.d53b12e5.png&w=3840&q=75 "Trivas.ai")
by Trivas.aiHere's how it usually goes. Someone on the team reads a blog post listing "25 ecommerce metrics to track," copies the list into a spreadsheet, and calls it a dashboard. Half those metrics have nothing to do with how the business actually makes money. The other half get abandoned within a month because nobody tied them to a real decision.
Then there's the mechanical problem. Someone exports GA4 sessions, pulls Shopify order data, downloads a Meta ads report, and manually stitches them into one tab. It works for about a week. Then a column shifts, a date range doesn't match, and the whole thing quietly goes stale while everyone keeps reporting numbers nobody's checked.
An ecommerce KPI dashboard template fixes the first problem, at least. It gives you the 15 to 20 metrics that actually move revenue, organized so you're not guessing what to track or why. It won't fix the manual-refresh problem on its own, but we'll get to that.
This post includes a downloadable template structure, plus guidance on which numbers matter depending on what stage your revenue is at. A brand doing $500K a year and a brand doing $20M don't need the same dashboard. We'll tell you which rows to fill in first.
A usable template has four tabs: revenue and sales, marketing and acquisition, retention and LTV, and inventory and ops. Most teams only build the first two and wonder later why margins quietly eroded while top-line revenue looked fine.
Every KPI on every tab needs three things: a formula, a named data source, and a target benchmark. A number sitting alone, with no formula behind it and no benchmark to measure against, isn't a KPI. It's trivia.
Pageviews, impressions, and email open rates feel good to report. They rarely change what you do next week. Contribution margin, CAC payback period, and repeat purchase rate are decision metrics: when they move, you actually act. The template should be weighted toward the second group, not the first.
Build the review cadence into the structure itself. Weekly: revenue, conversion rate, CAC by channel. Monthly: margin, LTV:CAC, inventory sell-through. Checking everything weekly creates noise. Checking margin only once a quarter means you find out about a problem three months too late.
Start with net revenue, not gross. Gross revenue minus refunds, discounts, and chargebacks is the number that actually hits your bank account, and it's often 10 to 15% lower than what the storefront reports.
Break AOV out by new versus returning customers. Blending them hides whether your acquisition channel is bringing in bargain hunters or your best customers are quietly spending less over time.
Track gross margin and contribution margin per order, not just revenue. A $90 order with free shipping and a 20% discount code can lose money. If your dashboard only shows revenue, you won't catch that until the P&L does.
Finally, conversion rate by channel: paid, organic, email, direct. A single blended conversion rate across all traffic sources papers over the fact that your paid traffic might be converting at half the rate of email.
Blended CAC first, then CAC by channel (Meta, Google, TikTok). Platform-reported numbers almost always look better than reality because each platform claims credit for the same conversion.
That's why MER (marketing efficiency ratio) belongs on every dashboard: total revenue divided by total ad spend. It's a sanity check against inflated platform ROAS. If Meta says 4x ROAS but your MER is sitting at 1.8x, something in the attribution is lying to you.
New customer ROAS and blended ROAS need separate rows too. Blending them hides a channel that's great at acquisition but terrible at reselling to existing buyers, or the reverse: a channel that looks efficient only because it's remarketing to people who were going to buy anyway.
If you want to check a single channel's numbers before building out the full dashboard, the ROAS calculator is a fast way to sanity-check platform-reported figures against your actual spend.
Repeat purchase rate at 30, 60, and 90 days tells you more about long-term health than almost anything else on this list. Track it as a trend line, not a single snapshot.
LTV:CAC ratio belongs here too, but most spreadsheet versions calculate LTV wrong. They take average order value times average order count and call it a day. That ignores cohort behavior entirely. A customer acquired in January and a customer acquired in September don't repeat-purchase the same way, especially for seasonal products. Cohort-based LTV, tracking actual repeat behavior by acquisition month, is more work to build but it's the only version that's honest.
Email and SMS-attributed revenue as a percent of total revenue is worth its own row. If it's under 15%, you're over-reliant on paid acquisition and probably underusing the list you already have.
For subscription or replenishment products, add churn rate or reorder gap: the average time between a customer's expected reorder date and when they actually reorder (or don't). This is the earliest warning sign of a product or fulfillment problem, long before refund rates spike.
Sell-through rate and days of inventory remaining go on every dashboard that sells physical goods. Without them, you're either sitting on dead stock or scrambling to reorder too late.
Stockout rate on your top 20% of SKUs by revenue matters more than overall stockout rate. Running out of a product that drives 2% of revenue is an annoyance. Running out of your best seller during a paid campaign is a five-figure mistake.
Fulfillment cost as a percent of revenue rounds out the tab. Shipping costs creep up slowly enough that nobody notices until margin has eroded by several points.
Here's the uncomfortable truth: most marketing-built dashboards skip this entire tab. Marketing teams build what they understand, which is ad metrics. Meanwhile a stockout on a hero SKU or a fulfillment cost creep can cost more in a quarter than a CAC problem ever would. If operations isn't getting a seat on the dashboard, it's the biggest blind spot in the whole thing.
The template works right up until someone has to maintain it by hand. Manual exports from Shopify, Amazon Seller Central, and whichever ad platforms you're running drift out of sync within a week, sometimes days. Someone forgets to pull Tuesday's numbers, a currency field gets copied wrong, and now the dashboard is quietly lying to the team that trusts it.
It gets worse for multi-channel sellers. Reconciling Amazon fees, Shopify discounts, and retail returns by hand, across different currencies and different refund logic, isn't a spreadsheet problem anymore. It's a full-time job nobody signed up for.
This is the point where a lot of teams stop updating the file and just eyeball platform dashboards instead, which brings back the exact attribution problems MER was supposed to catch.
Trivas's dashboards pull these same KPI categories, revenue, marketing, retention, inventory, automatically from Shopify, Amazon, Meta, Google, and GA4 into one Redshift-backed view. No manual refresh, no reconciling currencies by hand. If you're running on both Shopify and Amazon, that reconciliation layer is usually the first thing that breaks in a spreadsheet, and the first thing that stops breaking once it's automated. It's not a replacement for understanding your own metrics. It's what you move to once the template's done its job and the manual upkeep starts costing more time than it saves.
Four tabs, four categories: revenue and sales, marketing and acquisition, retention and LTV, inventory and ops. Weekly check on the numbers that move fast, monthly check on the ones that move slow. That's the whole system.
Download the ecommerce KPI dashboard template and start filling in your own numbers this week. If you want benchmark context for any single metric, the guides and reports library has deeper breakdowns on what "good" looks like at different revenue stages.
And if you'd rather skip the manual upkeep entirely, you can start a free trial and see the same KPI categories live, pulled automatically, no spreadsheet required.
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