The ecommerce metrics that actually move revenue

Two decompositions carry nearly all the useful work.

Within a period: Revenue = Sessions x Conversion rate x Average order value.

Across customers: Revenue = Customers x Orders per customer x Average order value.

The first tells you where to spend effort this quarter. The second tells you whether the business compounds or resets every month. Any metric that does not sit inside one of these identities is either a diagnostic for one of the terms or decoration.

Run the arithmetic before choosing a project. A store converting at a low rate with high traffic has a different problem from one converting well with thin traffic, and the same tactic will help one and waste a month for the other.

Note which cell is empty. Nothing in the list moves two levers at once reliably, which is why "do everything" programs usually move nothing.

Contribution margin per order, after every variable cost, is the metric that turns revenue into a decision.

Take the order value. Subtract cost of goods, payment processing, shipping and fulfillment actually incurred, packaging, and the expected cost of returns for that product. What remains is what the order contributes before you pay for the marketing or the overhead.

Two consequences follow immediately. First, a channel's acceptable cost per acquisition is derived from contribution margin, not from a ROAS target somebody chose last year. Second, product mix changes your blended economics without any marketing change at all. A campaign that shifts volume toward low-margin lines can raise revenue and lower profit, and a revenue dashboard will congratulate you for it.

That last row matters more than it looks. Lifetime value measured over "all time" always rises for older cohorts simply because they have had longer. Fixing the window is what makes cohorts comparable.

Average customer value across the whole database mixes customers acquired under different offers, from different channels, at different prices. It can rise while every recent cohort gets worse.

Build the simple version:

1. Group customers by acquisition month.
2. For each cohort, track cumulative contribution per customer at 30, 90, 180 and 365 days.
3. Plot the curves on the same axes.
4. Read the shape. Curves flattening earlier than they used to means retention is weakening. Curves starting lower means you are buying worse customers or discounting harder to acquire them.
5. Segment by acquisition channel and by first product once the base cohort view works.

This is the view that tells you whether a cheap acquisition channel is actually cheap. A channel with a low acquisition cost and a flat cohort curve is more expensive than it looks.

Keep it to one screen and the same screen every week.

1. Revenue, orders, average order value, units per order.
2. Sessions by channel, with brand and non-brand separated.
3. Conversion rate overall, plus add-to-cart and checkout completion.
4. New customers, new customer acquisition cost, marketing efficiency ratio.
5. Contribution margin for the week, and margin mix by top category.
6. Refunds and returns, flagged by product where above the normal range.
7. One line on what changed: a promotion, a stock-out, a site release, a channel switched on.

That last line is the one most teams skip, and it is the one that makes the history readable. Six months later nobody remembers that conversion rate dropped because a key product went out of stock.

• Averages hiding mix shifts. Conversion rate is flat, but a high-converting branded segment grew while everything else fell.
• Acquisition cost calculated on all orders. Repeat purchases make it look better every month while new-customer economics deteriorate.
• Target ROAS inherited from nowhere. If it was not derived from contribution margin, it is a guess with a decimal point.
• Double counting across channels. Each platform claims the same order. Summing platform-reported revenue produces a number larger than the till.
• Returns excluded. In categories with high return rates, gross revenue is close to fiction. Measure on net.
• Site analytics and store reports not reconciled. Currency, timezone and tax settings differ, so two sources disagree and nobody trusts either. Reconcile once, document the difference, pick one source for each metric.
• Daily decision-making on small samples. A store with modest daily order volume has conversion rate noise wider than most improvements. Review weekly and act on trend.

The metric set is the first week of work, not a reporting deliverable at the end. We establish the decomposition, reconcile store and analytics data, and derive the acquisition ceiling from contribution margin before proposing any growth plan, because the plan depends on which term in the identity has slack. That diagnostic feeds directly into ecommerce growth work, and where the constraint turns out to be the storefront rather than the demand, into Shopify and ecommerce build work. Agree what better means before the work starts.