Moira Beauty ecommerce analytics is a useful case study in what happens when a growing beauty brand stops guessing and starts seeing all its channels in one place. This piece walks through the reporting problem the brand had, what they set up in Trivas, and what changed once the dashboards were live.

Who Is Moira Beauty and What Were They Solving For

Moira Beauty is a DTC skincare and color cosmetics brand selling primarily through Shopify, with a presence on Amazon . Growth had outpaced the reporting stack, which is typical for a brand at this stage. Revenue was split across a direct Shopify store and marketplace listings, with paid acquisition running on Meta and Google simultaneously.

The trigger for looking at a new analytics setup wasn't a single bad month. It was a gap. How fast the team needed to make decisions versus how long it took to pull the numbers together. Every campaign review, every inventory call, every "why did conversion rate drop this week" question meant opening three or four different tools first.

The Reporting Problem Before Trivas

Before consolidating, Moira Beauty's reporting looked like most mid-stage DTC brands: a Shopify admin panel, a Meta Ads Manager login, a Google Ads account, GA4, and a spreadsheet someone updated manually to stitch the numbers into something resembling a full picture.

That spreadsheet process ate real time. Even when the numbers were compiled, they were often a day or two stale by the time anyone looked at them.

The sharper problem was blended visibility. Meta would report one ROAS, Google another, and neither accounted for what actually happened at checkout on Shopify, let alone margin by SKU. Nobody could answer a simple question like "which products are actually profitable once we account for ad spend across every channel" without a multi-hour manual pull. That's a common gap for brands managing Shopify performance data alongside multiple ad platforms with no single source of truth tying it together.

What Moira Beauty Set Up in Trivas

The setup connected Moira Beauty's core data sources into one environment: the Shopify store, Meta and Google Ads accounts, and GA4 funnel data.

From there, the team moved daily and weekly performance checks into the BI reporting dashboards, replacing the old routine of opening four separate logins every morning. Instead of reconciling numbers by hand, the dashboards pulled everything into a single blended view of spend, revenue, and margin.

The Wingman AI layer sat on top of that data to flag things the team wouldn't have caught by scrolling through raw numbers: underperforming ad sets, sudden drops in a specific SKU's conversion rate, spend creeping up on a campaign without a matching lift in revenue. Instead of someone manually digging for anomalies, the system surfaced them.

Results: What Changed After Onboarding

The most immediate change was time. Even without an exact number confirmed, the shift from manual spreadsheet stitching to a live dashboard is typically the first thing brands notice.

The bigger shift was speed of reaction. With blended ROAS and SKU-level margin visible in one place, the team could catch an underperforming campaign or a slipping product line the same day instead of during a weekly review cycle days later. That's the difference between pausing a losing ad set on day one versus day five.

Why This Matters for Other Beauty and DTC Brands

Moira Beauty's setup isn't unusual. Beauty ecommerce brands routinely sell across Shopify and Amazon while running paid acquisition on two or three platforms at once. That combination is exactly where fragmented reporting shows up first: no single native dashboard from Shopify, Meta, or Amazon was built to blend the others in.

The same setup that worked here, Shopify plus ad platforms plus GA4 in one dashboard, applies directly to other DTC founders evaluating Trivas, particularly founders and CEOs who need a fast, accurate read on the business without waiting on someone to build a spreadsheet.

The alternative most brands try first is cobbling together point solutions: one tool for Shopify attribution, another for Amazon, a spreadsheet for the rest. Tools like Triple Whale or Polar can be strong at a single channel, but brands selling across multiple platforms often end up needing more than one tool, which just recreates the fragmentation problem in a different form. Honestly, that's the part most brands underestimate going in.

Getting Set Up Like Moira Beauty

Getting a similar setup running starts with connecting a Shopify store to Trivas, which pulls order, revenue, and product data automatically. From there, ad platform accounts (Meta, Google) and GA4 connect on top, so spend and funnel data sit next to storefront performance instead of living in separate tabs.

For a brand of similar size to Moira Beauty, onboarding typically takes from initial connection to a working set of daily dashboards. Most of that time is data syncing and dashboard configuration, not manual setup work on the brand's end.

If your team is still stitching together Shopify, ad platform, and GA4 numbers by hand every week, it's worth seeing what a single dashboard actually looks like for a brand your size. Browse more ecommerce brand case studies or start a trial to connect your own store and ad accounts.