Baymard's cart abandonment average and its reasons-for-abandonment survey are different datasets. The average summarizes documented abandonment rates from multiple studies; the reasons research asks shoppers about their experiences. Neither tells you that a specific share of your own abandoned carts can be recovered by fixing one issue. Keeping that distinction makes the research more useful for checkout decisions and more accurate to cite.
The headline average is a compilation, not one giant experiment
Baymard's public statistics list currently reports an average abandonment rate of 70.22% based on 50 studies. The page displays a September 22, 2025 update date while its title refers to 2026. The underlying list contains studies from different years. Cite it as Baymard's compiled average, not as a single experiment conducted in 2026. [1]
That distinction matters because an average of published rates is not automatically a shopper-weighted global census. The public list supplies a useful contextual figure. It does not establish that every source used identical event definitions, traffic populations or attribution windows.
A store's own abandonment calculation needs an explicit start event. Adding to cart, beginning checkout and reaching the payment step represent different levels of intent. Moving the start event deeper into the funnel can change the reported loss rate without any improvement in the underlying experience.
The reasons survey describes people and recalled experiences
A shopper can remember abandoning a purchase for several different reasons across different visits. That makes survey answers useful for identifying friction, but unsuitable for directly dividing your lost revenue into mutually exclusive buckets.
Baymard reports that 42% of U.S. online shoppers had abandoned a cart during the preceding three months because they were browsing or not ready to purchase. Its public presentation then sets that browsing segment aside when discussing other reasons. The same article reports 17% citing a long or complicated checkout. [2]
These findings do not mean that reducing form fields will recover the corresponding percentage of your abandoned sessions. The survey's unit is the responding shopper and reported experience. Your analytics unit might be a session, cart, checkout or order. The bridge between them has to be tested rather than assumed.
| Evidence | What it describes | Useful application | Unsupported shortcut |
|---|---|---|---|
| Compiled abandonment average | Rates documented in multiple studies | Industry context | Declaring every store above it broken |
| Reported shopper reasons | Reasons people recall abandoning | Prioritizing investigation | Allocating your lost orders by survey percentage |
| Store event funnel | Observed events under your tracking definition | Locating your largest drop-off | Inferring the shopper's motivation from an exit alone |
| Controlled checkout experiment | Outcomes after a specific change | Estimating that change's effect | Assuming the effect transfers unchanged to other stores |
| Figure | Measure | Scope and source |
|---|---|---|
| 70.22% | Compiled abandonment average | 50-study compilation, source [1] |
| 42% | Browsing or not ready | U.S. shoppers reporting abandonment during prior three months, source [2] |
| 40% | Extra costs | Displayed reasons after browsing segment is set aside, source [1] |
| 20% | Slow delivery | Same displayed non-browsing reasons, source [1] |
| 19% | Credit-card trust concern | Same displayed non-browsing reasons, source [1] |
| 18% | Account creation required | Same displayed non-browsing reasons, source [1] |
| 17% | Long or complicated checkout | U.S. shopper-reported experience in past quarter, source [2] |
Table sources: [1] Baymard statistics compilation and [2] Baymard reasons research. These figures are not interchangeable fractions of your store's lost orders.
The biggest reported reason is a starting point for investigation
Baymard's current list places excessive extra costs at 40%, slow delivery at 20%, lack of credit-card trust at 19%, and mandatory account creation at 18% among the displayed non-browsing reasons. These are the source's survey findings, not TrueMargin measurements. [1]
Use each reason to ask a question that your site can answer. Are shipping charges first disclosed late' Is the arrival estimate visible before payment' Does guest checkout look available' Can customers understand the full amount before entering personal information'
Start with evidence from your own support messages, checkout errors and user sessions. A complaint about shipping cost may actually concern a surprise charge, an unexpectedly high total or a free-delivery threshold that was difficult to understand. Those cases suggest different interventions.
Removing the charge altogether is only one option. Earlier disclosure, clearer delivery promises or a better explanation of eligibility may address the uncertainty at lower cost. The research identifies a possible friction category; it does not prescribe the most profitable remedy for your business.
Session loss is not the same as permanently lost demand
A cart can be abandoned in one session and converted in another. A visitor may save the cart, compare options, ask another household member or return on a different device. Your tracking setup determines whether those actions can be connected.
