Ecommerce KPIs: The Metrics That Tell A DTC Brand What To Fix Next

A dashboard with thirty numbers on it doesn’t tell you what to do on Monday. Five ecommerce KPIs do, as long as you read them together: conversion rate, average order value, repeat purchase rate, contribution margin after acquisition cost, and lifetime value relative to acquisition cost. Each one has a formula, a place it lives in Shopify or GA4, and a decision it drives.

The first three come straight out of Shopify. The last two need your ad spend, which Shopify doesn’t have. That split shapes how you build the report and where your targets for next year should come from.

Revenue is sessions times conversion rate times average order value. If 50,000 sessions convert at 1.4% with a $108 average order, that’s 700 orders and $75,600. Those inputs are illustrative, but the structure isn’t: when revenue moves, one of those three moved, and the KPI that moved tells you where to look.

The other two ecommerce KPIs answer whether that revenue was worth buying. Repeat purchase rate tells you how much of next year’s revenue you won’t have to pay to acquire again. Contribution margin after acquisition cost and lifetime value against acquisition cost tell you how much you can afford to spend on ads to get a new customer.

Every other number on a dashboard explains one of these five. Add-to-cart rate explains conversion rate. Discounts explain average order value. Ad spend by channel explains acquisition cost.

That’s the test for which ecommerce metrics to track: keep the ones that explain a KPI you’ve set a target on, and drop the rest from the weekly report.

A target is only as good as the tracking under it. If your purchase events stopped firing, every GA4 number downstream is wrong, and nothing in the dashboard says so. On August 26, 2026, scripts in the Additional Scripts field stopped running on the Thank you and Order status pages of non-Plus Shopify stores, which is the case our post on broken Shopify conversion tracking walks through.

Shopify Analytics’ main features (opens in new tab) are available on every Shopify plan, so the in-platform numbers are there for any store. GA4 is the one that needs work: the purchase event has to fire once per order, with the right value. Our GA4 setup and reporting service starts with an audit that maps your conversion points against what GA4 is tracking, then fixes the gaps before anyone builds a dashboard on them.

Shopify and GA4 will never agree exactly, and neither one is wrong. They measure different things under similar names.

GA4’s revenue comes from the value parameter on the purchase event (opens in new tab), which is the sum of price times quantity for the items in the order, with shipping and tax left out. Purchase revenue in GA4 (opens in new tab) is that revenue minus any refunds, and refunds arrive as a separate refund event (opens in new tab) tied to the transaction ID. Shopify’s average order value (opens in new tab) is gross sales minus discounts, divided by orders, measured before any post-order adjustments. One is net of refunds and the other isn’t.

Conversion rate splits the same way. In Shopify it’s the share of online store sessions that completed checkout. GA4’s closest metric, session key event rate, is the share of sessions with a key event, and a key event (opens in new tab) is whatever action you’ve marked as important to the business. If you’ve marked more than purchases, GA4’s rate isn’t a purchase rate.

Which one should you set targets in? Pick one source per KPI and write it next to the target. Shopify is the better source for orders, sales and customers, because it’s the system that took the money. GA4 is the better source for anything by channel or campaign.

On September 21, 2026, Shopify changed how it counts sessions (opens in new tab). The default sessions view now focuses on human visitors where the human or bot filter is available, classifies bot and system traffic more consistently, and keeps real shopper visits visible even when there’s no standard pageview.

That change moves sessions, conversion rate, add-to-cart rate, reached checkout rate, checkout conversion rate and bounce rate. Orders, sales and customer counts aren’t affected. A target written as “beat last year’s conversion rate” compares a denominator counted one way with a denominator counted another, so set conversion rate targets off the months after the change, and keep year-over-year comparisons for orders and sales.

Shopify defines conversion rate (opens in new tab) as sessions that completed checkout divided by sessions. The unit is a session, not a visitor and not an order. A visitor can have more than one session, and two orders in one session count once.

