Your Data Lives in 5 Places. Your Decisions Shouldn't.

Your Shopify dashboard says one revenue number. GA4 says something close, but not identical. Amazon Seller Central has its own version of the truth, on its own schedule. Your Target and Walmart partner portals report a third and fourth set of numbers, none of which reconcile with the others by SKU or by day. This is the daily reality of ecommerce analytics for a brand running retail and DTC, and it's the reason so many finance teams don't trust their own dashboards.

The actual cost of this isn't abstract. It's finance spending 6 to 10 hours a week pulling CSVs out of five systems and stitching them together in a spreadsheet just to produce one blended P&L. That's a part-time job dedicated entirely to reconciliation, not analysis.

Trivas exists to remove that job. The core of the platform is a single Redshift-backed data layer that pulls DTC, marketplace, and retail data into one source of truth, automatically, without a manual export in sight.

What "Omnichannel Analytics" Actually Needs to Do

Most tools that call themselves "omnichannel" aren't. For a brand actually running retail plus DTC, there are three non-negotiables.

Unified SKU-level mapping across channels. Your Shopify SKU, your Amazon ASIN, and Target's internal product ID all need to point to the same underlying product, automatically, or every report downstream is wrong.

True blended CAC and margin, not just ad platform ROAS. A channel can look great on ROAS and still be dragging down blended margin once Amazon referral fees, retail chargebacks, and co-op marketing deductions are factored in.

Same-day data freshness. Retail EDI feeds often lag 48 to 72 hours. If your dashboard is only as current as your slowest data source, you're making this week's decisions on last week's numbers.

Generic ecommerce analytics tools tend to fall short here because they were built DTC-first, meaning Shopify plus Meta and Google ad platforms, with Amazon bolted on afterward and zero real support for retail or EDI data. That's fine if you're a pure DTC brand. It's a real gap the moment retail becomes a meaningful part of revenue.

This is also why spreadsheet reconciliation breaks down as you scale. A manual SKU-mapping error doesn't just cost you one bad report, it compounds every time a retailer changes a product ID, runs a promo, or restates a prior period. By the time someone notices the numbers don't add up, it's usually three weeks and two reorders too late.

For brands built specifically around the Amazon side of this problem, see how Trivas handles Amazon marketplace data in more depth.

How Trivas Covers the Full Channel Stack

Trivas connects to the specific channel stack that a retail-plus-DTC brand actually runs, not a generic subset of it.

DTC runs through Shopify for storefront and subscription revenue, plus GA4 for on-site funnel behavior. Marketplace means Amazon and Amazon Ads for sales and sponsored performance. Paid media is Meta and Google Ads for upper-funnel spend. And retail: direct connectors for Walmart, Target, Best Buy, and Home Depot, the partners where EDI feeds and portal exports usually create the most reconciliation pain.

Frankly, that retail connector list is the differentiator. Most competitors stop at Amazon.

All of it lands in one Redshift warehouse. That's the part that actually matters in practice: it means "revenue" or "margin" is calculated the same way regardless of whether the underlying data came from a Target EDI feed or a Shopify order. No more reconciling two definitions of gross margin that were never built to match in the first place.

On top of that data layer sits Wingman, the AI insights layer, which surfaces anomalies without anyone having to go looking for them. A practical example: Wingman flags when a retail channel's sell-through starts dropping on a SKU at the same time DTC ad spend is being scaled on that exact product, a pattern that's easy to miss when each channel lives in its own dashboard, and expensive to miss for more than a few days.

One Dashboard, Blended Margin by Channel and SKU

The concrete output is one view: gross margin per SKU, broken out side by side across DTC, Amazon, and each retail partner. Not five reports that someone has to mentally merge. One screen.

In practice, this turns the weekly channel reconciliation from a 3-hour manual pull into a 20-minute review. The work that used to be "build the report" becomes "read the report and decide something."

It also feeds forecasting. Trivas's AI-driven demand forecasting factors in retail partner reorder cycles alongside DTC seasonality, so inventory planning isn't happening channel by channel in isolation, with one team guessing at Amazon FBA replenishment while another guesses at a Q4 DTC spike. This is where BI reporting built for blended channel data matters most, since a forecast is only as good as the unified data feeding it.

Trivas vs. Northbeam, Polar, and Triple Whale for Omnichannel Brands

If you're evaluating options, it's worth being blunt about what each tool is actually built for.

Northbeam

  • Primary focus: DTC attribution and ad spend measurement
  • Retail/marketplace depth: Limited native support [VERIFY current retail integration depth before publishing]

Triple Whale

  • Primary focus: DTC and Shopify-centric ecommerce analytics, ad spend attribution
  • Retail/marketplace depth: Limited native support [VERIFY current retail integration depth before publishing]

Polar Analytics

  • Primary focus: Broader BI and reporting across ecommerce channels
  • Where it's closer: Reporting breadth is a genuine strength [VERIFY specific channel coverage]
  • Where it falls short: Lacks the native AI insights and forecasting layer Trivas ships out of the box [VERIFY against Polar's current feature set]

The decision rule for a brand in this position is simple: if more than 20% of revenue comes from Amazon or retail partners, a tool built primarily for DTC attribution will always leave a blind spot on blended margin. That blind spot doesn't show up as a missing dashboard, it shows up as a number that looks fine, until finance tries to tie it back to the bank statement.

For the full feature-by-feature breakdown, see the detailed comparison of Northbeam, Polar, and Trivas.

Built for the Teams Actually Running This

Different roles need different slices of the same data, which is why the unified layer matters more than any single dashboard.

Founders and CEOs need the weekly blended P&L without asking finance to hand-build it every Monday. For marketing leaders, it's channel-level ROAS that already nets out retail chargebacks and Amazon fees, not a raw ad platform number that overstates real performance. Operations managers want inventory and fulfillment visibility across 3PL, Amazon FBA, and retail DC shipments in one place, instead of three separate logins to check on the same product.

Onboarding typically doesn't require hiring an in-house data analyst. Channel connectors and SKU mapping get handled during setup, which is the part that usually derails DIY reconciliation projects in the first place.

See Your Blended Channel Data in One Call

If you're running ecommerce analytics for a brand with retail and DTC channels and still reconciling spreadsheets to get one number everyone trusts, the fastest way to see the difference is to look at your own data, not a demo account.

Book a walkthrough and the team will map your actual channel mix (your current retail partners, Amazon, and DTC platform) before the call, so what you see is your real dashboard structure, not a generic sample.

Stop reporting per channel. Start reporting per brand.

If you're still comparing vendors and want a lower-commitment starting point, a trial is available alongside the option to talk to a founder directly.