Ecommerce KPIs are the handful of metrics an online store picks to judge whether it is on track: usually conversion rate, average order value, revenue, gross margin, customer acquisition cost, repeat purchase rate and return rate. A metric becomes a KPI when someone owns it, it has a target, and a change in it triggers a decision.
The short version: revenue is sessions x conversion rate x average order value, and profit is what is left after product cost, shipping, payment fees, returns and acquisition cost. Track one or two KPIs for each of those levers, review them on a fixed rhythm, and add the product data KPIs most lists leave out, because a product with no image, no size and no category converts worse no matter how much traffic you buy. Below are 18 KPIs with formulas, a worked example with real numbers and a scorecard you can copy.
Three tests that turn a metric into a KPI
Your analytics tool reports hundreds of metrics: bounce rate, pages per session, scroll depth, items viewed. A KPI is a metric you have decided to steer on. A useful test is to ask three things of each candidate:
- Does it connect to revenue or profit within one or two steps?
- Can a specific person or team move it?
- Would a 10% change make you do something different next week?
If the answer to any of these is no, keep it as a diagnostic metric you check when a KPI moves, not a number on the weekly dashboard. A store with 40 KPIs effectively has none.
The ecommerce KPI tree
Most ecommerce KPIs are parts of one equation. Seeing them as a tree tells you which number to look at when revenue drops.
- Revenue = sessions x conversion rate x average order value
- Gross profit = revenue - cost of goods sold
- Contribution per order = average order value - product cost - shipping - payment fees - expected returns
- Profit from a new customer = contribution over the customer's lifetime - customer acquisition cost
The ecommerce KPI tree
When revenue falls, walk down the tree. Did sessions fall (traffic and acquisition), did conversion fall (site, pricing, product pages, stock), or did order value fall (mix, discounts, bundles)? Each branch has its own KPIs and its own owner.
18 ecommerce KPIs, with formulas
Traffic and acquisition
- Sessions by channel. Visits split into organic search, paid search, paid social, email, direct and referral. Watch the mix, not only the total: a store that depends on one paid channel is exposed to that channel's prices.
- Organic search clicks. Clicks from Google Search Console, by page type (category, product, content). It measures unpaid demand from Google directly, independent of your analytics tag and consent banner, so it is a useful cross-check on session data.
- Customer acquisition cost (CAC). Marketing spend / new customers acquired in the same period. Use new customers, not orders, or repeat buyers will make paid channels look cheaper than they are.
- Return on ad spend (ROAS). Revenue attributed to ads / ad spend. A ROAS of 4 means $4 of revenue per $1 spent. It ignores margin, so read it next to contribution per order.
Conversion
- Conversion rate. Orders / sessions x 100. Shopify defines its online store conversion rate as "the percentage of sessions that resulted in a purchase" (Shopify Help Center). Segment it by device and by traffic source before drawing conclusions.
- Add-to-cart rate. Sessions with an add to cart / sessions. Low add-to-cart with high product page views points at the product page: price, images, missing information, stock.
- Cart abandonment rate. 1 - (completed orders / carts created). Baymard Institute puts the documented average at 70.22%, calculated from 50 studies (Baymard Institute). Use that as context, not a target; your own trend matters more.
- Revenue per session. Revenue / sessions. It combines conversion and order value in one number, which makes it the fairest way to compare channels or landing pages.
Order value and profit
- Net revenue. Gross sales - discounts - refunds, excluding VAT or sales tax. GA4's purchase revenue is defined as revenue from purchases "minus any refunds given" (Google Analytics Help), but only if your refunds are actually sent to GA4.
- Average order value (AOV). Net revenue / number of orders. Our average order value calculator does the math and shows what a change is worth.
- Gross margin. (Net revenue - cost of goods sold) / net revenue x 100. Track it per category; a sales mix shifting toward low-margin categories can hide behind rising revenue.
- Contribution margin per order. AOV - product cost - shipping - payment fees - expected return cost. This is the number that tells you how much you can afford to spend to win an order.
Customers and retention
- Repeat purchase rate. Customers with two or more orders / all customers, over a fixed window such as 12 months.
- Customer lifetime value (CLV). Contribution per order x expected orders per customer. Use contribution, not revenue, or CLV will look far better than it is (2.5 times better in the worked example below). The customer lifetime value calculator works through the inputs.
- CLV to CAC ratio. CLV / CAC. Below 1 you lose money on every new customer; how far above 1 you need to be depends on how fast you need the cash back.
Products and operations
- Return rate. Units returned / units sold x 100, per product and per category. A single product with three times the category's return rate usually has a product data problem: wrong size information, misleading photos, a missing material. The return rate calculator shows what returns cost you.
- Stockout rate and lost sales. Share of active SKUs at zero stock, and the estimated revenue missed while they were out. Revenue reports never show sales that did not happen; the stockout loss calculator gives a first estimate.
- Product data completeness. Share of active products with every required field filled: title, description, at least one image, category, brand, GTIN and the attributes your filters and channels need. See product data quality metrics for how to define the required fields.