For a hypothetical example, suppose 1,000 tracked carts produce 300 same-session orders. The immediate abandonment rate is 70%. If another 100 of those carts later convert within a declared follow-up window, the final non-conversion rate for that cohort becomes 60%. Both calculations can be correct because they answer different timing questions.
Those hypothetical figures are not a claim about typical recovery. They illustrate why a report should state whether it measures same-session conversion, eventual conversion or message-attributed recovery.
Do not count every order after an abandoned-cart email as incremental recovery. Some shoppers would have returned without the message. A holdout group helps distinguish credited orders from additional orders, while a consistent follow-up window keeps older and newer cart cohorts comparable.
Recoverable revenue should include the cost of recovery
A checkout improvement is valuable when its additional retained contribution exceeds its costs. An increase in order count alone does not settle that question.
Suppose an illustrative offer generates 20 additional orders with $30 contribution each before its extra delivery subsidy. That is $600 in incremental contribution before the subsidy. If the offer also subsidizes 200 orders that would have happened anyway at $4 each, that portion alone costs $800. Under those assumptions the additional orders do not cover the subsidy.
This is why a free-shipping test needs a control and an order-level contribution view. Evaluate the effect on existing buyers as well as the new orders it creates. Include refunds over an appropriate window, especially when promotions can change which products people buy.
TrueMargin's practical recommendation is to keep the funnel question and the economics question connected. First establish that the change produced additional orders. Then check retained revenue and variable costs. Do not turn a broad abandonment statistic into a forecast of guaranteed profit.
A compact measurement plan is more useful than a universal target
Write down the funnel before changing it. Specify the cart event, successful order event, deduplication rule and observation period. Preserve failed payments and technical errors as separate events rather than mixing them with normal browsing exits.
Segment only where the result can guide an action. Device, acquisition channel, country, new versus returning visitor and payment method can reveal different constraints. Keep traffic mix changes visible: a campaign that attracts more early-stage browsers can increase abandonment while still creating profitable new customers later.
Choose the intervention from the observed problem. A visible shipping calculator addresses uncertainty about total cost. A guest-checkout improvement addresses account friction. A payment integration repair addresses failed transactions. Testing all of these at once can improve outcomes, but makes it harder to identify which change earned the return.
Record the primary success measure before launch and inspect guardrails afterward. A checkout that collects more orders while increasing fraud, refunds or unprofitable fulfillment needs a different verdict from one that improves retained contribution.
Source differences should stay visible
At review, Baymard's dedicated statistics page showed 70.22%, while its separate checkout article still referred to 70.19%. These values come from different live presentations. We use the dedicated statistics page for the compiled headline and the checkout article for its explanation of survey scope. [1] [2]
A small page-to-page difference is a reason to name the exact source, not to manufacture a larger trend. Keep an access-date note in your working research file and update the citation when the publisher changes its figure. A current year in a page title does not prove that every component dataset was collected that year.
Methodology and limitations
This is an interpretation of Baymard's public source pages, combined with TrueMargin's original measurement framework and hypothetical arithmetic examples. We did not conduct a new consumer survey, reproduce Baymard's underlying research or verify its participant-level data.
The public pages describe several research components, so we do not assign the total participant count from the broader checkout program to a specific reason percentage. We also do not claim a guaranteed conversion lift or recoverable revenue amount for a reader's store.
Frequently asked questions
Is Baymard's average a conversion target'
It is contextual research. Your product mix, acquisition traffic, tracking definition and follow-up window affect comparability. Use your own consistent cohorts to measure improvement.
Can I multiply abandoned revenue by a reason percentage'
That produces an assumption, not a supported recovery forecast. A shopper survey is not a ledger of your lost orders, and fixing a reported issue will not necessarily change every affected decision.
Should cart abandonment and checkout abandonment be combined'
Keep them separate when they begin at different events. Someone who added an item and someone who entered payment details have reached different funnel stages.
Does an abandoned-cart email prove recovered demand'
It proves an attributed order under your reporting rules. Estimating incremental recovery requires a comparison that accounts for shoppers who would have purchased anyway.
Which headline Baymard figure should a writer use'
Cite the dedicated live statistics page and name its compilation basis. Avoid combining a number from one version with a sample description from a different study.
Related research: see a comparison of retailer return fees for a worked application of these measurement distinctions.
Sources
- Baymard: Cart Abandonment Rate Statistics, compiled rate and displayed reasons.
- Baymard: Reasons for Cart Abandonment, shopper scope and checkout research explanation.