Marketing metrics explained

Ecommerce KPIs: the twelve an online shop should track

The right KPIs for a shop depend on what it is trying to fix.

About the Shopify connector

A new store needs to know whether its marketing pays for itself; an established one, whether customers come back. The twelve below cover both, and most come from an orders export and an ad platform.

The twelve

1. Revenue. Money taken in the period, after discounts. From the orders export.
2. Orders. Orders in the period. From the orders export, test orders excluded.
3. Average order value. Revenue ÷ orders. AOV formula. From the same two figures.
4. Conversion rate. Orders ÷ sessions. Conversion rate formula. Orders from the shop, sessions from Google Analytics 4; keep the same pair every month.
5. Gross margin. (Revenue - cost of goods) ÷ revenue. Gross margin formula. Take the figures from your catalogue or your accounts.
6. Contribution margin. Gross margin after variable selling costs such as shipping and payment fees. Contribution margin formula. From the accounts.
7. Customer acquisition cost. All marketing spend ÷ new customers. CAC formula. Spend from every platform plus the fees behind it; new customers from the orders export, first orders only.
8. ROAS. Attributed revenue ÷ ad spend, per platform. ROAS formula. From each platform's own reporting, which is why two platforms' ROAS should never be added.
9. Marketing efficiency ratio. Total revenue ÷ total marketing spend. Marketing efficiency ratio. Revenue from the shop, spend from every platform; it catches what per-platform ROAS double counts.
10. Customer lifetime value. AOV × orders per customer × gross margin. Customer lifetime value formula. From the orders export, by customer ID.
11. Repeat purchase rate. Customers with two or more orders ÷ all customers, over a window. Retention rate formula. From the orders export by customer ID; cohort analysis is the proper version.
12. Email click-through rate. Clicks ÷ emails delivered. What counts as a good one. From the email platform.

CAC and LTV are the pair that decides whether the business works; the LTV to CAC ratio is the thirteenth figure, built from them.

Published benchmarks

The honest answer to "what is a good number" for any of these is that it depends on category and price point. The published figures below show how much, because two of them disagree with each other by a factor of five:

The two AOV figures are both medians from large panels of real stores, and they are $250 apart, because one panel is weighted to the stores that sell most on paid social and the other excludes any store with fewer than 250 orders. Neither is wrong. Neither is yours.

The average ecommerce conversion rate and average email open rate pages take the benchmark question further.

Why benchmarks mislead

A published conversion rate averages shops selling £8 phone cases with shops selling £800 sofas. A published AOV depends on the currency and category mix of whoever answered the survey. A published repeat rate depends on a window the headline rarely states. And every benchmark comes from shops still trading when the data was collected, so the worst numbers are missing from the average.

Find your own baseline

Pick a window long enough to be steady (a quarter for most of the list, a year for LTV and repeat rate) and compute each figure from your own data. With orders by day in a tab called Shopify, the order count is:

=SUM(INDEX('Shopify'!A:Z, 0, MATCH("Orders", 'Shopify'!1:1, 0)))

Divide by sessions from your GA4 tab over the same dates for conversion rate. Record the twelve in one row per month. After three months the row above is your baseline.

Keeping it current

A scheduled Fresh Sheets report can pull orders from Shopify into a tab of your spreadsheet, by day or one row per order, with the order count, net payment and the rest of the order totals; further reports can bring sessions from Google Analytics 4 and spend from Google Ads into tabs of their own. Each tab's contents are replaced on every refresh, so keep the KPI row on a separate tab and find every column by heading with INDEX and MATCH, as above. Cost of goods and the fees behind CAC are typed in by hand on that tab. The two customer KPIs, LTV and repeat purchase rate, need a customer ID against each order, which the Shopify report does not carry; those come from Shopify's own orders export, as the cohort analysis page explains.