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by Trivas.aiIf you landed here expecting one big success story, that's not what this page is. Trivas.ai case studies live at /resources/case-studies, and it's a library, not a single narrative. Different brands, different platforms, different problems.
The brands in there span CPG, fashion and retail, electronics, and beauty. Some sell only on Amazon. Some run Shopify-first DTC operations. A lot of them do both, plus a handful of secondary marketplaces on top.
Here's the thread that ties all of them together: every single one was drowning in scattered data before Trivas. Amazon Seller Central in one tab, Shopify admin in another, ad platforms in a third. This page exists to help you skip the scrolling and find the one or two case studies that actually look like your business.
The setup was almost always the same, regardless of industry. Someone on the team, usually a marketing lead or an ops person, pulled exports from Amazon Seller Central, Shopify admin, and Meta or Google Ads separately. Then they stitched it together by hand in a spreadsheet.
No single source of truth existed for margin, ROAS, or inventory across channels. A brand could be profitable on Shopify and bleeding money on Amazon Ads, and nobody would know for a week because the numbers lived in different systems that didn't talk to each other.
Forecasting was worse. Demand planning ran off spreadsheets with static inputs, updated whenever someone remembered to refresh them, not off live sales data. That's a real problem when you're managing seasonal SKUs or promotional spikes.
Add it up and most of these teams were spending several hours a week just reconciling exports. Not analyzing anything, just getting the numbers into a shape where analysis was even possible. That's dead time that should've gone toward actual decisions.
Large CPG accounts share a pattern: high SKU counts, multiple markets, and a presence across several retailers plus Amazon. That combination breaks spreadsheet-based reporting fast. You're not comparing ten SKUs in one country, you're comparing hundreds across a dozen.
These brands lean on Redshift-backed dashboards to compare performance across regions and retailers without rebuilding a report every time someone asks "how are we doing in Germany versus the UK this month." The underlying data warehouse does the heavy lifting so the dashboard layer stays fast even as SKU and market counts grow.
For the specific breakdowns of how this plays out, Royal Canin and Nestle both have dedicated writeups covering their setups in more detail. Worth a look if you're managing a multi-market CPG portfolio and recognize the SKU-sprawl problem.
Fashion and home goods brands live and die by seasonal demand swings. A miss on forecasting in this category doesn't just cost a few sales, it means markdowns on unsold inventory or stockouts during the exact weeks that matter most.
That's why forecasting accuracy carries more weight here than in almost any other vertical. These brands use the Wingman AI layer to flag SKU-level anomalies before they turn into a stockout or a pile of overstock sitting in a warehouse. Catching a demand shift two weeks early is worth a lot more than catching it after the shelf is empty.
This group also tends to pull GA4 funnel data into the mix alongside Amazon and Shopify performance. Makes sense: fashion and home goods purchases often involve more browsing and consideration than a repeat CPG buy, so understanding the funnel matters more for these brands than it does for, say, a pet food subscription.
Electronics sellers face a different problem: thin margins. When your margin on a unit is already tight, ad efficiency isn't a nice-to-have metric, it's the difference between a profitable SKU and a loss leader you didn't mean to run.
These accounts consolidate Amazon Ads and Meta or Google Ads spend against actual product margin, not just top-line revenue. That distinction matters more than it sounds. A campaign can post a great ROAS on revenue and still be quietly unprofitable once you account for the real margin on what's selling.
This vertical is also where multi-marketplace complexity shows up most. Electronics and specialty retail sellers often aren't just on Amazon and Shopify, they're on Walmart, Best Buy, or Target too. If your ads and inventory data live across five platforms instead of two, the reconciliation problem from earlier in this article gets multiplied, not just added to.
Strip away the industry differences and the same architecture shows up in every one of these accounts. None of them are running some custom one-off build for their specific brand. They're all on the same BI reporting layer, built on Amazon Redshift, that scales whether you've got 50 SKUs or 5,000.
They also all use the Wingman insights layer for anomaly detection and plain-language reporting. That's the piece that cuts manual analysis time, because instead of someone staring at a dashboard trying to spot what changed, Wingman surfaces it directly: this SKU's conversion dropped, this campaign's efficiency shifted, here's why.
Forecasting and simulation show up again and again too, particularly for brands managing seasonal or promotional demand, which is most of the fashion, home goods, and even some CPG names on this list.
The honest takeaway: the differentiator between these brands isn't the tool they're using, it's how they've pointed the same tool at their specific problem. That's actually good news if you're evaluating Trivas, because it means the case study library is less "look how special this brand is" and more "here's a repeatable pattern you can map onto your own setup."
Don't try to read all of them. Use a simple filter instead.
First, match by platform mix. Are you Amazon-only, Shopify-only, or running both? That narrows the list fast, since the reporting challenges (and the fixes) look different depending on which channels you're actually reconciling.
Second, match by industry vertical. A CPG brand managing hundreds of SKUs across multiple retailers has a different problem than a fashion brand fighting seasonal demand swings, even if both are technically "ecommerce."
The full, current list lives at /resources/case-studies, and it's worth checking directly since new stories get added over time. Anything written here is a snapshot, not the complete picture.
One more thing: if you're an agency managing multiple brands rather than running one of your own, don't force-fit yourself into a single vertical. Look at the agency-specific patterns instead. Managing five client accounts across different platforms is its own use case, and it's worth treating it that way rather than picking whichever brand story feels closest.
Reading case studies only gets you so far. At some point it's more useful to talk through your actual setup than to keep pattern-matching against someone else's.
If you want to figure out which case study pattern actually fits your business, talk to a founder directly. No deck, just a conversation about your platforms, your SKU count, and where your reporting is breaking down right now.
Prefer to poke around first? Start a trial and look at the dashboards yourself before you commit to anything. And if you just want more of this kind of breakdown as new brands get added, keep an eye on the case studies library, it's the fastest way to see what's actually changed.
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