Most Shopify stores under a few million dollars in revenue don't need a dedicated analyst, they need five numbers checked consistently every week: blended MER, the new-versus-returning customer revenue split, the AOV trend, contribution or gross margin per order, and repeat purchase rate. Consistency in checking them matters more than the sophistication of the dashboard.
I've spent a lot of time in Shopify merchants' analytics dashboards, and the most common problem I see isn't missing data. It's too much of it. Most stores under a few million dollars in revenue don't have, and don't need, a dedicated analyst. What they need is a short list of numbers worth checking every week, and permission to ignore the rest until they're bigger.
Before building Aeyora, I watched merchants do the same thing over and over: open four or five different dashboards, each reporting a slightly different number for what should be the same metric, and walk away more confused than when they started. The problem was never that the tools were bad. It was that nobody had told them which handful of numbers, out of the hundreds available, actually deserved their attention every week. That gap is the whole reason this post exists.
Every analytics tool, including ours, will happily show you dozens of charts. That's not the same as telling you what to do next. If you're running the store yourself, or with a small team, the goal isn't comprehensive coverage of every possible metric. It's a handful of numbers you actually look at every week and that would change a real decision if they moved.
1. Blended MER (marketing efficiency ratio). Total revenue divided by total ad spend across every channel, full stop. Not Meta's reported ROAS, not Google's, the blended number using your own Shopify revenue as the numerator. It's the one metric that doesn't care which platform's attribution is broken this month, because it's built entirely from numbers you actually control.
2. New versus returning customer revenue split. Not just customer count, revenue. A store where returning customers drive a growing share of revenue is building a real asset. A store where that share is flat or shrinking is renting its growth from ad spend, and that's worth knowing well before it becomes a cash flow problem.
3. Average order value, as a trend, not a snapshot. The single AOV number matters less than which direction it's moving and why. A slow decline usually means discounting creep or a shift toward lower-margin products, and it's much easier to catch as a trend line than as a number you check once a quarter.
4. Contribution margin, or at minimum gross margin, per order. Revenue growth that comes with shrinking margin isn't really growth, it's a more expensive version of the same business. If you can only track one profitability number, this is the one, since ROAS and revenue alone will both happily go up while your actual profit goes sideways or down.
5. Repeat purchase rate, or time to second order. How many first-time customers come back, and how quickly. This is usually the earliest, most honest signal of whether the product and post-purchase experience are actually working, well before lifetime value numbers have had time to mature.
Multi-touch attribution modeling, channel-level incrementality testing, and cohort-by-acquisition-channel LTV curves are all real, useful things. They're also expensive to do properly, in tooling and in the time it takes to actually interpret them. Below roughly a few million dollars in revenue, the five numbers above will surface almost every problem worth catching early. Save the deeper attribution science for when you have the ad spend, and the team, to act on what it tells you.
I'd add one more caution here: don't let a vendor's demo talk you into buying sophistication you can't yet use. A media-mix model is only valuable if someone on your team has the time and the budget authority to act on what it recommends every week. If that's not true yet for your store, the fanciest attribution platform in the world won't outperform five numbers checked consistently.
Check the same five numbers on the same day every week, in the same place, rather than jumping between whichever dashboard feels most reassuring that day. The specific tool matters far less than the consistency. A founder who glances at blended MER, the new-versus-returning split, AOV trend, margin, and repeat rate every Monday will catch problems earlier than one with a beautiful, comprehensive dashboard they only open once a month.
That's really the whole philosophy behind how we built Aeyora: not more charts, but the handful of numbers that actually change what you do next, in the order you'd realistically check them.
Five numbers cover most of what matters: blended marketing efficiency ratio (total revenue divided by total ad spend), the revenue split between new and returning customers, the average order value trend, contribution or gross margin per order, and repeat purchase rate. Checking these consistently each week matters more than tracking a large number of metrics occasionally.
Blended marketing efficiency ratio is total revenue divided by total ad spend across every channel, calculated from your own Shopify revenue rather than any single ad platform's reporting. It matters because it isn't affected by attribution gaps or tracking issues on any individual platform, since it's built entirely from numbers you control directly.
Usually not yet. Multi-touch attribution and cohort-level LTV modeling are genuinely useful, but they're expensive in tooling and time to interpret correctly. Below roughly a few million dollars in revenue, a small set of core metrics checked weekly typically catches the same problems earlier and more simply.
Weekly, on a consistent day, looking at the same core set of numbers each time. Consistency in when and what you check tends to surface problems earlier than occasionally reviewing a more comprehensive dashboard.