Store Performance Metrics for Shopify: What to Track Beyond Revenue

Shopify's own field reference gives a formula for each of the core store rates: conversion rate is sessions that completed checkout divided by sessions, average order value is gross sales minus discounts divided by orders, and returning customer rate is returning customers divided by customers. Published benchmarks for these rates disagree or come without a stated method, so the comparison you can verify is your own store against its own history, with the same filters and date ranges.

Which metrics does Shopify itself treat as essential?

Shopify's field reference says its most helpful fields cover the essential metrics most merchants need for day-to-day analytics. Beyond revenue, these describe the store:

• Net sales: gross sales minus discounts and sales reversals, which Shopify calls the best approximation of actual revenue.
• Average order value: (gross sales - discounts) / orders.
• Sessions: online store visits, counted only when visitors consent to cookies through the cookie banner.
• Conversion rate: sessions that completed checkout / sessions.
• Added to cart rate and reached checkout rate: sessions with cart additions / sessions, and sessions that reached checkout / sessions.
• Bounce rate: bounces / sessions, where a bounce is a visit that ended after a single pageview.
• Returning customer rate: returning customers / customers.
• Speed: Shopify's web performance reports rank the three Core Web Vitals: Largest Contentful Paint is Good at 2500ms or less, Interaction to Next Paint at 200ms or less, and Cumulative Layout Shift at 0.1 or less.

Shopify notes that other analytics platforms calculate sessions differently. Checkouts can exceed add-to-cart counts when customers buy directly from channels such as Instagram Shopping or Facebook Shops without visiting the cart page.

Why can conversion rate change when the store did not?

Conversion rate is calculated from sessions, and how sessions are measured can change. Shopify's session measurement rollout ran from September 21 to 23, 2026. Shopify says sessions, conversion rate and other session-based metrics might differ afterwards without any change in traffic, orders or customer behavior, and advises treating the update as a new baseline. Orders, sales and customer counts are not affected. Identified bot sessions are filtered out of session reports by default, and a session ends after 30 minutes of inactivity.

GA4 uses some of the same names for different quantities. In Google's definitions, bounce rate is the percentage of sessions that were not engaged, where an engaged session lasted longer than 10 seconds, had a key event or had 2 or more screen views; Shopify's bounce rate counts visits that ended after a single pageview. GA4's session key event rate counts sessions with any key event, not only purchases, so it is not Shopify's conversion rate.

Is there a good conversion rate or cart abandonment rate to aim for?

Published figures disagree. Shopify's blog cites two global figures: 1.4% of visits converted in the first quarter of 2026 from one benchmark provider, and a 2.66% average from another. It says such numbers give a rough baseline rather than a basis for performance targets. The same page lists June category averages from 0.63% for luxury and jewelry to 5.7% for pet care and vet services, without naming a dataset or method. Treat these as figures published by Shopify, not independently audited.

Cart abandonment shows the same spread. Baymard Institute averages 50 published studies to 70.22%, and the individual studies on its page range from 55.00% to 84.27%. In its own quantitative study, Baymard found that 42% of US online shoppers have abandoned a cart because they were just browsing or not ready to buy, and it calls abandonment from browsing, price comparison and saving items for later largely unavoidable.

In its field reference, Shopify prints one range for returning customer rate: most stores display 20-40%, without saying how that was measured. Its targets documentation points the same way: you can set a target period up to 2 years in the past to help you determine a reasonable goal based on your store's historical data.

What do retention and acquisition cost show?

Shopify defines a returning customer as one whose order history already includes at least one order, and its customer reports use the entire order history, so a customer acquired in November still shows as a repeat customer if the second order lands in December. The Customer cohort analysis report groups customers by the date of their first order and shows retention as the percentage of a cohort that placed an order in each period. For lifetime value, Shopify's field reference points to amount spent per customer, the average cumulative amount customers have spent, and to cumulative net sales since first purchase to see when acquisition costs pay back.

Shopify's campaign report defines customer acquisition cost as the total amount spent on advertising and sales divided by first-time customers attributed to the campaign, and return on ad spend as revenue earned divided by spend on the campaign. Both depend on how credit is assigned: spend-based metrics appear only for channels that report cost data to Shopify, and the attribution model decides which channel a sale counts for. The post on tracking Shopify sales by marketing channel compares the two tools' models.

How do I check these numbers on my own store?

Use one calendar month.

1. Open Analytics, set the date range, and note sessions, sessions that completed checkout, and conversion rate. Divide the second by the first. Pass: your result matches the reported conversion rate. Fail: it differs, which points to a different date range or Human or bot filter between the figures.
2. Treat September 21 to 23, 2026 as a measurement break. Use the period after it as the new baseline for session-based metrics, keep the same Human or bot filter when you compare periods, and compare orders and sales across the break. Pass: every chart you use states which side of the break it covers. Fail: a trend line mixes periods from before and after the break.
3. Create a target from your own history: open Analytics, click Create target, choose a metric such as conversion rate, and set a past period up to 2 years back. Pass: the current value the target shows for that past period matches the same metric in a report for the same dates. Fail: the two differ, which means the metric or the filters are not the same.
4. Open Customer cohort analysis, switch to the retention curve, and read each first-order cohort period by period. Pass: you compare cohorts of the same age. Fail: you compare a young cohort with an old one.

Sources, 6 October 2026: Field reference (Shopify Help Center, 2026); Session measurement update (Shopify Help Center, 2026); Customers reports (Shopify Help Center, 2026); Setting targets for metrics (Shopify Help Center, 2026); Measuring marketing performance (Shopify Help Center, 2026); Marketing reports (Shopify Help Center, 2026); Web performance reports (Shopify Help Center, 2026); API dimensions and metrics (Google for Developers, 2026); Ecommerce conversion rate benchmarks (Shopify blog, 2026); Cart abandonment rate statistics (Baymard Institute, 2026)

Related answers

• Tracking Shopify sales by marketing channel
• Causal attribution
• Conversion rate
• Customer acquisition cost
• Repeat purchase rate