Checkout optimization is the disciplined work of raising the share of started purchases that end in approved payments, judged on completed orders per checkout started. It begins as measurement: sessions reaching checkout, reaching the payment step, payments submitted and payments approved, with each gap owned by a different cause: cost and form friction, payment method fit or issuer behavior. Work runs in order of value: authorization data such as full billing address, network tokens and valid SCA exemptions first, then payment method coverage, then structural friction like forced registration, with button colors last. A store doing 300 orders a month cannot detect small lifts, so it fixes defects without testing.
The four counts you need before changing anything
Sessions that reach the checkout. Sessions that reach the payment step. Payments submitted. Payments approved. Three gaps sit between those numbers, and each gap has a different owner: cost and form friction, payment method fit, and issuer behavior.
Without that split you are guessing. Teams routinely spend a quarter redesigning a form when the loss was sitting in declines on foreign-issued cards.
Money first, pixels later
Work in descending order of value per hour. Authorization data comes first — complete billing address, network tokens, a correctly claimed SCA exemption where the risk profile allows one — because it lifts revenue on traffic already paid for and asks nothing of the buyer.
Payment method coverage comes second; for cross-border sellers a localized checkout beats any interface change. Structural friction, such as dropping forced registration in favor of guest checkout, comes third. Button colors come last, if at all.
Sample size decides whether you can test at all
A store doing 300 orders a month cannot detect a one-point improvement in any useful timeframe. Run that arithmetic before the experiment: at a 3% base rate, a test powered to catch a relative 10% lift needs tens of thousands of sessions per variant.
Below that volume, stop testing and copy established practice. Underpowered tests do not produce weak evidence, they produce confident wrong answers, because the winner gets called as soon as it looks good.
Changes that never need an experiment
Some fixes are not hypotheses. Address validation that rejects valid postal codes, a missing autofill attribute, a pay button hidden behind the keyboard, an untranslated error message — these are defects. Ship them and move on.
Reserve tests for real trade-offs: one page or three, wallet buttons above or below the card form, address before payment or after.
The average hides the market that is broken
A site-wide figure is the least useful number in the report. Split by device, country, issuer country and payment method. A single market performing far below the median is a specific, fixable fault that vanishes into a blended conversion rate.
Classify the losses too, rather than treating them as one pool: cart abandonment reporting mixes declined payments in with genuine changes of mind, and the two need different work.