by Om RathodRetail POS data sits in one system. Wholesale orders live in EDI feeds, a distributor portal, or a shared spreadsheet nobody fully trusts. DTC data sits in Shopify and whatever ad platforms you're running. Three businesses, three data schemas, and usually one person stuck reconciling all of it before anyone can answer a simple question: which channel is actually making money.
If you're searching for ecommerce analytics for a brand with retail, wholesale, and DTC running at once, you already know the problem isn't a lack of dashboards. It's that most dashboards only speak one language. A finance or ops lead at a mid-size omnichannel brand can easily burn 5 to 10 hours a week just pulling three separate revenue pictures into one tab before the real analysis starts.
Most of the popular ecommerce analytics tools (Triple Whale, Northbeam, Polar Analytics) were built DTC-first. Wholesale and retail get bolted on as an afterthought, if they're supported at all. This page is for the brands those tools weren't built for: the ones running Amazon, wholesale accounts, and a Shopify storefront simultaneously, not the single-channel DTC startup still figuring out its first ad campaign.
Blended CAC only means something if every dollar of revenue sits in the same place as every dollar of ad spend. Once wholesale revenue lives in a separate spreadsheet, your CAC math is quietly wrong, and nobody notices until a board meeting.
Here's the part that trips up most tools: wholesale data usually comes in as a CSV upload. That breaks the "real-time" promise these platforms sell you on, and manual uploads introduce errors every single time someone fat-fingers a column.
Then there's margin. Retail and wholesale don't behave like DTC. Distributor discounts, MAP pricing enforcement, chargebacks, slotting fees: none of that maps cleanly onto a DTC-native margin model. A generic dashboard built for one revenue stream just averages over the differences, which means the number on screen is wrong in a specific, structural way.
Take a brand doing $8M in DTC and $4M in wholesale. Without normalizing both revenue streams onto one schema, there's no way to see true blended contribution margin. You'll get two numbers that don't talk to each other, and a founder asking why the "total revenue" slide never matches what finance signed off on.
Four data types have to sit in the same warehouse before any of this works: DTC storefront orders, marketplace/retail sell-through, wholesale purchase orders, and ad spend across Meta, Google, and Amazon Ads. Miss one, and you're back to manual reconciliation.
This is why it needs to be an actual warehouse, not a reporting layer bolted onto Shopify's API. Shopify's API was never built to hold wholesale PO volume alongside ad spend history. Amazon Redshift can. At any real scale, the difference between "reporting layer" and "warehouse" is the difference between a dashboard that times out and one that doesn't.
You also need channel-level margin logic baked in, not applied after the fact: wholesale discount tiers, retail slotting fees, DTC promo codes, all reconciled to one P&L view. Without that logic, "revenue" is a vanity number and "margin" is a guess.
And forecasting only works if it accounts for all three. Inventory planning that's based purely on DTC sell-through velocity will miss wholesale PO lead times entirely, which means you either overstock for DTC or get caught short when a distributor places a bigger order than usual.
Trivas is built on Amazon Redshift, pulling Amazon, Shopify, Meta and Google Ads, and GA4 funnel data into one schema. That's not a marketing detail. It's the reason wholesale volume doesn't choke the system the way it does with API-only reporting tools.
On top of that sits Wingman, our AI insights layer. Instead of building a pivot table to catch a margin problem, Wingman surfaces it directly: something like "wholesale account X margin dropped 6 points this quarter" shows up as a flagged insight, not a mystery buried in a spreadsheet three tabs deep.
Forecasting follows the same logic. Our AI-driven demand forecasting factors in wholesale PO lead times alongside DTC velocity, so inventory planning isn't blind to two-thirds of your business.
Practically, this turns a weekly cross-channel report from a multi-hour spreadsheet exercise into a dashboard you check like email. If you want to see how the dashboard layer works in detail, that's covered on bi-reporting, and marketplace/retail sell-through integration specifics are covered under Amazon solutions.
Triple Whale, Northbeam, and Polar Analytics are strong at what they were built for: DTC attribution and ad spend reporting. [VERIFY] each platform's exact wholesale/retail connector support before publishing specifics here, since that changes fast and vendor claims don't always match what's actually shippable.
The structural difference is the warehouse underneath. Trivas runs on Redshift; dashboard-only tools tend to cap out on data volume and channel breadth once you add wholesale POs or multi-marketplace retail feeds on top of DTC.
For a brand deciding between these options, the real questions are: how many channels do you actually need connected, how much data volume are you pushing through it, do you need custom margin logic for wholesale/retail pricing structures, and are you looking for forecasting or just spend reporting. Answer those honestly and the shortlist gets short fast.
Full breakdown of how these platforms stack up is here: Northbeam vs. Polar vs. Trivas.
This is built for brands doing roughly $5M to $50M+ combined revenue across DTC, Amazon or another marketplace, and at least one wholesale or retail distribution channel. Below that, a single-channel tool is probably fine. Above it, spreadsheet stitching stops scaling.
Two people tend to land on this page. Founders and CEOs who need one blended view for board reporting and can't afford to present three disconnected revenue numbers. And operations managers who are the ones actually reconciling channel-level inventory and margin every week, usually the person who feels the 5-10 hours of manual work most directly.
If that's you, go deeper on your specific workflow: founders and CEOs or operations managers.
The objection we hear most: "this sounds like a big lift to implement." It's fair, given how bad most enterprise BI rollouts have been.
In practice, it's connector-based. Shopify, Amazon, and ad platforms connect through native integrations, and that part is fast. Wholesale is the piece that varies: it comes in via EDI feed, CSV, or API depending on what your distributor's system actually supports. That's not a Trivas limitation, it's the reality of how wholesale data gets shared industry-wide.
For the DTC storefront side, setup details are covered under Shopify integration. For wholesale feeds or anything that isn't native-API-ready, that's handled through custom data integrations.
Realistic timeline: standard channels are live in days. Wholesale feed mapping depends on your distributor's data format, so that timeline varies, but it's not a six-month project unless someone's system is genuinely a mess.
One warehouse, one margin model, every channel. That's the whole pitch, and it's worth seeing against your actual channel mix before you commit to anything.
Book a walkthrough with a founder and bring your specific retail, wholesale, and DTC setup. If you'd rather explore it yourself first, the self-serve trial is open too.
explore more insights
3 min read
3 min read
3 min read
.d53b12e5.png&w=3840&q=75 "Trivas.ai")