What Counts as a Normal Cart Abandonment Rate

The problem

You checked your Shopify numbers, saw that most of the carts people build never turn into orders, and now you are wondering if your store is quietly failing. A high abandonment rate looks alarming when it sits there on its own. It is also the wrong number to judge your store by.

Why it happens

A high abandonment rate is not automatically a fault in your store. Averaged across 50 studies, the documented cart abandonment rate works out to 70.22% Source: baymard.com(opens in a new tab). That is a figure pooled from many separate studies, not a benchmark your own store is meant to match. A pooled average cannot diagnose a single store, so on its own it cannot tell you whether your own rate is healthy.

Part of why the pooled figure stays high is that a lot of carts were never real intent to buy. In Baymard's survey, 42% of shoppers in the US said they had left a cart behind because they were browsing or not yet ready to buy Source: baymard.com(opens in a new tab). That figure is the share of shoppers who have done it, not the share of your abandonments, but it shows how ordinary the behaviour is. Those people are comparing prices, saving items for later or exploring gift options, and Baymard counts that kind of abandonment as one you mostly cannot design away.

The useful signal lives in the other reasons, the ones you can act on. Baymard then sets the browsing and not-ready shoppers aside and looks only at the reasons that remain. Within that reduced group, the reason named most is money at the checkout. Once the just-browsing shoppers are excluded, 40% pointed to extra costs being too high, meaning shipping, tax and fees Source: baymard.com(opens in a new tab). The checkout process itself sits further down the same list. Among US online shoppers, 17% have walked away from an order because the checkout was too long or too complicated Source: baymard.com(opens in a new tab). None of these reasons show up in a single top-line percentage. That is why the headline rate cannot tell you whether anything is broken. It mixes the browsing and not-ready shoppers in with the ones you are losing for a reason you could fix.

What to do about it

Stop comparing your one number to a blog benchmark and start reading your own funnel in Shopify. The sessions data behind this report does not include a cart-addition-to-checkout rate, so it cannot hand you a like-for-like replacement for the headline number. What it does give you is a count of cart activity for context and one funnel rate that isolates a single step cleanly enough to watch over time.

What follows is a checklist of the fields to include, not a click-by-click procedure. For opening the editor and running the report, follow Shopify's own walkthrough, Build your first query(opens in a new tab), which covers reaching the editor from Analytics, whether you start a New exploration or pick from Reports.

1. checkout_conversion_rate. Of the visits that reached checkout, this is the share that went on to buy. It isolates the checkout-to-purchase step, and it is the one rate here you can watch over time.
2. sessions_that_reached_checkout. This is the count checkout_conversion_rate is calculated over, its denominator, so it tells you how large a sample the rate rests on.
3. sessions_with_cart_additions. Include it only as a separate read on cart interest, the number of visits that added an item. It is context about the cart, and it says nothing about how solid the checkout rate is.
4. A filter on human_or_bot_session to keep the report to human sessions, plus a comparison of this month against the previous period.

Know the limit while you are here. This data gives you the cart-addition count but no rate for the cart-to-checkout step. If losing people between the cart and the checkout is your worry, no session metric here measures it. Shopify does list a reached_checkout_rate, but its formula divides by all your visits rather than by the visits that added to cart, so it cannot stand in for a cart-abandonment measure.

How to tell if it worked

This is directional monitoring, not proof. If you changed the checkout, watch checkout_conversion_rate this period against its previous-period value and note which way it moved. A rise points in the right direction. It is not proof that your change caused it, because traffic mix shifts, seasons come and go and other changes you made at the same time all push on the same number.

Be careful with thin data. checkout_conversion_rate is only as steady as the number of visits it is built on, which is sessions_that_reached_checkout. When that count is small, the rate is noisy, and a difference from one period to the next can be chance rather than a real change. There is no set number of periods that turns a movement into a verdict. Keep reading the direction over successive periods, treat a rise as encouraging and a fall as worth a closer look, and hold off on firm conclusions.

What not to do

Do not turn a published benchmark into a target. A pooled figure like the 70.22% average (Baymard Institute(opens in a new tab)) describes a crowd of stores, not yours, so beating it or missing it proves nothing about your own funnel.

Do not reach for reached_checkout_rate or conversion_rate to judge a change to your cart or checkout. Both divide by all your visits, so they answer a broader question than the checkout step does, and a shift in the mix of who visits can move them on its own. For the checkout step, stay with checkout_conversion_rate. For the cart-to-checkout step, this sessions data offers no rate at all, so treat sessions_with_cart_additions as context only and do not read a cart-abandonment result into these numbers.