How to Calculate Your Cart Abandonment Rate (Formula and Example)

Cart abandonment rate equals unfinished carts divided by carts started, multiplied by 100 to express the result as a percentage. Subtract completed purchases linked to those carts from carts started before dividing, and keep the same population and reporting window throughout the calculation.

Formula: (Carts started - completed purchases from those carts) / carts started × 100.

The multiplication converts a proportion into a percentage. If no carts were started in the period, the rate is undefined rather than evidence of perfect completion.

Choose what counts as a cart before extracting the data. Counting repeat add-to-cart events as separate carts can inflate the denominator, while counting unrelated purchases in the numerator can make the reported completion result misleading.

Checkout abandonment uses checkout starts rather than all carts as its denominator. The calculation is (checkouts started - completed purchases from those checkouts) / checkouts started × 100, with the same requirement to match purchases to the population being measured.

A cart can exist before the shopper begins entering information. Checkout is a later stage, so keep its abandonment rate separate from the cart-level rate in your report.

A checkout is abandoned once it remains incomplete for more than ten minutes after the shopper entered an email (Shopify Help Center, 2026). Its abandoned checkout records therefore are not interchangeable with every add-to-cart event collected by another analytics tool.

Use a consistent closure rule when looking at later purchases. A checkout completed after the reporting window should be handled the same way in each comparison, rather than counted for one period and ignored for another.

Example: Store A has 100 checkout starts in a month and 45 completed orders from those starts. Its abandonment rate is (100 - 45) / 100 × 100 = 55%; Store B has 1,000 checkout starts and 300 matching orders, so its rate is (1,000 - 300) / 1,000 × 100 = 70%.

Store B has a higher abandonment rate but also more completed orders, so the rate alone does not tell you which store has a stronger business.

To compare profit, add product margins, acquisition costs and fulfillment expenses to the report. Before considering calls, check how many unfinished checkouts have contact information and a useful reason for follow-up.

Keep the rate, completed-order count and financial result together when reviewing your own store. That prevents a percentage improvement caused by fewer low-intent visits from being mistaken for additional profitable orders.

Use begin_checkout to identify checkout starts, add_shipping_info to examine progression through shipping, add_payment_info to examine payment progression, and purchase to record completed purchases. Verify that your installation collects each event at the intended checkout step.

Confirm that the events fire at the intended action and that purchase is tied to a completed order. Check transaction identifiers so reloading a confirmation page does not create another completed purchase in your analysis.

Compare shipping and payment progression with recorded checkout errors and customer questions. A decline at shipping may point you toward delivery terms or total cost, while payment-stage exits require a closer look at payment options and failures.

Do not turn raw event totals into a funnel without checking identity and repetition. A shopper can return to a step, submit corrected information or visit from another device; the analysis must distinguish repeated actions from distinct purchase attempts.

• Choose carts or checkout starts as the denominator and name the measure clearly.
• Remove test orders and internal checkout activity consistently.
• Deduplicate completed purchases using transaction identifiers.
• Keep time zones and reporting windows aligned.
• Decide how later completions are assigned to the original checkout population.
• Check consent-related measurement gaps and differences between tools.

Document changes to your tracking implementation beside the report. If an event starts firing at a different step, the apparent abandonment change may come from the measurement change rather than the checkout.

Compare analytics with operational order records before relying on either total. Investigate mismatches instead of forcing one tool's denominator together with another tool's completed purchases and presenting the result as a clean rate.

The average cart abandonment rate is 70.22% across 50 studies (Baymard Institute, 2025). Use it as broad cart-level context, not a target that every checkout-level report must meet.

A checkout-start rate from your store may have a narrower population than Baymard's cart figure. If you compare them, explain that difference rather than calling your store better or worse on the basis of mismatched definitions.

Your most useful operational comparison is often a consistent version of your own measurement before and after a change. Note traffic sources, promotions and product availability so a different mix of shoppers does not get credited to a checkout repair.

The reduce abandoned carts hub explains what to examine once the measurement is reliable. Use your checkout records, shopper questions and margins to choose a recovery audience.

Loop Voice attribution tracking ties recovered revenue to calls, helping merchants review calling outcomes separately from the store's overall abandonment rate. Its outbound recovery flow waits ten minutes by default, and the delay is adjustable.

Credits are used only when someone picks up, with about 1.5 credits per answered conversation on average (Loop Voice pricing data). Local quiet hours, frequency caps and stop-on-order or opt-out controls apply; the agent identifies the store and never claims to be human. See Loop Voice pricing when comparing conversation cost with recovered-order contribution.