The same schema breaks that rate into three steps. Added to cart rate is sessions with a cart addition divided by sessions. Reached checkout rate is sessions that reached checkout divided by sessions. Checkout conversion rate is sessions that completed checkout divided by sessions that reached checkout.

Those steps are what make conversion rate useful. A low overall rate with a healthy checkout conversion rate points at product pages and pricing, not checkout. A good add-to-cart rate with a weak reached checkout rate points at the cart: shipping costs shown too late, a surprise at the threshold, a broken button on mobile. Conversion funnel analysis maps each of those steps, puts a dollar figure on each drop-off and ranks the fixes.

What should you do when the leak is found? Fix it, and test the changes that are big enough to test. Our guide to conversion rate optimization covers how to run that as a program, including how much traffic a test needs before its result means anything.

Shopify’s average order value (opens in new tab) is gross sales minus discounts, divided by orders. Gross sales excludes taxes, shipping, duties and fees. Shopify moved to this definition on January 9, 2023 (opens in new tab), replacing one built on an order’s total sales value, and the change applies to historical data in the admin too.

That means Shopify’s average order value won’t match one you compute from total sales in a spreadsheet, and it won’t match GA4’s average purchase revenue, which is net of refunds. Pick one and say which.

On the revenue math, average order value and conversion rate carry equal weight. Lift either one by 10% on the illustrative store above and revenue goes from $75,600 to $83,160.

The difference is what each lift costs. A conversion rate lift from a faster product page costs nothing per order. An average order value lift from a discount or a free shipping threshold can raise revenue while lowering what you keep, which is why our guide to raising average order value judges every tactic on contribution per session, not order value alone.

Shopify has two repeat numbers, and they answer different questions. Its returning customer rate (opens in new tab) is returning customers divided by customers, where a returning customer is one in your results who’d bought from you before. That’s a share of the customers who ordered in the period you selected.

The Returning customers report (opens in new tab) counts something else: every customer whose order history includes two or more orders. That’s a lifetime count, not a period rate. When someone on your team quotes a repeat purchase rate, ask which one they mean, because a target set on one can’t be checked against the other.

The same set of customer reports includes a customer cohort analysis, which groups customers by the date of their first order and shows a selected metric over the weeks, months or quarters after it. For setting targets, a cohort view is the more honest of the Shopify KPIs on repeat buying, because it holds the starting group fixed instead of mixing this year’s new buyers with five years of past ones.

GA4’s returning users metric isn’t a substitute. It counts users who’ve had at least one previous session (opens in new tab), which makes it a returning visitor, not a returning customer.

Repeat rate tells you how much of your revenue doesn’t need new ad spend. Our guide to retention marketing covers why young or small cohorts produce noisy repeat rates, and why the shortcut of dividing one by the churn rate overstates lifetime value.

This is the KPI that tells you whether growth is paying for itself, and it’s the one Shopify can’t produce on its own. Contribution margin after marketing is sales revenue minus variable costs minus marketing expense. Variable costs are the ones that rise with each order: the product, shipping, payment fees.

Shopify gets you most of the way. Gross profit (opens in new tab) is net sales minus cost of goods sold, and gross margin is gross profit divided by net sales, based only on sales where cost of goods sold is recorded. Profit reports (opens in new tab) need a cost per item on your products, so a catalog with missing costs reports a margin for only the part of your sales that has costs recorded.

Shopify also estimates profit per order (opens in new tab), subtracting cost of goods, shipping costs, duties and import taxes, and payment processing and international fees from revenue. That estimate excludes marketing costs, packaging costs and all sales reversals. So Shopify will take you to profit after fulfillment, and the ad spend has to come in from Meta, Google and the rest, or from your finance sheet.

Here’s how it works on an illustrative first order. Take a $108 order with product costs at 40% of the order: $43.20 in cost of goods leaves $64.80 in gross profit. Subtract $12 for shipping and payment fees and you’re at $52.80. If it cost $45 in ads to win that customer, the first order contributes $7.80.

That $7.80 is the number that decides how hard you can push acquisition. If it’s positive, each new customer pays for themselves on the first order. If it’s negative, you’re betting on the second order, and repeat purchase rate stops being a nice-to-have.

