Trivas: Omnichannel Analytics for Amazon, Shopify, Meta, Google, and GA4
by Trivas.ai
|
5 min read
Sep 25, 2026
One Dashboard for Every Channel You Sell On
Trivas is an ecommerce analytics and automation platform built on Amazon Redshift. That's the whole pitch, really: one place that holds your Amazon, Shopify, Meta, Google Ads, and GA4 data, instead of five logins and a spreadsheet that's out of date by Tuesday.
Most brands doing real volume across channels are stitching together four or five tabs just to answer "how did we do last week." Amazon Seller Central for sales and fees, Shopify admin for orders and refunds, Meta and Google ad managers for spend, GA4 for the funnel in between. Someone on the team, usually the founder, ends up pasting numbers into a spreadsheet by hand to get one P&L view. Trivas omnichannel analytics exists specifically to kill that spreadsheet.
If you want to see what that looks like with your own data, you can start a trial and connect your channels in one sitting rather than reading about it secondhand.
How Trivas Omnichannel Analytics Works
Here's the mechanical part, because it matters. Trivas pulls raw data straight from each platform's API (Amazon, Shopify, Meta, Google Ads, GA4) into Amazon Redshift, then normalizes everything into a single schema. A "sale" means the same thing whether it came from Amazon FBA or a Shopify checkout. A "cost" includes ad spend, fees, and returns, mapped consistently across channels.
That's different from tools that just embed each platform's native report next to the others. Side-by-side widgets look unified but aren't. You're still doing the math in your head to reconcile Amazon's definition of revenue against Shopify's. Actual normalization means the numbers already agree before you look at them.
Data refreshes on a cadence built for daily decisions, not real-time trading floor stuff nobody needs for a DTC business. You don't need spend updating every 30 seconds. You need yesterday's full picture ready when you sit down with coffee. That's what "real-time enough" means here.
The practical result: a task that used to take a few hours of manual pulls and formula-checking turns into one login. The BI reporting layer is where all of this actually lives, and it's the foundation everything else in Trivas sits on top of.
AI Wingman: Insights on Top of the Data
Once the data's unified, the next problem is that nobody actually stares at dashboards all day. Wingman is the AI layer that watches the numbers for you and surfaces what changed, in plain language, across every connected channel.
Say your Meta CAC jumps 30% on a Thursday. Without Wingman, you'd catch that in Monday's weekly review, four days late, after you've already spent into it. With it, you get flagged the day it happens, with enough context to know whether it's a bid issue, an audience fatigue thing, or just a bad day.
This replaces the habit of manually eyeballing five dashboards hoping you spot the anomaly before it costs real money. You won't, not reliably. People are bad at scanning for small deviations across multiple screens; that's exactly the kind of pattern-matching software does better. More on how the AI layer works is on the product page if you want the deeper mechanics.
Forecasting Built on Your Actual Cross-Channel Data
Forecasting only means something if it's built on data you actually trust, which is why Trivas doesn't bolt a separate modeling tool onto the side. It runs on the same unified Redshift dataset as everything else. No exporting numbers into a different app and hoping the definitions still line up.
Say you're deciding whether to pull 15% of budget off Amazon Ads and push it into Meta prospecting. You can model that shift against your actual historical CAC, margin, and conversion patterns from both channels before you commit a dollar. That's a very different exercise than a spreadsheet forecast, which is accurate the day you build it and stale the moment your channel mix shifts, which is basically always.
Take a closer look at forecasting and simulation if budget planning is where you're currently losing the most time.
Built for Amazon and Shopify Sellers Specifically
Trivas wasn't built as a generic BI tool that someone later retrofitted with ecommerce connectors. It was built around Amazon and Shopify data structures from the start, which shows up most clearly in reconciliation.
Amazon sellers deal with a mess of fees, ad spend across multiple ad types, and FBA inventory costs that don't map cleanly onto a simple revenue line. Shopify sellers deal with a different mess: order-level discounts, partial refunds, multi-currency checkouts. Generic BI tools handle neither well, because neither is generic. Trivas handles both because the schema was designed with these specific quirks in mind, not patched in afterward.
If you sell on Amazon, /solutions/amazon covers the channel-specific detail. Shopify sellers can check /solutions/shopify, and merchants who'd rather install directly from their Shopify admin can find Trivas on the Shopify App Store.
Who Uses Trivas
Different roles pull different things out of the same dashboard, which is sort of the point of unifying the data in the first place.
Founders and CEOs mostly want the single P&L view: revenue, cost, margin, one number, no reconciling. Marketing leaders live in channel-level ROAS and want to know which platform is actually earning its budget this month. Data analysts want raw access to the Redshift warehouse so they can build their own queries instead of waiting on someone else's report. Agencies managing multiple brands need reporting that doesn't require rebuilding a dashboard from scratch for every client.
There's a dedicated page for founders and CEOs if that's your seat. And since pricing scales with how many channels you connect and how much data flows through, /pricing has the actual breakdown rather than a vague "contact us."
See Your Channels in One Place
If you're still pulling numbers from five different logins every Monday morning, that's the exact problem Trivas omnichannel analytics was built to remove. Connect Amazon, Shopify, Meta, Google, and GA4 in one sitting and see what a single, reconciled view actually looks like.
If you're comparing options, particularly if you're evaluating a switch from Triple Whale, Northbeam, or Polar, it's worth talking to a founder directly rather than reading feature lists. Either way, the core idea holds: one dashboard, real reconciliation, no more stitching spreadsheets together by hand. If none of that fits your workflow yet, our resources hub has more on how teams are actually using this stuff day to day, worth a look before you decide either way.
Content author and contributor at Trivas.ai, sharing insights on e-commerce analytics, business intelligence, and data-driven strategies to help businesses grow.
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