Checkout friction score calculator

Three of our tools deal with the same money from different angles, and it helps to know which one you opened. The funnel calculator tells you WHICH stage people vanish at. The cart abandonment calculator tells you HOW MUCH that hole costs and how much of it is recoverable. This one answers the harder question that comes next: WHY your checkout loses people and what to fix first. It does not measure your traffic, it grades the design of the flow.

How to use it

1. Open your own checkout on a phone, as a customer, with a real product in the cart. That is where friction hurts most and where most stores have never truly looked.
2. Count the steps to a confirmed order and the fields you had to fill in. A pre filled field you can still edit counts; a hidden field nobody sees does not.
3. Enter how many distinct payment methods the store offers. Credit card and debit card count as two, a wallet and a bank transfer count as two more.
4. Tick the best practices your checkout already meets. When in doubt, leave it unticked: an optimistic diagnosis fixes nothing.
5. Fill in monthly orders and average order value to turn friction into money, then read the fix queue from the top down.

How it works: the model behind the score

The score is the sum of 11 weighted items that add up to exactly 100 points. No black box: if you disagree with a weight, you know which one to move and by how much.

The weights: fields 14, steps 12, shipping visible upfront 12, guest checkout 12, mobile ready 10, inline validation 8, payment methods 8, progress indicator 6, trust signals 6, distraction free checkout 6, order summary visible 6. The split follows the relative weight of the abandonment causes documented in public checkout usability research: extra cost revealed late and a forced account lead by a wide margin, with too many fields and too many steps right behind.

The three numeric items use a linear ramp rather than a step function on purpose. Going from 13 fields to 11 deserves to move the score, otherwise the tool only rewards finishing the job and gives you no reason to start it.

Worked example (reproduces the default result)

With the values already loaded, describing a very common store: 4 steps, 11 fields, 3 payment methods, shipping hidden until the end, a forced account, a checkout still carrying the menu and a banner, and the rest of the best practices in place.

• Steps: 12 × (5 − 4) ÷ 4 = 12 × 0.25 = 3.0 of 12.
• Fields: 14 × (16 − 11) ÷ 10 = 14 × 0.5 = 7.0 of 14.
• Shipping upfront: no, 0 of 12. Guest checkout: no, 0 of 12. Distraction free: no, 0 of 6.
• Payments: 8 × (3 − 1) ÷ 4 = 8 × 0.5 = 4.0 of 8.
• Progress 6, mobile 10, trust 6, inline validation 8, order summary 6, all full.
• Score: 3 + 7 + 0 + 0 + 4 + 6 + 10 + 6 + 0 + 8 + 6 = 50 out of 100, band D, high friction.
• Potential lift: 35 × 50 ÷ 100 = +17.5% conversion.
• With 600 orders per month: 600 × 17.5% = 105 extra orders per month; 105 × $ 240 = $ 25,200 per month, or $ 302,400 per year.

The fix queue comes out as: shipping before checkout (+12), guest checkout (+12), fewer steps (+9), fewer fields (+7), remove distractions (+6), more payment methods (+4). Those six add up to exactly the 50 missing points. That is what the tool shows when the page loads, and it is the same reasoning you repeat with your own numbers.

How to read it and where the score misleads

The score is not a verdict on your store, it is a map of what the model can see. It ignores things that sometimes matter more than everything listed here: prices out of line with the market, slow delivery, the wrong product for the audience, a bad reputation on social. A checkout scoring 95 in a store with twelve day shipping still loses sales, and that is not a checkout problem.

The second trap is flattering yourself while answering. Almost everyone ticks trust signals and mobile ready by reflex, without ever opening the site on a phone. If you could not complete a test purchase on your own phone in under two minutes, untick it. The tool is only worth anything if the answers are honest, and the cost of lying is attacking the wrong item for a quarter.

The third is confusing potential with a promise. The estimate scales a research ceiling by the size of the gap and assumes fixing everything delivers everything, which rarely happens: part of checkout abandonment is people comparing prices who would never buy in that session, the same phenomenon the cart abandonment calculator handles with a recovery rate. Use the number to size the effort, not to promise a result in a meeting.

Finally, the model treats the items as independent, and they are not. Cutting three fields and one step usually touches the same screen, so the gains do not add up cleanly. One more reason to fix one item at a time and measure each one.

From the score to the test

A checklist is not evidence. Every item in your queue is a hypothesis about your customer behaviour, and the only way to know whether you were right is to measure it with an A/B test. The short path:

• Take the first item in the queue and turn it into a testable hypothesis with the hypothesis generator, in the if, then, because, measured by format.
• Size the experiment in the sample size calculator before switching anything on. Checkout traffic is thinner than the homepage, so the sample usually asks for more patience than people expect.
• If the change touches shipping, coupons or bundles, judge it by money in the revenue per visitor calculator, not by order count. Free shipping lifts conversion and sinks revenue with remarkable ease.
• Confirm the result in the significance calculator and only then declare a win.
• Re score the checkout after each fix ships. A shrinking gap is the progress metric for the whole project.

The full method lives in the conversion rate optimization guide, and what to test inside recovery emails is in cart abandonment email A/B tests. If you want to know first whether your current rate is any good, compare it with the conversion rate benchmarks.

Frequently asked questions

What is checkout friction?

Friction is every bit of effort, doubt or surprise a customer meets between deciding to buy and finishing the order. It shows up as an extra step, an unnecessary field, a forced account, shipping revealed only at the end, too few payment methods and error copy that never says what to do. Friction is not the same as low intent: someone who reached the checkout already wanted to buy, so every abandonment there is a sale you had in hand.

How does this tool score a checkout?

It grades 11 items with published weights that add up to 100 points. Three are numeric and use a linear ramp (steps, fields and payment methods), eight are yes or no and pay the full weight or nothing. The score is the sum of the points earned. Every weight is listed on this page, so you can disagree with one number and still use the fix queue, which only depends on how many points each item left on the table.

What is a good checkout score?

85 to 100 is low friction, 70 to 84 is friction under control, 55 to 69 is meaningful friction, 40 to 54 is high friction and below 40 the checkout sabotages the sale. Most stores that never went through a checkout review land between 40 and 60, almost always because of the same pair: a forced account and shipping costs hidden until the last screen.

Does the tool crawl my URL automatically?

No, and that is deliberate. A crawler can count fields, but it cannot tell whether shipping appeared before the checkout, whether the card error explained what to do, or whether the customer understood which step they were on. You answer in two minutes with information only someone who runs the store has, and you get an honest diagnosis. Nothing you type leaves your browser: this page makes no network calls at all.

Is the potential conversion lift guaranteed?

No. It scales a ceiling observed in public usability research by the size of your points gap, so it is an order of magnitude for deciding where to invest, not a forecast. Two stores with the same score can gain very different things by fixing the same items. The real number for each fix only appears when you measure it with an A/B test on your own store.

Where do I start when the queue has six items?

At the top, always. The queue is already sorted by points lost, which is this model proxy for impact. If two items tie, start with the cheaper one to ship: showing shipping before the checkout is usually a product page and cart change, while allowing guest purchase can touch your customer database. Do one at a time and measure, otherwise you never learn which change paid for itself.

Keep going

With a score and a queue in hand, the next step is to stop arguing and start measuring. Find where the funnel leaks in the conversion funnel calculator, price the hole with the cart abandonment calculator and read the method in the CRO guide.