Shopify Cart Abandonment: 70% of Carts Abandoned—How to Recover

Shopify cart abandonment is one of the most persistent challenges online retailers face. Across industries, roughly 70 percent of online shopping carts are abandoned before purchase; for Shopify stores the figure is often similar. That means for every 10 shoppers who add an item to their cart, seven leave without completing the sale. Fixing this gap can feel overwhelming, but many causes are diagnosable and testable. This article breaks down the key reasons shoppers abandon carts, practical cart abandonment solutions you can implement, and the A/B testing approach you can use to find what works best for your store.

The scale of the problem: why 70 percent of carts get abandoned

Around 70 percent is a commonly reported average for cart abandonment across e-commerce. The exact number varies by industry, device and region; luxury goods may see higher consideration and thus higher abandonment, while commodity items may convert better. Two basic realities drive that average:

• Online shopping is low-commitment: adding an item to a cart does not signal purchase intent; many people use carts as wishlists or price-comparison tools.
• Checkout friction is common: unexpected costs, slow pages, limited payment choices and trust concerns all interrupt momentum.

Understanding which of those forces affect your store is the first step to reducing Shopify cart abandonment and recovering lost revenue.

Why shoppers abandon carts: the most common causes

To reduce cart abandonment Shopify merchants need to diagnose the specific drivers for their audience. Common causes include:

• Unexpected costs: Shipping fees, taxes or extra charges that appear late in the process are the single biggest cause of cart abandonment.
• Complicated checkout: Long forms, mandatory account creation, or unclear progress indicators increase friction.
• Slow page speeds: Mobile shoppers in particular will abandon slow product, cart or checkout pages.
• Payment options: Lack of preferred payment methods such as PayPal, Apple Pay or regional wallets can stop a sale.
• Trust and security: Unclear returns, weak social proof, or missing security badges reduce confidence.
• Distractions and multitasking: Shoppers on mobile often get interrupted; long processes lose attention.
• Price and comparison shopping: Some shoppers use your cart to compare prices and then buy elsewhere.
• Mobile UX issues: Tiny buttons, third-party popups, or forms that do not fill properly on mobile cause abandonment.
• Product uncertainty: Poor photography, vague descriptions, bad sizing information and missing reviews create doubt.

Diagnose where abandonment happens: data you should collect

Before implementing fixes, collect the data that shows where users drop off and why. Useful metrics include:

• Funnel drop-off rates: Measure conversion from product page to add-to-cart, add-to-cart to cart view, cart view to checkout, and checkout to order confirmation.
• Device and browser breakdown: Compare desktop, tablet and mobile performance to find platform-specific problems.
• Traffic source analysis: Identify whether paid channels, organic search or email bring users who convert differently.
• Page speed: Use Lighthouse, Google PageSpeed Insights and real user monitoring to measure load times and Core Web Vitals.
• Session recordings and heatmaps: Tools like Hotjar or FullStory show where users hesitate, click, or rage-click.
• Cart abandonment reasons: Add a short optional survey on cart pages or in abandonment emails to learn why shoppers leave.

These data points tell you whether the issue is mainly discovery, product detail, cart UX or checkout friction. Once you know that, you can prioritise tests and fixes.

Why A/B testing is the right approach

Many "best practices" do work, but e-commerce is highly contextual. A change that boosts conversions for one store can harm another. A/B testing gives you an evidence-based route to reduce cart abandonment Shopify-wide because it lets you:

• Test hypotheses rather than rely on assumptions.
• Measure real impact on revenue and conversion rate.
• Optimise iteratively: small wins compound into big gains.
• Segment outcomes: see what works for mobile users, new visitors or returning customers.

Testing should be hypothesis-driven: propose a reason shoppers abandon carts, design a variant to address it, and measure the impact on a primary metric like conversion rate or revenue per session.

Practical A/B tests to reduce Shopify cart abandonment

Below are concrete, hypothesis-driven tests to run. Each item includes what to change, why it might work and the metric to track.

