by Trivas.aiNobody buys an attribution tool for the dashboard. They buy it for what feeds the dashboard.
A slick UI on top of half-connected data is worse than useless, it's actively misleading. It'll show you clean charts built on incomplete inputs, and you won't know what's missing until a number doesn't match reality.
If you're running Shopify plus Amazon plus three or four ad platforms, you need to check integration depth before you sign anything, not after. Once you've built dashboards and trained your team on a tool, ripping it out gets expensive fast.
So this post walks through the Northbeam integrations supported list as it stands today, where the real gaps show up for brands with mixed sales channels, and how Trivas compares on the same categories. No fluff, just what connects and what doesn't.
Northbeam's bread and butter is ad spend data. Its native integrations cover Meta, Google Ads, TikTok, Pinterest, Snapchat, and the usual roster of paid social and search channels.
This makes sense. Attribution tools were built to solve one problem first: figuring out which ad dollar actually drove a sale. So ad platform coverage is Northbeam's strongest category, full stop. If your spend lives mostly in Meta and Google, you'll get solid, mature integrations.
Where it gets murkier is feature parity across channels. The older integrations (Meta, Google) tend to be more battle-tested, with deeper reporting fields and fewer sync quirks. Newer channel additions sometimes lag behind on the details, missing certain campaign-level breakdowns or updating on a slower cadence. Worth asking directly, during a demo, whether a specific platform you spend heavily on gets the same treatment as Meta and Google, or whether it's a newer, thinner connection.
Shopify is the backbone here, as it is for most attribution-first tools. Northbeam's Shopify integration is deep, since order and pixel data from Shopify is basically the other half of the attribution equation. BigCommerce and WooCommerce show up too, though the depth of support tends to trail the Shopify build.
Amazon is the interesting gap. Attribution tools built around web pixel data treat Amazon as an afterthought at best, because Amazon doesn't hand over the same conversion-level tracking a Shopify pixel does. For a pure DTC brand that's a non-issue. For a hybrid Shopify-plus-Amazon brand, it means your Amazon revenue either doesn't show up natively or gets bolted on through a workaround. If Amazon is a meaningful chunk of your revenue, check how Trivas handles Amazon data as a point of comparison before assuming any attribution tool will treat it as a first-class data source.
Email and SMS tend to connect through Klaviyo, which is close to universal at this point. But whether order-level and customer-level data actually flows into the attribution model, versus just sitting there as a separate report, varies by platform. That distinction matters more than people think when you're trying to tie lifetime value back to acquisition channel.
Here's the pattern worth naming directly: attribution-first tools are architected around ad spend and pixel/conversion data. They were not built to reconcile backend sales across marketplaces. That's not a knock on Northbeam specifically, it's a structural fact about the category.
The practical effect shows up in a few places:
None of that is fatal if you're a single-channel Shopify brand running ads on three platforms. It becomes a real problem the moment you add a marketplace or two, because now you're reconciling two systems by hand every reporting cycle. That's exactly the kind of gap worth mapping out with a resource like Trivas's data integration documentation before you commit to a tool built around a narrower data model.
Trivas starts from a different architecture, which changes what "integration" even means. Instead of building an attribution model first and bolting on data sources, Trivas runs on Amazon Redshift as a unified data warehouse. Everything lands in one pipeline, so it's not trying to stretch an ad-spend-first model to cover marketplace or backend data after the fact.
That shows up most clearly in marketplace coverage. Trivas connects Amazon, Walmart, Target, eBay, Etsy, Best Buy, Home Depot, Zalando, and Allegro, among others. For a brand selling across three or four marketplaces plus a DTC storefront, that's the difference between one dashboard and five spreadsheets.
On ecommerce platforms, Trivas supports Shopify and WooCommerce natively. If you're running Shopify specifically, Trivas AI on the Shopify App Store is the direct install path, and the Shopify integration details cover what data actually syncs.
Operational tooling is the other differentiator. Trivas connects Klaviyo, Mailchimp, Stripe, ShipStation, Akeneo, and Easyship, pulling in order, fulfillment, and billing data that attribution-first platforms generally don't touch. That matters if you're trying to connect ad spend all the way through to fulfillment cost and actual margin, not just top-line revenue.
For anything outside the standard list, Trivas has an integration request process alongside API and developer support for brands running non-standard stacks. Here's a quick side by side on where the two philosophies land:
Category
Northbeam
Trivas
Core architecture
Attribution model built around ad spend and pixel data
Unified data warehouse on Amazon Redshift
Ad platforms
Meta, Google, TikTok, Pinterest, Snapchat
Meta, Google, TikTok and others feeding the same warehouse
Marketplaces
Not a core focus, workarounds common
Amazon, Walmart, Target, eBay, Etsy, Best Buy, Home Depot, Zalando, Allegro
Ecommerce platforms
Shopify primary, BigCommerce/WooCommerce lighter support
Shopify and WooCommerce native
Operational tools
Limited beyond email/SMS
Klaviyo, Mailchimp, Stripe, ShipStation, Akeneo, Easyship
Before you sign, run the list against your actual stack, not the vendor's homepage screenshot. A few questions worth asking directly:
Does the tool cover every ad platform you actively spend meaningful budget on, not just the top three everyone supports? A tool that nails Meta and Google but treats Pinterest or Snapchat as an afterthought will show you a distorted channel mix.
Does it reconcile marketplace sales, or only DTC web traffic? If Amazon, Walmart, or Etsy make up a real share of revenue, ask specifically how that data gets in, and whether it's live or a manual upload.
Are the integrations native and real-time, or CSV-based workarounds? A "supported" integration that means exporting a spreadsheet every Monday isn't really an integration, it's a chore with a nicer name.
Is there a clear path for adding a source that isn't on the default list? A vendor with a real API and a documented request process is telling you something about how the product is built. A vendor without one is telling you something too.
If you want the fuller picture, feature by feature and pricing included, the side-by-side breakdown of Northbeam, Polar, and Trivas covers more ground than integrations alone.
But the integrations question is the one that decides whether the rest of the tool even works for you. If you're selling only on Shopify with a handful of ad platforms, most attribution tools will do fine. The moment Amazon, Walmart, or another marketplace enters the picture, integration breadth stops being a nice-to-have and becomes the whole decision.
Worth spending an afternoon on before you sign a contract, not after. If you're mapping out your own stack, take a look at what Trivas currently connects, and if something you rely on isn't listed, ask about it directly rather than assuming it's off the table. And if you want more breakdowns like this one, our resource hub gets updated as integration lists change on both sides.
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