Cart Abandonment Rate Calculator

Use this calculator to measure your ecommerce cart abandonment rate based on completed orders and shopping carts created. Essential for identifying checkout friction, quantifying lost revenue, and prioritising conversion rate optimisation efforts.

What this calculator does

The cart abandonment rate calculator helps you measure the percentage of shopping carts that are created but never completed as an order.

It uses:

• number of completed orders
• number of shopping carts created

This gives you your cart abandonment rate - the percentage of potential buyers who added items to their cart but did not complete the purchase.

How to use the cart abandonment rate calculator

1. Enter your completed orders - the total number of orders successfully placed during the period
2. Enter your shopping carts created - the total number of carts initiated during the same period, including those that were abandoned
3. The calculator instantly shows your cart abandonment rate

Both figures are available in your ecommerce platform analytics - Shopify, WooCommerce, and most other platforms report cart creation and order completion separately.

Cart Abandonment Rate Formula

Cart Abandonment Rate = (1 - (Completed Orders / Carts Created)) x 100

Where:

• Completed Orders = total orders successfully placed during the period
• Carts Created = total shopping carts initiated during the same period
• Cart Abandonment Rate = percentage of carts that did not result in a completed order

Example calculation

If:

• Completed orders = 150
• Carts created = 600

Then:

• Completion rate = 150 / 600 = 25%
• Cart abandonment rate = 1 - 0.25 = 75%

75% of customers who added items to their cart did not complete a purchase. For every 4 carts created, only 1 resulted in an order.

What is cart abandonment rate?

Cart abandonment rate is the percentage of online shoppers who add items to their shopping cart but leave the store without completing the purchase. It is one of the most widely tracked ecommerce metrics because it directly quantifies the gap between purchase intent and completed revenue.

A high cart abandonment rate does not always mean something is broken - some abandonment is normal and expected. But consistently high rates, or rates that are rising over time, typically indicate friction in the checkout process or a mismatch between customer expectations and what the store delivers.

What is a good cart abandonment rate?

Cart abandonment rates are consistently high across ecommerce as a whole:

• Average across ecommerce - typically 65% to 80% depending on the sector and device type
• Fashion and apparel - often 70% to 85%
• Electronics - often 75% to 85%
• Travel and hospitality - often 80% to 90%
• Mobile devices - typically higher than desktop due to checkout friction on smaller screens

An abandonment rate below 60% is strong for most ecommerce categories. The most important benchmark is your own historical rate - a declining rate over time indicates checkout improvements are working.

Why cart abandonment rate matters for ecommerce

Tracking cart abandonment rate helps you:

• quantify the revenue gap between purchase intent and completed sales
• identify whether checkout friction is increasing or decreasing over time
• prioritise conversion rate optimisation efforts where they have the most impact
• measure the effectiveness of cart recovery strategies like abandoned cart emails
• benchmark your checkout performance against industry averages

Why do customers abandon carts?

Common reasons for cart abandonment include:

• Unexpected costs at checkout - shipping fees, taxes, or fees that only appear at the final step
• Forced account creation - requiring registration before checkout adds friction
• Complicated checkout process - too many steps or form fields
• Concerns about payment security - lack of trusted payment options or security signals
• Slow page load times - especially on mobile
• Browsing or price comparison - some shoppers use the cart to save items while they consider the purchase

How to reduce cart abandonment rate

Practical strategies for recovering more abandoned carts:

• Show all costs upfront - display shipping costs and fees before the checkout page
• Offer guest checkout - let customers buy without creating an account
• Simplify the checkout flow - reduce the number of steps and form fields required
• Add trusted payment options - include PayPal, Apple Pay, Google Pay, and buy now pay later options
• Use abandoned cart email sequences - automated emails sent 1, 24, and 72 hours after abandonment recover a significant portion of lost sales
• Add urgency signals - low stock indicators or limited-time offers can encourage completion

When to use this calculator

Use this calculator when you want to:

• measure your current cart abandonment rate as a baseline
• track whether checkout improvements are reducing abandonment over time
• quantify the revenue impact of your abandonment rate
• prepare ecommerce performance reporting for internal review or investors
• benchmark your rate against industry averages before prioritising optimisation work

Common mistakes when calculating cart abandonment rate

Common mistakes include:

• including wish list additions or save-for-later actions as cart creations, which inflates the abandonment rate
• not accounting for the natural browsing behaviour where customers use the cart as a comparison tool
• comparing abandonment rates across very different traffic sources without segmenting - paid traffic often abandons at different rates than organic
• treating all abandonment as a problem to solve - some level of abandonment is a normal part of the purchase consideration process

Cart abandonment rate vs conversion rate

These two metrics measure the same customer journey from different angles.

• Cart abandonment rate measures the percentage of cart initiations that do not result in a purchase
• Conversion rate measures the percentage of all visitors or sessions that result in a purchase

A business can have a reasonable overall conversion rate while still having a high cart abandonment rate if most drop-off happens before the cart stage. Use the Conversion Rate Calculator alongside this one to understand the full funnel picture.

Cart abandonment rate vs cart recovery revenue

Knowing your abandonment rate helps you estimate the revenue opportunity from cart recovery campaigns.