1. Product page experiments

• Test product titles and descriptions: Hypothesis: clearer benefits and urgency reduce hesitation. Variant: reword title and description to emphasise benefit, clarity and key specs. Metric: add-to-cart rate and conversion rate.
• Test imagery and visual hierarchy: Hypothesis: more usable images reduce uncertainty. Variant: add 360-degree views, lifestyle images, zoom or video. Metric: add-to-cart and bounce rate.
• Test price presentation: Hypothesis: anchoring or price bundling increases perceived value. Variants: show per-unit pricing, bundle discounts or strikethrough "was" prices. Metric: add-to-cart, average order value.
• Test urgency messaging carefully: Hypothesis: limited stock messaging increases conversions. Variant: display low-stock indicators or countdowns for offers. Metric: add-to-cart and purchase rate; watch for long-term annoyance.

2. Cart page experiments

• Surface shipping cost earlier: Hypothesis: showing shipping estimates on product pages and cart reduces surprise. Variant: add estimated shipping or a link to a shipping calculator. Metric: cart-to-checkout conversion.
• Guest checkout vs account creation: Hypothesis: removing mandatory sign-up reduces friction. Variant: allow guest checkout and show benefits of creating an account separately. Metric: cart-to-checkout and checkout completion rate.
• Progress indicators and fewer form fields: Hypothesis: clear progress and shorter forms reduce drop-off. Variant: show multi-step progress bar and minimise fields. Metric: checkout completion rate.
• Express payment buttons: Hypothesis: adding PayPal, Apple Pay, Google Pay reduces friction on mobile. Variant: add express pay options. Metric: checkout conversion rate, particularly on mobile.

3. Checkout experiments (Shopify constraints)

Checkout pages on Shopify are subject to platform constraints unless you are on Shopify Plus. That affects how you can test. Options include:

• For non-Plus merchants: test everything leading up to checkout: product pages, cart pages and pre-checkout modals. Use cart-level experiments and email recovery tests to influence checkout outcomes.
• For Shopify Plus merchants: you can edit checkout.liquid and run more direct checkout experiments. Keep tests lean and ensure compliance with payment provider rules.

Checkout-focussed tests include simplifying shipping and payment options, adding reassuring copy about data security and returns, and testing pre-filled fields for returning customers.

4. Cart recovery email and onsite remarketing tests

• Test email timing and subject lines: Hypothesis: a well-timed first email recovers immediate abandoners. Variant: send first email at 30 minutes, 1 hour and 4 hours; test subject lines focussing on urgency, benefit or reminder. Metric: recovered revenue and click-through rate.
• Test email content types: Hypothesis: social proof beats discounts in early recovery emails. Variants: email A shows reviews and product benefits; email B offers an immediate discount. Metric: recovered revenue and net margin.
• Test onsite exit intent and cart overlays: Hypothesis: a subtle onsite prompt recovers distracted shoppers. Variants: show an exit-intent modal with free shipping threshold reminder, or a simple cart save banner. Metric: add-to-cart completion and recovered sessions.

Designing tests: best practice checklist

To get reliable results, follow these testing practices:

• Define a clear hypothesis: Explain why you expect a change to improve conversion.
• Select a primary metric: Use conversion rate, revenue per visitor or average order value depending on the test goal.
• Calculate required sample size: Use a sample size calculator to avoid underpowered tests. For small stores aim for larger effect sizes or longer test durations.
• Run tests for a full business cycle: Include weekends and weekdays to avoid time-of-week bias; typical minimum is two weeks but longer may be necessary.
• Avoid peeking: Do not stop tests early based on preliminary results; wait for statistical significance.
• Segment results: Analyse results by device, traffic source and new vs returning visitors to ensure the change helps across critical segments.
• Test one major variable at a time: Start with single-variable tests to learn causation; use multivariate testing later for fine-tuning.
• Track revenue and downstream effects: A change that increases add-to-cart may not increase net revenue; always check order value, returns and margin.

Sample hypotheses and test ideas you can implement this week

Here are specific experiment ideas with sample hypotheses you can pick from depending on your data.

