How to Reduce Cart Abandonment: A Data-Driven Approach for Ecommerce Stores

Most guides on how to reduce shopping cart abandonment give you the same tips: “Add trust badges”, “Offer free shipping”, “Simplify checkout”. Some of those work—but not always, and not for everyone. The hard truth is that the average shopping cart abandonment rate is high, and it has been high for years.

If you try to implement every “best practice” at once, two things usually happen:

1. you spend time and money on changes that weren’t your real problem, and
2. you can’t tell what actually improved results because everything changed at once.

This guide uses a diagnostic-first approach: use your data to find where customers drop off, identify the real cause, then fix the biggest bottleneck first. Industry research shows the biggest abandonment drivers are often shipping and extra costs, lack of trust, forced account creation, and checkout friction—but the mix is different for every shop.

Illustrative example (hypothetical): One store stopped guessing and found that most drop-offs happened the moment shipping costs appeared. They moved shipping estimates earlier in the journey, and that change outperformed a long list of other tweaks.

What is cart abandonment and why it matters

What is cart abandonment? Cart abandonment happens when a shopper adds items to an online cart but leaves without completing the purchase.

This matters because cart abandonment is a conversion problem hiding in plain sight. You already paid (with time or marketing spend) to get that shopper to:

• find a product,
• consider it,
• and click “Add to cart”.

When they leave at checkout, it usually means one of two things:

• they weren’t ready to buy yet (common), or
• something in your checkout experience caused doubt or friction (fixable).

The revenue impact is bigger than most store owners think. If your store makes $500,000/year and your cart abandonment is roughly 70%, then roughly 30% of “carts created” become orders. If you could remove all checkout friction (you can’t, but this shows scale), the “full potential” from people who start the cart could be about $500,000 ÷ 0.30 ≈ $1.67M, meaning ~$1.17M is not converting. The exact figure depends on how you define “cart created” and your tracking set-up—but the opportunity is real.

A practical “opportunity” calculator. Instead of assuming you can recover everything, estimate what a realistic improvement is worth:

• Monthly opportunity (rough):
  Monthly carts × Average order value × (current abandonment rate) × (recoverable share)

A conservative planning range for “recoverable share” is often single digits, because many people were browsing. For example, marketing benchmarks show abandoned cart automations can drive meaningful placed-order rates, but they vary by industry and list quality.

Why does cart abandonment happen and what “normal” looks like

Benchmarks you can use without being misled

It helps to know what’s typical, so you don’t panic—or get complacent.

• Baymard Institute tracks a long-running benchmark and reports an average documented cart abandonment rate around 70% (roughly 70.19%–70.22% depending on the dataset snapshot).
• Other benchmark datasets can be higher because they measure different mixes of sites and behaviours. For example, Dynamic Yield reports a global average around 76.99% in its benchmark view.

Important: Benchmarks tell you if you might have a problem. They do not tell you what your problem is. The only way to get that is to look at your funnel drop-offs by step, device, traffic source, and product category.

The most common reasons people abandon

Baymard’s compiled reasons (excluding “just browsing”) show the biggest reported drivers include:

• Extra costs too high (shipping, tax, fees): 39%
• Delivery too slow: 21%
• Didn’t trust the site with card information: 19%
• Site wanted them to create an account: 19%
• Checkout too long/complicated: 18%
• Couldn’t see total cost up-front: 14%
• Not enough payment methods: 10%
• Website errors/crashes: 15%

You’ll also see other summaries quoting slightly different percentages (for example, ~13% for payment methods). Differences usually come from survey waves, country mix, and whether the “just browsing” segment is separated.

One more benchmark insight that matters: “just browsing / not ready to buy” is a big slice. Baymard has reported 43% of shoppers abandon because they were browsing—not because checkout was broken. That’s why your goal is rarely “eliminate abandonment”. It’s “remove preventable friction and recover the highest-intent abandoners”.

Calculating cart abandonment rates with clean definitions

If you want to reduce cart abandonment rate, you first need to measure it the same way every week.

The basic formula

A common formula for calculating cart abandonment rates is:

Cart Abandonment Rate = (1 - Completed purchases / Carts created) × 100

This is simple—but the definitions behind “carts created” and “completed purchases” can vary across tools.

Cart abandonment vs checkout abandonment

Many stores accidentally mix these up.

• A “cart” is typically earlier: a shopper adds to cart.
• A “checkout” is later: the shopper begins checkout and may enter shipping/payment details.

On Shopify, an “abandoned checkout” is specifically tied to a checkout that remains incomplete after the customer has provided their email, and Shopify treats it as abandoned after a short time window (Shopify documentation describes the trigger and timing).
Shopify’s developer documentation similarly describes an abandoned checkout as an incomplete checkout where the customer added items and provided contact information but didn’t complete the purchase.

