alone can’t fully capture.
Metrics That Matter Alongside ROAS
For ecommerce brands, stronger performance insights come from combining ROAS with metrics like:
- Site conversion rate
- Cost per acquisition (CPA)
- New vs returning customer revenue
- Average order value (AOV)
- Incremental revenue growth
- Full-funnel contribution
In many accounts, improving conversion rate has a greater impact than chasing minor ROAS fluctuations.
Stop Treating ROAS as the Truth
ROAS is not a single source of truth.
It is a platform-specific lens shaped by attribution rules, tracking limitations, and customer behavior.
Successful brands understand how attribution works, interpret ROAS in context, and focus on overall business growth rather than one isolated number.
Proof Digital helps businesses internalize and act with this mindset. Our Paid Media Services combine performance across Meta, Google, Bing, and ecommerce platforms like BigCommerce to make smarter decisions backed by data.
Ready for reporting that tells the whole story? Partner with Proof Digital to align paid media with real business growth.
FAQs
Why does ROAS look different in Meta, Google, and BigCommerce?
Each platform uses a different attribution model and lookback window, so the same sale can be credited to different channels depending on where you’re viewing the data.
What attribution model does BigCommerce use?
BigCommerce typically uses last-click attribution, meaning the final traffic source before purchase receives 100% of the revenue credit.
How does Meta attribute conversions?
Meta uses a 7-day click and 1-day view attribution window, allowing it to credit conversions influenced by ads even if the final purchase happens elsewhere.
Is ROAS an accurate measure of profitability?
No. ROAS measures revenue relative to ad spend, but it does not account for margins, overhead, repeat purchases, or incremental lift.
How should ecommerce brands use ROAS correctly?
ROAS should be used to compare trends and performance within the same platform, not as a cross-channel source of truth or a standalone decision-maker.