• Hypothesis A: If we display shipping cost on the product page, fewer shoppers will abandon during checkout. Test: show "estimated shipping" based on postcode country selector on product pages. Metric: cart-to-checkout and checkout conversion.
• Hypothesis B: If we add PayPal and Apple Pay buttons on the cart page, mobile conversions will rise. Test: add express payment buttons to the cart and measure mobile checkout conversions.
• Hypothesis C: If we simplify product descriptions to emphasise benefits and use a bulleted specs box, add-to-cart rate will increase. Test: create a variant with a condensed description and a visible guarantee or returns link.
• Hypothesis D: If we change the cart CTA from "Checkout" to "Secure Checkout - Fast & Free Returns", shoppers will feel more confident and convert more. Test: CTA text and colour variants. Metric: click-through rate to checkout and completion rate.

Cart recovery sequences that actually work

Emails and ads are the final opportunity to recover abandoned carts. A tested recovery sequence might include:

• Email 1 (30–60 minutes): Friendly reminder with product image and clear CTA; no discount initially. If your analytics show a strong response to social proof, include a short testimonial.
• Email 2 (24 hours): Highlight benefits, size or fit information and free returns policy; include a dynamic cart summary and urgency only if stock levels are actually limited.
• Email 3 (48–72 hours): Offer a time-limited discount or free shipping if margins allow; personalise with the original cart items. Test whether discounts actually increase net revenue after considering acquisition and margin impact.
• Ad retargeting: Serve personalised dynamic ads on social and display networks showing the abandoned items and complementary products; frequency cap to avoid ad fatigue.

Test each step of the sequence. Measure recovered revenue, number of recovered sessions, and the cost of incentives versus their incremental sales.

Technical notes for Shopify stores

Shopify has platform-specific considerations that affect how you test and fix cart abandonment:

• Checkout customisation limits: On standard Shopify plans the checkout is locked down; you can test product pages and cart pages freely, but checkout-level edits are available mostly to Shopify Plus merchants. Use pre-checkout optimisations and recovery emails if you are not on Plus.
• Theme editing and Liquid: Product and cart pages are rendered via theme templates using Liquid. Many A/B test apps operate by injecting JavaScript or by providing theme-compatible variations to avoid heavy code changes.
• Apps and third-party scripts: Too many apps or heavy scripts can slow pages and increase abandonment; run an audit, remove unused apps and defer non-essential scripts to improve speed.
• Payment gateways and payment buttons: Express payment buttons (Shop Pay, Apple Pay, Google Pay, PayPal) significantly reduce friction; ensure they are enabled and visible especially on mobile.
• Analytics and attribution: Make sure Google Analytics, Shopify analytics and any A/B testing tool share consistent order and funnel data; cross-check revenue figures to avoid attribution errors.

Avoid common pitfalls when running tests

Some mistakes cause merchants to draw wrong conclusions:

• Running tests too briefly: Small stores need longer tests to reach statistical significance.
• Testing during promotions: Running A/B tests during a sale or major marketing push can bias results; avoid such periods unless you test the promotional creative itself.
• Confounding variables: If you change multiple elements at once without proper design, you cannot know which change caused the result.
• Neglecting business metrics: Optimising purely for conversion rate can reduce average order value or margin; always measure revenue and profit impact.
• Failing to segment: A variant that looks neutral overall might be highly positive for mobile users and negative for desktop users; segmentation helps allocate changes to the right audience.

Low-cost, high-impact fixes you can implement today

If you need immediate actions that often reduce Shopify cart abandonment, start here:

• Show estimated shipping or a clear shipping link on the product page.
• Enable guest checkout and express payment options.
• Remove mandatory account creation and make sign-up optional after purchase.
• Improve page speed: compress images, defer third-party scripts and use a fast theme.
• Add clear returns, refunds and shipping policies close to the add-to-cart button.
• Place trust badges and secure payment icons near payment areas.
• Test the cart CTA copy and colour: small wording changes often produce measurable lifts.
• Set up a three-step cart recovery email flow with clear CTAs and dynamic cart content.