• Cart abandonment rate quantifies the problem - how many carts are being lost
• Cart recovery revenue estimates the upside - how much revenue can be recaptured through abandoned cart emails and retargeting

Use the Cart Recovery Revenue Calculator to estimate how much revenue a cart recovery strategy could generate based on your abandonment rate and average order value.

Related calculations

Once you know your cart abandonment rate, you may also want to:

• Use the Cart Recovery Revenue Calculator to estimate recoverable revenue from abandoned carts
• Use the Conversion Rate Calculator to measure overall store conversion rate
• Use the Average Order Value Calculator to understand the revenue at stake per abandoned cart
• Use the Revenue per Session Calculator to measure overall revenue efficiency per website visit

Useful resources

• Shopify - ecommerce platform with built-in cart abandonment reporting and abandoned checkout recovery tools
• Klaviyo - email and SMS marketing platform with automated abandoned cart flows for ecommerce stores
• Postscript - SMS marketing platform for ecommerce with abandoned cart recovery via text message
• Gorgias - ecommerce customer support platform for resolving checkout issues that contribute to abandonment

FAQs

What is cart abandonment rate?

Cart abandonment rate is the percentage of shopping carts that are created but not completed as an order. It measures how many potential buyers leave the store without purchasing after adding items to their cart.

How do you calculate cart abandonment rate?

Cart Abandonment Rate = (1 - (Completed Orders / Carts Created)) x 100.

What is a good cart abandonment rate for ecommerce?

Most ecommerce stores see abandonment rates between 65% and 80%. Rates below 60% are considered strong. The most useful benchmark is your own historical rate - focus on reducing it over time rather than hitting a specific number.

What causes high cart abandonment rates?

The most common causes are unexpected shipping costs or fees at checkout, forced account creation, a complicated checkout process, lack of trusted payment options, and slow page load times - particularly on mobile.

How do abandoned cart emails work?

Abandoned cart emails are automated messages sent to customers who left items in their cart without purchasing. A well-timed sequence - typically sent 1 hour, 24 hours, and 72 hours after abandonment - can recover 5% to 15% of abandoned carts depending on the offer and timing.

Does cart abandonment rate differ by device?

Yes. Mobile devices typically have higher abandonment rates than desktop due to checkout friction on smaller screens. Optimising your mobile checkout experience is one of the highest-impact improvements most ecommerce stores can make.

How does free shipping affect cart abandonment?

Offering free shipping - either always or above a minimum order threshold - is one of the most effective ways to reduce cart abandonment caused by unexpected costs. Many stores display a free shipping progress bar in the cart to encourage customers to add more items.

How can I track cart abandonment rate in Shopify?

Shopify reports abandoned checkouts in the Analytics section under Behaviour. You can see the number of abandoned checkouts and recovered checkouts, and set up automated abandoned checkout emails directly in Shopify or through an email marketing integration like Klaviyo.

Interpreting your result

Your cart abandonment rate result should always be interpreted in context:

• compare it against your historical baseline
• review it alongside the main commercial or operational drivers behind the metric
• compare it across products, channels, periods, or segments where relevant
• avoid interpreting the result in isolation without checking the underlying input values

A single period can be noisy, so trend direction over several periods is usually more useful than one standalone result.

Data quality checklist

Before acting on this result, verify:

• the inputs use the same time period and reporting basis
• one-off anomalies are identified separately from steady-state performance
• discounts, refunds, taxes, or fees are handled consistently where relevant
• the underlying values are complete enough to support a meaningful conclusion

Small input inconsistencies can materially change the result.

How to improve this metric

Practical ways to improve this metric depend on the underlying business model, but often include:

• identify the main driver behind the result before making changes
• test one variable at a time so the impact is easier to measure
• compare performance by segment rather than only at an overall level
• review the metric regularly so changes can be caught early

Improvement is most reliable when measurement definitions remain stable over time.

Benchmarks and target setting

A good target depends on your industry, business model, and stage of growth.

When setting targets:

• compare against your own historical trend before relying on outside benchmarks
• define both minimum acceptable and aspirational target ranges
• review targets whenever pricing, cost, demand, or channel mix changes materially
• pair benchmark review with the underlying commercial context, not just the final number

Your own historical performance is usually the most practical benchmark.

Reporting cadence and decision workflow

For most teams, a simple cadence works best:

• Weekly: monitor the metric when trading conditions or campaign activity change quickly
• Monthly: compare the result against target and prior periods
• Quarterly: reassess assumptions, targets, and the main drivers behind the metric

A practical workflow is to calculate the metric, identify the primary driver of change, test one improvement, and then review the next comparable period before scaling.

Common analysis scenarios

You can use this metric in several practical scenarios:

• monthly performance reviews
• pricing, margin, or cost analysis
• planning and forecasting discussions
• investor, lender, or management reporting

In each scenario, pair the result with the underlying business context so decisions are not made on one number alone.

FAQ extensions

Should I compare this metric across channels?

Yes, but only when definitions and attribution rules are consistent.

How many periods should I review before making changes?

At least 3 comparable periods is a good baseline unless there is a clear data issue or one-off event.

What should I do if this metric improves but profit declines?

Check whether costs, discounts, conversion quality, or downstream profitability changed at the same time.