This matters because:

• you can only email recover a checkout if you captured an email/phone in the first place,
• but you can still retarget carts via ads or on-site reminders even without email.

Where to find the data

You can get this from most analytics stacks if ecommerce events are configured.

• In Google Analytics 4, recommended ecommerce events include add_to_cart, begin_checkout, and purchase, plus checkout-step events like add_shipping_info and add_payment_info.
• GA4’s Purchase journey and Checkout journey reports help visualise drop-offs across steps (where users move from beginning checkout to purchase, and where they drop).
• Shopify includes abandoned checkout-related fields (for example, “Abandoned checkout date”) in its analytics field references, which supports reporting and campaign timing.

Go deeper than one headline number

If you only track “overall abandonment”, you will miss the real levers. Strong diagnostic questions include:

• “What is abandonment by device?”
• “What is abandonment by traffic source?”
• “Which checkout step has the biggest drop-off?”
• “Which products/categories have the highest abandon rates?”

Diagnosing your biggest abandonment bottleneck

This is the part most competitors skip. They jump straight to tactics, even though the “right” tactic depends on where and why people drop.

Build a simple drop-off map

Start by mapping your funnel as a sequence of steps you can measure:

• add to cart
• view cart
• begin checkout
• submit shipping info
• submit payment info
• purchase

GA4’s journey reports exist specifically to show where users drop between steps.

What you’re looking for: the step with the largest drop-off relative to the step before it. That is almost always your highest ROI fix.

Segment abandoners so you stop averaging away the truth

Abandonment is not one group of people. It’s a mix. Benchmarks show meaningful differences by device, region, and industry.

A simple segmentation set that works for most ecommerce teams:

• Device: Mobile abandonment tends to be higher than desktop in large benchmark datasets.
• Traffic source: Paid traffic often behaves differently from email/returning customers (compare your own sources).
• Cart value: High AOV carts abandon for different reasons than low AOV carts (shipping thresholds and sticker shock often show up here).
• New vs returning: New shoppers often need more trust signals than returning customers (use your own cohort data).

Add qualitative “why” to the quantitative “where”

Numbers tell you where the leak is. They don’t always tell you why.

This is where behaviour analytics tools help. For example, Hotjar describes using funnels and connected session recordings to visualise drop-offs and then watch recordings tied to those steps to understand what happened.
Hotjar and similar tools also support surveys (including exit surveys) to capture reasons in the shopper’s own words.

A practical workflow that doesn’t require a data team:

• Watch 20–30 recordings of people who abandon at the worst step.
• Collect 100–200 survey responses at the same step (simple multiple-choice + optional free text).
• Compare themes with your abandonment segments (mobile vs desktop, new vs returning).

Watch for data quality issues before you “fix” the wrong thing

If your abandonment reporting suddenly spikes and nothing else changed, it may be bots or spam checkouts. Shopify merchants have reported fake abandoned checkouts created by bots that distort analytics and can even harm email deliverability if you automatically message them.

When in doubt, validate with:

• trend breaks (when did it start?),
• session recordings,
• and a sample of abandoned checkout details.

Strategies to lower ecommerce cart abandonment using evidence and prioritisation

This section includes practical strategies to lower ecommerce cart abandonment—but the rule is: do the fix that matches your biggest drop-off first.

Below are eight high-impact levers, written in a “diagnostic → fix” style.

Fix the biggest cost surprise first
If many users abandon when they see shipping/tax/fees, prioritise transparency earlier in the journey. Baymard’s research highlights extra costs as a leading abandonment driver.
Baymard explicitly recommends showing estimated shipping costs on product pages (or otherwise early) so users can judge the true total cost before checkout.

Reduce checkout form work before redesigning checkout steps
It’s tempting to obsess over “one page vs multi-step”. But research suggests what matters most is how much work users feel they must do—especially the number of form fields. Baymard reports the average checkout in 2024 had 11.3 form fields, and that far fewer fields are often sufficient.
Baymard also reports that an “ideal” checkout can be as low as 12–14 form elements (and fewer if you count only form fields), while real checkouts often show far more by default.

Make guest checkout obvious, not hidden
Even when guest checkout exists, many sites make it hard to spot. Baymard’s research notes forced account creation drives abandonment, and the “guest” option must be prominent to work.

Treat mobile checkout as its own product
Large benchmark datasets show mobile is usually higher abandonment than desktop.
Practical fixes are often basic: larger tap targets, better autofill support, fewer fields, and faster load times. (Use your device segmentation and recordings to confirm what breaks on your site.)

