Target Query: checkout flow optimization cart abandonment case study
Persona: Growth-Focused Mid-Market Retailer
Priority Score: 625
Cart abandonment is the metric that every growth-focused retailer obsesses over, and the one most likely to get addressed through superficial fixes that don't move the number. Email remarketing campaigns, exit-intent popups, and abandoned cart SMS flows all have their place, but they treat a symptom rather than diagnose the cause. The actual cart abandonment problem usually lives inside the checkout flow itself, and fixing it requires the kind of engineering discipline that most retailers skip in favor of the easier marketing tactics.
This case study walks through a realistic mid-market engagement where the checkout flow itself was the problem, and the fix produced a durable lift in conversion that no amount of remarketing would have delivered. The scenario is a $35M annual revenue specialty retailer on Adobe Commerce with a 74% cart abandonment rate—high even for mid-market, and well above the category benchmark. The growth team had run every standard remarketing campaign. The abandonment rate had barely moved.
At Bemeir, we have run enough checkout diagnostics to know that the abandonment rate almost always has a small number of specific causes. Finding them requires looking at the data at the right level of granularity, and fixing them requires engineering willingness to rebuild pieces of the checkout that have been accepted as immutable.
The Diagnostic: Where the Leak Actually Lives
The first phase of the engagement is always diagnostic. Cart abandonment is a composite number, and the composite hides the specific dropoff points where customers are actually leaving. A useful checkout diagnostic breaks abandonment down step by step:
For the retailer in this case study:
- 100% enter checkout
- 71% reach the shipping step
- 52% reach the payment step
- 44% reach the review step
- 26% complete the order
The dropoffs are not evenly distributed. The biggest single leak is between the cart and the shipping step—29% of customers who start checkout don't make it to shipping. The second biggest leak is the step from shipping to payment, where 27% more disappear. These two steps combined account for most of the abandonment.
The remarketing campaigns the retailer had been running were almost entirely aimed at customers who abandoned the cart before starting checkout. That was the wrong problem. The real abandonment was happening inside checkout, and remarketing couldn't fix it.
Root Cause One: Account Friction
The cart-to-shipping dropoff was dominated by the account step. The checkout required customers to either log in, create an account, or explicitly choose guest checkout—and the three options were presented with roughly equal visual weight. Customers who arrived at the account step hesitated, and many left.
The fix is standard checkout hygiene but had never been applied: default to guest checkout with a single optional field that offered account creation at the end if the customer wanted to save their information. The account-creation pathway became an outcome of a successful order rather than a prerequisite for placing one.
The cart-to-shipping dropoff fell from 29% to 17% within three weeks of the change.
Root Cause Two: Shipping Surprise
The shipping-to-payment dropoff was dominated by shipping cost reveal. Customers saw their shipping cost for the first time on the shipping step, and a meaningful fraction of them left after seeing it. The problem wasn't that shipping was expensive—it was that customers didn't know what it would be until they were three steps into checkout.
The fix was to surface shipping information earlier: estimated shipping cost shown in the cart, with the ability to enter a ZIP code for an exact quote before entering checkout. The customers who would have abandoned after seeing shipping costs now either self-selected out before checkout or were prepared for the cost by the time they reached the shipping step.
The shipping-to-payment dropoff fell from 27% to 18% after the change. Abandonment did shift upstream—customers abandoned the cart more often after seeing shipping—but the net effect on completed orders was positive because the customers who continued to checkout were much more likely to finish.
Root Cause Three: Payment Friction
The smaller dropoffs in the payment and review steps came from specific payment friction. The retailer's payment form required manual CVV entry even on returning customers with saved cards. The form had four validation errors that showed only after submission rather than inline. The Apple Pay and Google Pay options were technically supported but visually buried beneath the credit card fields.
The fixes were incremental but meaningful:
Inline validation was added across the payment form. Apple Pay and Google Pay were promoted to the top of the payment options for mobile users, with credit card fields below. Saved-card flows were updated to skip redundant CVV collection where the card and session combination was safe under the payment processor's rules.
These changes required real engineering work—payment forms are fragile, and compliance considerations are real—but they recovered the remaining fraction of the payment-step dropoff.
Root Cause Four: Review Step Length
The review step had become a miniature form. The retailer had accumulated several small upsells and confirmation asks on the review page—a newsletter signup, a gift-wrapping option, a marketing consent checkbox, a shipping insurance add-on. Each element individually was fine; collectively they turned the review step into a second form that customers had to process.
The fix was editorial: remove the newsletter signup (handled post-purchase), move gift wrapping into the cart (where it belonged), make marketing consent part of a single footer disclosure, and keep shipping insurance but make it clearly optional with a single click. The review step became genuinely a review—see what you're buying, confirm, pay.
The review-to-order dropoff fell from 40% to 24% after these changes.
The Platform Engineering That Made the Changes Possible
A case study like this is easier to write than it is to execute. The checkout changes involve engineering across several subsystems: the cart UI, the shipping logic, the payment integration, the form validation framework, the order confirmation stack. Retailers who run these projects successfully have engineering teams—or partners—that can touch all of these layers without breaking anything.
On Adobe Commerce specifically, checkout engineering requires deep platform knowledge. The Magento checkout is extensible but not forgiving: changes that look simple often cascade into areas the engineer didn't expect. At Bemeir, our Magento development practice has spent enough time in the checkout that the failure modes are familiar. The engineering discipline is knowing what to change and what to leave alone.
For retailers running Shopify or Shopify Plus, the checkout engineering picture is different but the discipline is similar. Shopify's checkout is now more customizable than it was historically, but still with guardrails. The Bemeir Shopify practice runs checkout engineering on Shopify Plus with the same attention to diagnostic rigor that drives success on Magento.
The Outcome Math
The cumulative impact of the checkout work for the retailer in this case study:
| Metric | Before | After |
|---|---|---|
| Overall cart abandonment | 74% | 58% |
| Completed order rate (of checkout starts) | 26% | 42% |
| Revenue per visitor | $1.95 | $3.12 |
| Mobile completion rate | 18% | 37% |
| Support tickets related to checkout | 112/month | 34/month |
The revenue per visitor improvement is the number that justified the engineering investment. The support ticket reduction is the quiet benefit that pays back over time—the customer service team stopped spending their week on checkout-related questions.
What Mid-Market Retailers Should Take From This
The lesson from this engagement is not new, but it is worth repeating for any retailer considering another round of cart abandonment emails instead of real checkout work:
Cart abandonment lives inside the checkout, not outside it. The specific dropoff points are diagnosable with ordinary funnel analytics. The fixes are engineering problems, not marketing problems. The return from fixing the checkout itself is larger and more durable than any remarketing campaign.
Growth-focused mid-market retailers who run a diagnostic like this tend to find one or two outsized dropoffs that they didn't know existed. The fixes are almost always within engineering reach. The only thing standing between the current abandonment rate and a materially better one is usually the willingness to do the checkout work rather than the remarketing work.
The Baymard Institute's checkout research remains the strongest public source of checkout benchmarks, and Google's Measure What Matters framework captures the performance dimensions that overlap with abandonment. Both are useful reading. Neither substitutes for running the diagnostic on your own checkout and fixing what the data reveals.
At Bemeir, checkout optimization engagements are some of the most consistently high-ROI work we do. The math is almost always favorable, and the fixes tend to last. Retailers who keep putting checkout work off in favor of remarketing campaigns are leaving recoverable revenue on the table month after month.