Using A/B testing tools with Shopify

There are two common approaches to A/B testing on Shopify:

• Client-side testing: Tools inject variants via JavaScript in the browser. They are fast to implement and work well for product and cart pages but can have flicker if not configured properly.
• Server-side or theme-based testing: Variants are baked into Liquid templates or run at the server. This approach avoids flicker and is more robust, but often needs developer time and may be limited on checkout pages.

Choose a test platform that tracks conversions and revenue and integrates with your analytics to measure real business impact. For product copy and title tests specifically, tools that work with the Shopify theme and product templates allow you to run many experiments without touching checkout.

Measure return on effort: what counts as success

When trying to reduce cart abandonment Shopify merchants should define success in terms that matter to the business. Common success metrics include:

• Conversion rate: Visitors who complete a purchase divided by total visitors for a page or funnel stage.
• Revenue per visitor (RPV): Total revenue divided by total sessions; this encapsulates changes in average order value.
• Recovered revenue: Revenue generated from cart recovery emails and retargeting, net of incentives and ad spend.
• Average order value (AOV): Watch for trade-offs between conversion rate and AOV.
• Customer lifetime value (CLV): For retention-led models, a slight dip in margin on an initial purchase may be acceptable if CLV increases.

Prioritise experiments that improve RPV and recovered revenue rather than those that only increase clicks or add-to-cart events.

Case study examples and realistic expectations

Small changes can deliver significant wins, but results vary. Examples from stores include:

• A DTC apparel brand that added size guides, improved product imagery and moved shipping cost estimates to the product page; result: a 12 percent rise in add-to-cart conversions and a 9 percent uplift in checkout conversion across mobile users.
• An electronics retailer that enabled PayPal and Apple Pay on the cart page saw a 7 percent increase in mobile checkout completion.
• A niche homewares store A/B tested cart CTA wording and layout; a simpler CTA and clearer returns link produced a 4 percent increase in conversion rate and eliminated a long tail of abandoned carts from new visitors.

These examples underline that both product information and checkout friction matter. Expect incremental improvements rather than overnight miracles; consistently running hypothesis-driven tests compounds into substantial gains over months.

Checklist: a step-by-step plan to reduce cart abandonment on Shopify

Use this checklist to structure your work over the next 30 days:

• Install or review your analytics and session recording tools; ensure order tracking is accurate.
• Analyse funnel drop-offs by device and traffic source to identify priority pages.
• Run page speed audits and fix obvious bottlenecks: large images, third-party scripts, and slow apps.
• Enable guest checkout and express payments; test them on mobile.
• Implement a short cart recovery email sequence and set up UTM tagging to measure recovered revenue.
• Create a backlog of 5 hypothesis-driven A/B tests focussed on product pages and cart pages.
• Run tests with adequate sample sizes and avoid overlapping experiments that target the same funnel stage for the same audience.
• Measure outcomes by revenue and revenue per visitor; iterate on winners and roll back losers.

Conclusion and next steps

Shopify cart abandonment is common but not inevitable. The 70 percent headline number indicates opportunity: small improvements in product information, checkout friction and recovery messaging compound into meaningful revenue. The right approach is diagnostic and iterative: collect funnel data, form hypotheses, run controlled A/B tests and measure business outcomes rather than surface metrics. For many merchants the fastest wins come from clarifying pricing and shipping, simplifying checkout, adding express payment options and improving product copy and imagery.

Next steps: audit your funnel, pick one high-impact test for this week and one recovery sequence to deploy. Track revenue impact and use those learnings to prioritise further tests.

Cart abandonment starts on the product page. Better product copy = fewer abandoned carts. Test your copy with ConvertLab.

Ready to test product titles, descriptions and prices without touching your checkout? ConvertLab helps Shopify merchants run A/B tests on product pages, measure revenue impact and roll out winners. Learn more and install on the Shopify App Store: ConvertLab on the Shopify App Store.