Add-to-cart, returns, organic clicks: where the catalog shows up
Most KPI lists stop at marketing and sales. But several of the KPIs above are driven by the catalog: add-to-cart rate, return rate, organic clicks and whether a product can be listed on a channel at all. A product with no image or a two-line description converts worse, and a product that sits in no category is hard to find on site and in Google Shopping.
Shoppers notice. In Syndigo's 2025 State of Product Experience report, a vendor survey, 44% of shoppers said they had abandoned a purchase because of insufficient product information. Baymard Institute found that 56% of users start their product page visit by exploring the images. A store that tracks conversion rate but not catalog completeness is measuring the result and ignoring one of the causes.
Three product data KPIs are worth a line on the dashboard:
- Completeness rate: share of active products with all required fields filled.
- Average quality score: a 0 to 100 score per product that weights fields by importance, tracked as a trend.
- Channel-ready rate: share of products that meet a channel's requirements (Google Merchant Center, a marketplace) and can actually be listed.
In WISEPIM, Quality Guard scores every product and the analytics side ranks products by sales and quality together, so you can see which bestsellers are held back by weak listings and which slow sellers have a problem the data does not explain.
May in numbers: a home and kitchen store on Shopify
A home and kitchen store on Shopify has these numbers for May:
- Sessions: 40,000
- Conversion rate: 1.8%, so 720 orders
- AOV: $62, so net revenue of $44,640
- Per order: product cost $27, shipping $5.50, payment fees $1.90, expected return cost $3.10
- Contribution per order: 62 - 27 - 5.50 - 1.90 - 3.10 = $24.50
- CAC: $18 for a new customer
- Orders per customer over 12 months: 1.6
So a new customer earns $24.50 on the first order, minus $18 to acquire: $6.50. Over a year, CLV on a contribution basis is 24.50 x 1.6 = $39.20, and CLV to CAC is about 2.2.
Now compare three levers, each a modest change:
- Conversion rate from 1.8% to 2.0%: 800 orders instead of 720, so 80 extra orders x $24.50 = $1,960 more contribution, with no extra ad spend.
- AOV from $62 to $66: if the extra $4 carries the same 39.5% contribution share as the rest of the order (24.50 / 62), contribution per order rises by about $1.58, or about $1,140 across 720 orders.
- Return cost from $3.10 to $2.10 per order: $1 x 720 = $720, plus fewer unhappy customers.
Extra monthly contribution per lever
Illustrative example| Category | Extra contribution |
|---|---|
| Conversion 1.8% to 2.0% | $1,960 |
| AOV $62 to $66 | $1,140 |
| Return cost $3.10 to $2.10 | $720 |
The numbers make the priority obvious for this store: conversion first. The next question is why 98.2% of sessions do not buy, which is where add-to-cart rate, page-level conversion and product data completeness come in. Plug your own numbers into the conversion rate calculator to run the same comparison.
A starter scorecard with owners and action triggers
Use one line per KPI with an owner, a source, a review rhythm and a trigger that forces action. A starter scorecard for a store doing between a few hundred and a few thousand orders a month:
Starter KPI scorecard
| Net revenue | Founder or ecommerce manager | Shop platform | Weekly | 10% below the same week last year two weeks in a row |
|---|---|---|---|---|
| Conversion rate | Ecommerce manager | Shop platform | Weekly, by device | Mobile drops 0.2 points |
| AOV | Ecommerce manager | Shop platform | Weekly | Falls during a period without a promotion |
| Contribution per order | Finance | Shop platform plus cost sheet | Monthly | Below target for a month |
| CAC and ROAS by channel | Marketing | Ad platforms plus shop platform | Weekly | CAC above first-order contribution |
| Repeat purchase rate | CRM or email | Shop platform | Monthly | Falls two months in a row |
| Return rate by category | Customer service and catalog | Returns data | Monthly | Any product at twice its category average |
| Product data completeness | Catalog or content manager | PIM | Weekly | New imports below the catalog average |
| Organic clicks by page type | SEO | Search Console | Monthly | Product pages fall while categories hold |
Smaller stores can start with five: revenue, conversion rate, AOV, contribution per order and CAC. Add the rest as the team grows and each KPI gets a real owner.
Reporting traps that make KPIs mislead
- Comparing conversion rates across tools. Shopify, GA4 and your ad platforms count sessions and purchases differently, and consent banners hide part of the traffic from GA4. Pick one source per KPI.
- Revenue without refunds or VAT handled consistently. Net revenue should exclude tax and subtract refunds, every time.
- ROAS without margin. A ROAS of 5 on a 20% margin product loses money once shipping and returns are paid.
- Store-wide averages only. A flat conversion rate can hide mobile falling and desktop rising. Segment by device, channel and category.
- Reporting on everything, deciding on nothing. If no one acts when a KPI moves, drop it from the weekly review.
- Ignoring the catalog. Conversion and return problems often start with missing sizes, a single blurry photo or a description that contradicts the image.
If you want sales and product data in one place, WISEPIM analytics combines store orders, Google Analytics 4 and Search Console data with quality scores per product, so a drop in conversion can be traced to the listings behind it. The free plan covers up to 100 products.