Build trust at the exact moment doubt appears
Trust concerns consistently appear in abandonment research. Baymard reports a meaningful share of users abandon because they don’t trust the site with card details.
The best trust signals are not generic “badge spam”. They’re clear answers to common fear questions: “Is checkout secure?”, “Can I return this easily?”, “Who do I contact if something goes wrong?” Combine this with what your surveys actually say.

Offer the payment methods your customers expect
Limited payment methods are a known abandonment reason in survey data. Baymard’s compiled list shows a measurable share cite “not enough payment methods”.
If you sell internationally, “expected” methods vary by country. The right approach is not “add everything”, but “add what your customers use most” (use payment drop-off data and support tickets).

Use abandoned cart recovery sequences that match intent
Abandoned-cart automation can be one of the best “quick wins” because it recaptures people who already showed buying intent. Klaviyo reports abandoned cart flows drive high revenue per recipient and an average placed order rate in its benchmarks.
Dynamic Yield also cites benchmark-style findings that abandoned cart emails get high opens and that a portion of clicks lead to recovered sales.

A simple three-touch sequence that many teams start with:

• First message: reminder (no discount)
• Second message: address objections (delivery, returns, trust)
• Final message: limited incentive only if needed (avoid training customers to abandon for discounts)

For timing, Omnisend recommends sending the first abandoned cart email fairly quickly (often within an hour range) to catch intent while it’s fresh—then test variations for your audience.

Test one change at a time and measure the step you targeted
A/B testing is not only for button colours. Your goal is to prove: “Did the drop-off at this step fall after the change?” GA4 funnels/journeys and behavioural funnels are built for exactly that sort of step-level measurement.

If your team struggles to answer questions quickly, Daymark’s positioning is that you can ask questions like “Show cart abandonment by device” in plain English, generate charts, and share dashboards—useful for fast iteration cycles.

Track improvements and avoid the mistakes that keep abandonment high

To reduce cart abandonment rate sustainably, you need a weekly scoreboard and a “don’t fool yourself” mindset.

A simple weekly scoreboard

Track these consistently:

• Overall cart abandonment rate (your chosen definition)
• Checkout-step drop-offs (where the biggest leak is)
• Recovery performance (email/SMS/push) via placed order rate, revenue per recipient, and click-to-conversion (your ESP will show this)
• Segment trends (mobile vs desktop, top traffic sources, high-AOV vs low-AOV carts)

Common mistakes to avoid

Mistake: fixing everything at once. You lose the ability to learn what worked. Use one targeted fix per sprint, based on your biggest step drop-off.

Mistake: focusing only on recovery and ignoring prevention. Recovery emails help, but Baymard’s data shows many abandonment drivers are checkout UX issues you can remove (extra costs shown late, forced accounts, too many fields).

Mistake: obsessing over benchmarks without diagnosing your funnel. Benchmarks vary by dataset, device mix, and industry, so your goal should be trend improvement in your segments.

Mistake: trusting dirty data. If bots create fake checkouts, your abandonment rate can spike and your automations can send messages to junk addresses, hurting deliverability. Validate suspicious jumps.

Conclusion (keep this mindset): Cart abandonment is not “one problem”. It’s a mix of intent (“just browsing”) and friction (cost surprises, trust gaps, checkout effort). The data-driven approach is to identify the biggest leak, fix it, measure it, and repeat.

Frequently asked questions

What is cart abandonment in ecommerce?

Cart abandonment is when shoppers add products to an online cart but leave without completing the purchase.

Why does cart abandonment happen?

It happens for several common reasons: unexpected extra costs, slow delivery expectations, lack of trust, forced account creation, and checkout complexity. Different research reports vary by percentage, but these themes appear consistently.

What is the average shopping cart abandonment rate?

Benchmarks vary by dataset and definition. Baymard reports an average documented rate around 70%, while Dynamic Yield's benchmark view reports a global average around 76.99%. Use benchmarks as context, but diagnose your own funnel for action.

How do I calculate my cart abandonment rate?

A common method is: (1 - Completed purchases / Carts created) x 100. Make sure your definition of carts created stays consistent in your reporting each week.

How do I track cart abandonment in GA4?

GA4 supports ecommerce funnel tracking with recommended events like add_to_cart, begin_checkout, and purchase, plus checkout-step events. Use the Purchase journey and Checkout journey reports (or explorations) to see step-by-step drop-offs.

What is the best way to recover abandoned carts?

Abandoned cart automation (email and sometimes SMS/push) is widely used because it targets shoppers who already showed intent. A short sequence usually works best: reminder, objections, then incentive only if needed.

Should I offer discounts to cart abandoners?

Be careful. If you discount too early, you can train customers to abandon on purpose. Start with a reminder and objection-handling first, then reserve discounts for later touches or high-intent segments.