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Platforms measure correlation. Your business runs on causation. Most teams don't know the difference until the P&L tells them.
Every platform says it drove the sale. None of them can prove it. And you're making million-dollar budget decisions on that gap every quarter.
This 20-minute breakdown shows you what that actually costs and what to do about it.
What your marketing actually drives. Where ROAS breaks down. How leading brands measure incrementality.
On the setup call, we connect your data and scope your first test. By day 7, you'll know whether Stella is worth it.
We built Stella because the brands we worked with kept making million-dollar budget decisions on data that couldn't prove causality.
Platform ROAS looked great. The P&L told a different story. And nobody had a system to close that gap without hiring a full data science team.
I'm Brenden, co-founder of Stella and a former media buyer. I've sat in the CFO meeting where the numbers look right but the business isn't growing. That's the problem we solve.
If you're here, you're probably asking: What do I replace platform reporting with? And can I trust it enough to actually act on it?
The video walks through the full methodology. Here's the short version.
Most teams try one measurement method and walk away unconvinced. A single holdout or a standalone MMM gives you a number, but not enough confidence to act on it. Stella combines three methods that calibrate each other.
Geo Holdouts give you ground truth on a specific channel.
Media Mix Modeling takes that truth and shows you the full portfolio, including where each channel hits diminishing returns.
Always-On Incrementality runs daily so you catch shifts before they compound. It also tracks impact across Shopify, Amazon, and retail, so you see the full picture of what a channel is actually doing.
When all three point to the same answer, you're not guessing. That's the system.
A mid-market DTC brand came to us spending $400K/month across Meta, Google, and TikTok. Meta was reporting a 4.2x ROAS. Google showed 3.6x. The team was scaling Meta aggressively based on those numbers. Revenue was growing, but margins were tightening and nobody could explain why.
We started with a Meta holdout study. Turned off Meta in a set of matched test regions and measured what happened to revenue. The platform said 4.2x. The holdout showed the true incremental ROAS was 1.8x. More than half the conversions Meta was claiming would have happened anyway.
Next, we ran a Bayesian MMM calibrated by the holdout results. The MMM confirmed the 1.8x on Meta, but it also showed something the holdout alone couldn't: the response curve. Meta's marginal return was strong up to about $280K/month. Past that, each additional dollar was generating less and less incremental revenue. They were spending $400K. The last $120K was barely moving the needle.
Meta's response curve. The flattening past $280K/month is the saturation point.
But the team didn't just take the model's word for it. They ran a scale test. Pulled Meta back to $280K and redistributed the $120K into Google and TikTok campaigns that the MMM flagged as under-saturated. Over the next 8 weeks, total revenue held steady while marketing efficiency improved 18%. Same top line. Better margins. The model was right.
That's the system working. The holdout gave them ground truth. The MMM showed them where the saturation point was. The scale test proved the reallocation would hold. Three methods, one answer. The CFO stopped asking "how do you know?" because the team could show the work.
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We were making budget decisions on platform data and hoping for the best. Stella gave us the causal layer we needed. For the first time, we could tell finance exactly which dollars were incremental and which ones were just taking credit.
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We always suspected we were over-invested in channels that looked efficient on paper. Stella confirmed it and showed us exactly where to move budget. The first holdout study paid for a year of the platform.
The case above shows a channel that was overcredited and overspent. But measurement doesn't always reveal waste. Sometimes it reveals hidden value, like a channel that's under-invested or driving conversions on platforms you're not tracking. Both happen at the same time inside the same media mix. Here's what the shift looks like:
Google reports 3.8x ROAS. You scale the budget.
Revenue doesn't move. Marketing efficiency might even drop.
You don't know why because every channel is claiming credit for the same conversions.
Budget conversations feel political. Finance asks hard questions. You defend with platform screenshots.
Holdout shows Google's true iROAS is 1.2x. MMM confirms saturation.
You reallocate to channels with real headroom. Revenue improves on the same budget.
You can see halo effects across Shopify and Amazon that platform attribution misses entirely.
Budget conversations are grounded in causal evidence. You present converging data, not platform self-reports.
The hardest part of measurement isn't getting the data. It's doing something with it.
But when your holdout says a channel is a 1.2x, your MMM says it's saturated, and your Always-On shows declining contribution month over month, that's not one model's opinion. That's three independent methods agreeing.
At that point, the risk isn't in acting. It's in ignoring it.
The first question smart marketers ask about any model is "why should I trust it?" Fair question. Here's how Stella earns that trust.
Your data runs through multiple models. Stella surfaces the one with the strongest accuracy, not the one that tells the best story.
We test the model on data it hasn't seen before. If it can't predict what actually happened, it doesn't get used.
The MMM doesn't run on assumptions alone. It's anchored to real holdout experiments, so it's weighted by actual causal evidence.
Every result comes with a confidence range, not just a single number. We show the uncertainty because that's what honest measurement looks like.
We have no stake in whether your channels look good or bad. We care that the data is something we can stand behind.
If you're paying for a measurement tool, it should produce at least one of these:
1. Increase spend while maintaining efficiency. You find channels with headroom and push into them because the models confirm they haven't saturated.
2. Decrease spend while maintaining revenue. You pull back on overcredited channels without losing real sales. The revenue stays. The waste goes.
3. Maintain spend while increasing revenue. You reallocate from saturated channels to efficient ones. Same budget. More incremental revenue.
If your current measurement can't help you do at least one of these, it's not really measurement.
Stella is for mid-market ecommerce and retail brands spending real money on paid media. If you're investing six figures a month or more and your current measurement is mostly platform reporting, you're who we built this for.
If you already have a data science team and a $250K enterprise vendor, we're probably not replacing that. We're the alternative for everyone else.
One thing to know: this isn't a set-it-and-forget-it tool. The brands that get the most from Stella treat measurement as an ongoing practice, not a one-time project.
Bigger vendors are built for enterprise budgets and enterprise timelines. If you're mid-market, you're getting their junior team and their highest price tier. Stella was built for your scale. Results in days, not quarters.
DIY holdouts produce a number. But without a system connecting that number to an MMM and Always-On tool, you're back to making decisions the old way.
Running an MMM in ChatGPT or Claude will give you an answer you can't validate. Stella is the infrastructure. AI is the explainer. Those are not the same thing.
If you're spending $200K a month on paid media and relying on platform ROAS, there's a good chance 20-40% of that budget is going to channels that look efficient but aren't actually driving new revenue.
That's $40K to $80K a month.
Over a year, that's half a million to a million dollars in budget decisions based on correlation instead of causation.
Stella is $3,000 a month with no contract. The risk isn't paying for measurement. The risk is making million-dollar budget decisions without it.
We don't do traditional demos. There's no 45-minute deck followed by a "let me check with my manager on pricing" moment.
You book a 30-minute setup call. On that call, we connect your data sources, walk through your current measurement gaps, and scope your first study.
By the time we hang up, your 7-day free trial is live and you're already running.
At the end of 7 days, if you see value, you upgrade directly in the app. $3,000 a month. No annual contracts. No lock-in.
We'd rather earn the renewal every month than trap you in a contract.
And if you're not ready right now, that's fine. Come back when the timing makes sense. We're not going anywhere and we're not running a flash sale.
30 minutes. We connect your data, scope your first test, and you leave with a live workspace. Not a demo. A working session.
Works with your existing data sources. No data science team required. You'll know within 7 days if it's valuable.