What Is a Good Checkout Abandonment Rate? (2026 Benchmarks)

If you sell anything online, some share of the people who start a checkout never finish it. That share is your checkout abandonment rate, and it is one of the most important and most misread numbers in your funnel. This guide explains how to calculate it, what a good rate actually looks like in 2026, and why the headline figure almost always overstates how much revenue you can realistically win back.

How to calculate checkout abandonment rate

The formula is simple. Take the number of checkouts that were started in a period, subtract the ones that were completed, and divide by the number started:

abandonment rate = (checkouts started − checkouts completed) ÷ checkouts started

So if 343 people began a checkout last month and 37 paid, your abandonment rate is (343 − 37) ÷ 343 = about 89%. The inverse, your completion or conversion rate, would be roughly 11%.

What is the average checkout abandonment rate?

The most reliable public benchmark is the Baymard Institute average, which sits at 70.22% as of 2026, calculated across roughly 50 separate studies and updated as new ones are published.^[1] In other words, about seven of every ten started checkouts are abandoned.

But the average hides an enormous spread. Baymard’s underlying data ranges from the low 50s for routine, low-priced purchases (grocery near 50%, pet care around 52%) up into the 80s and 90s for high-consideration categories (finance and travel in the 81 to 91% band, B2B around 82%).^[1] So treat 70% as a loose anchor, not a target, and compare yourself to your own category and your own trend.

Digital products, SaaS subscriptions, and creator tools, the kind of thing sold through platforms like Polar, Lemon Squeezy, and Gumroad, behave differently from a physical-goods cart. There is no shipping cost to scare people off and usually no multi-step cart, but there are other forces pushing the number around, which is why a raw rate can be misleading.

Why the raw number lies (especially for cheap digital goods)

Before you panic about an 80 or 90% abandonment rate, understand what is actually inside it. Three things inflate the figure without representing lost, recoverable demand:

• Card-testing bots. Low-priced digital products are a favorite target for fraudsters validating stolen cards with small charges. These show up as started checkouts with throwaway or blank email addresses, then never complete. They are not real buyers, and chasing them with emails is pointless.
• Window shoppers. Some people open a checkout to see the final price or currency, then leave with no intent to buy today.
• Duplicate and test sessions. You, your teammates, and curious visitors all create started checkouts that were never going to convert.

So what counts as a good rate?

Rather than chase a universal benchmark, judge yourself against your own baseline and against the recoverable slice:

• Higher value, lower volume (subscriptions, $50+ products): you should expect a lower abandonment rate and every abandon is worth chasing. Aim to recover as many of these as possible.
• Low price, high volume ($5 to $20 products): a high gross abandonment rate is normal and partly noise. Focus on filtering fraud and recovering the genuine intent.
• Track the trend, not the absolute. A rate that is falling month over month, with stable traffic quality, is the real win.

How to reduce checkout abandonment

There are two levers. The first is prevention: reduce friction in the checkout itself, with a clear price, minimal fields, trusted payment options, and an obvious value proposition. The second is recovery: following up with the people who left, since even a well-optimized checkout will lose the majority of starts.

Recovery is where most of the upside hides, because prevention has a ceiling but a follow-up email costs almost nothing and can convert a meaningful share of genuine abandons. The mechanics matter though: you only want to email real buyers (not bots), send from a domain that lands in the inbox, and measure whether the email actually caused the conversion rather than taking credit for people who would have returned anyway.

That last point, causal measurement, is what separates a recovery program you can trust from one that just claims numbers. A small randomized holdback (a percentage of abandons who get no email) gives you a control group, so the lift you report is the part your follow-ups actually caused.

Next: see exactly how to recover abandoned checkouts on Polar.sh, or estimate your own numbers with the checkout abandonment calculator.