Customer acquisition cost is sales and marketing costs divided by the number of new customers those activities brought in. Lifetime value is the average sale value times the average number of repeat transactions times the average retention period in years, and it can be measured as revenue or as profit.

That choice changes the answer more than any other input. Say the customer in the example above places 1.8 orders over their life with you. On revenue, lifetime value is $194.40, which is 4.32 times the $45 acquisition cost.

On contribution after fulfillment, it’s $95.04, which is 2.11 times. Same customer, same ad cost. Set your LTV target on margin, because revenue doesn’t pay for the ads.

Is 3:1 the right LTV to CAC ratio? It’s a SaaS rule of thumb, not an ecommerce benchmark. David Skok, a venture capitalist writing about subscription software metrics (opens in new tab), puts the best SaaS businesses above 3 and calls it a guideline with exceptions. The Business Development Bank of Canada calls 2.5 to 3 times acquisition cost a very general guideline for business. On the margin version above, a 3:1 target caps acquisition cost at $31.68 and 2.5:1 caps it at $38.02, both below the $45 the example store is paying.

Skok also treats a payback period longer than 12 months as a sign of weak profitability. For a store, payback is easier to read in orders: at $52.80 of contribution per order, a $45 acquisition cost is recovered in 0.85 of an order, which is inside the first purchase.

Acquisition cost from ad platforms comes with a catch. Meta and Google report the sales they took credit for, which isn’t the same as the sales the ads caused. If your CAC is built on platform-reported conversions, incrementality testing is how you find out how much of that credit is real.

MER, marketing efficiency ratio, is total revenue divided by total marketing spend. On the illustrative store, $75,600 in revenue on $15,000 of spend is an MER of 5.04. It ignores which channel gets credit for which sale, which is both its weakness and its use: it can’t be inflated by two platforms claiming the same order.

Write the definition into your report so everyone reads the ratio the same way round. MER tells you whether total spend is in line with total revenue. It doesn’t tell you which channel to cut.

Benchmarks are useful for one thing: telling you whether a number is in a normal range or far outside it. They’re not targets, because every published benchmark comes from a specific set of stores, measured a specific way.

Two vendors publish conversion rate benchmarks from their own Shopify customers with the sample and method stated. Shogun, which sells a Shopify page builder, reports a median conversion rate of 1.74% and a mean of 2.54% (opens in new tab) across 803 active Shopify stores from January through September 2026, with conversion rate calculated as orders divided by sessions. Littledata, which sells Shopify-to-GA4 tracking, reports a median of 1.4% (opens in new tab)) across 427 Shopify stores sending data to GA4, over the 90 days from July 13 to October 10, 2026, with the top 10% at 3.6% or more.

Littledata’s figure for average order value (opens in new tab) over the same 427 stores and window is a median of $108, with the bottom 10% at $46 and the top 10% at $612. Its order value is GA4 purchase revenue divided by orders, so it’s net of refunds.

Don’t average the two conversion figures. They use different data sources, windows and inclusion rules, and Shogun’s 0.8-point gap between median and mean shows how much a few high converters pull an average up. Compare your rate to the median, in the same tool the benchmark used.

Set your repeat purchase rate target against your own cohorts.

Start from your own history, measured consistently. For conversion rate that means the months after September 21, 2026. For order value, repeat rate and sales, it means the same definition across both years.

Then set targets in pairs, so one KPI can’t be hit by breaking another. An order value target goes with a contribution margin target. A new-customer target goes with an acquisition cost ceiling. A conversion rate target goes with the funnel step you plan to fix.

Write the definition and the source next to each target: “conversion rate, Shopify, sessions that completed checkout divided by sessions.”

Getting there takes clean tracking under both systems and a report that puts ad spend next to Shopify’s profit numbers. That’s the work behind our GA4 setup and reporting service: an audit of what GA4 is tracking against what your store needs, the configuration to close the gaps, and dashboards your team can set targets from.