by Trivas.aiMost ROAS discrepancies people fight about have nothing to do with the dashboard they're staring at. They come from the data source underneath it. Platform-reported ROAS from Meta or Google isn't the same number as warehouse-level ROAS built off your actual orders, and no amount of pretty UI fixes that gap.
So before you ask which ecommerce analytics tool with the best ROAS tracking exists, ask a narrower question: where does each tool's ROAS number actually come from, and how fresh is it?
The real evaluation criteria are attribution methodology, data freshness, and whether the tool separates blended ROAS from platform ROAS instead of quietly blending them into one number and calling it a day. This comparison looks at four tools through that lens: Trivas, Triple Whale, Northbeam, and Polar Analytics. Not on brand polish, on how each one gets to the ROAS figure you end up making budget decisions with.
Trivas runs on Amazon Redshift, pulling raw order data and raw ad spend data into a warehouse rather than layering an attribution model on top of platform exports. That matters because it lets you put blended ROAS and platform-reported ROAS side by side and see exactly where they diverge, instead of trusting a single blended figure and hoping it's right.
Triple Whale is built around pixel-based tracking, with its own attribution model sitting on top of platform data. That model is doing real interpretive work between the raw click and the ROAS number you see, which is worth understanding before you treat it as ground truth.
Northbeam leans hardest into multi-touch attribution. MTA modeling is the core differentiator for how it splits ROAS by channel, which can be useful if you're trying to credit upper-funnel spend properly, but it also means the ROAS number is a model output, not a direct pull from your orders.
Polar Analytics plays a different game entirely. Its strength is flexible reporting and dashboard customization across data sources, so you can build the view you want. Whether that view reflects true blended ROAS depends on how you configure it.
Here's the part vendors don't volunteer: ask each one directly how their attribution window and de-duplication logic work. A 7-day click window versus a 1-day view window will move your ROAS by a lot, and if two ad platforms both claim credit for the same order, de-duplication logic decides whose number wins. Get that answer in writing before you trust any tool's ROAS output.
Data infrastructure
Ad platform coverage
Reporting speed
AI-driven insights
Forecasting
Setup and integration
If you want a deeper side-by-side on two of these specifically, we've also written up Northbeam vs Polar vs Trivas and Triple Whale vs Polar vs Trivas.
Most tools hand you a dashboard and leave the interpretation to you. You notice ROAS dipped on Tuesday, then spend twenty minutes cross-tabbing spend, CPM, and audience data to figure out why.
Wingman skips that step. It surfaces plain-language explanations for ROAS swings as they happen, instead of making you go find them.
Here's what that looks like in practice. A specific ad set's ROAS drops. Wingman flags it and points to a likely cause, maybe a CPM spike, maybe audience saturation, maybe creative fatigue, before you've even opened the ad platform to check. You're starting the investigation with a hypothesis instead of a blank dashboard.
That's a narrower job than full BI reporting, though. If you want the complete dashboard picture beyond just ROAS anomalies, that's what BI reporting is built for: the full performance view across Amazon, Shopify, and your ad platforms, with Wingman as the layer that tells you what to look at first.
Simple stacks (Meta/Google on Shopify only)
If you're running two ad platforms into one storefront, weigh simplicity against depth honestly. You may not need a Redshift-backed warehouse if your attribution questions are straightforward. But you'll still want to know whether the tool you pick separates blended from platform ROAS, because that gap shows up even in simple stacks.
Amazon plus Shopify brands
This is where a cross-channel pipeline earns its keep. Reconciling ROAS across a marketplace and a DTC storefront means pulling raw spend and order data from two very different environments and getting them into one place that doesn't play favorites. A Redshift-based pipeline like Trivas's is built for exactly that kind of reconciliation.
Agencies managing multiple client accounts
Reporting ROAS consistently across a dozen different client stacks is a different problem than reporting it for one brand. If that's your workflow, it's worth reading how this plays out for agencies and consultants specifically, since the reporting cadence and client-facing requirements are different from an in-house team's.
Data-heavy teams vs teams that want it out of the box
If your team wants to build custom ROAS models on top of raw data, a raw warehouse approach gives you room to do that. If you'd rather have a pre-built attribution model handed to you, a tool like Northbeam's MTA layer might fit better, so long as you understand what the model is doing under the hood.
Before you evaluate any vendor, sanity-check what you're currently looking at. Run your numbers through the ROAS calculator and compare your current blended ROAS against your platform-reported ROAS side by side.
Then pull 30 days of raw spend and order data yourself. Not the dashboard summary, the actual line items. Compare that against whatever your current platform is reporting. If the gap is small, your current tool's methodology is probably fine. If it's not small, that gap is exactly what you should be interrogating with every vendor on your shortlist, this one included.
If you're an Amazon-plus-Shopify brand trying to reconcile ROAS across marketplaces and ad platforms, talk to a founder and walk through how the Redshift pipeline handles it for your specific stack.
If you'd rather see it yourself first, start a trial and run Wingman's anomaly detection against your own ad accounts for a week.
Either way, the takeaway holds: pick your analytics tool based on data infrastructure and attribution transparency, not the dashboard's polish. And if you're still deciding, our blog has more breakdowns like this one worth a look before you commit.
explore more insights
3 min read
3 min read
3 min read
.d53b12e5.png&w=3840&q=75 "Trivas.ai")