Every mid-market retailer sees the same cart abandonment rate—hovering somewhere between 68% and 75%—and every CFO asks the same question: "Why are we spending on checkout optimization when the industry benchmark is the industry benchmark?" The objections to checkout work are predictable, and most of them are rooted in a misunderstanding of where the lift actually comes from. This article works through the objections growth-focused retail leaders hear most often and the direct answers that get checkout optimization projects funded.
The retailers winning on conversion aren't the ones who treat cart abandonment as an immutable law of eCommerce. They're the ones who dug into the specific friction patterns of their specific checkout and shipped measurable improvements. At Bemeir, we've run checkout optimization engagements where conversion lift on the checkout funnel hit 15-25%, not by reinventing checkout but by removing the specific frictions that were invisible until someone measured them.
"Industry Benchmark Cart Abandonment Is 70% — What Can We Actually Do?"
This is the "fatalism" objection, and it's built on a math error. The 68-75% industry benchmark from Baymard Institute measures the percentage of sessions that reach the cart and then don't complete checkout. That number includes sessions where users were comparison shopping, getting shipping estimates, saving items for later, or researching without intent to buy right now. It's not a measure of "people who wanted to buy and didn't."
The more useful metric is "checkout completion rate"—the percentage of users who start checkout (hit the first checkout step) and complete it. That number is typically 50-65% for mid-market retailers, and it's where meaningful optimization lives. Moving checkout completion from 55% to 65% is a 18% lift on checkout-initiated revenue. For a retailer doing $20M through checkout, that's $3.6M annually.
The industry benchmark isn't a ceiling. It's an average that includes a lot of retailers leaving significant revenue on the table because they accepted the average.
"We Already Optimized Checkout Two Years Ago"
Common and almost always stale. Checkout patterns that worked in 2023 are often already underperforming in 2025 because payment methods, device patterns, and buyer expectations have all shifted. Apple Pay and Google Pay adoption has nearly doubled on mobile. Buy-now-pay-later has become table stakes for certain product categories. Address autocomplete patterns have gotten more sophisticated. Checkout that was "optimized" two years ago probably has three or four specific friction points that weren't on the radar then.
The cleanest diagnostic is to run funnel analytics against current sessions and identify the biggest dropoff steps. For most retailers who haven't touched checkout in 18+ months, there's at least one step with a 30%+ dropoff that wouldn't survive a modern optimization pass. At Bemeir, we start every checkout engagement with that funnel analysis—not because we assume things are broken, but because we've yet to find a checkout that couldn't improve after two years untouched.
"Checkout Changes Risk Breaking Conversion"
This objection is valid and should be respected. A bad checkout change can absolutely damage conversion. The answer isn't to avoid changes—it's to ship them behind A/B tests with real statistical significance and monitored rollout patterns.
The retailers who ship checkout changes well do three things. First, they run optimization work in staged tests: small percentage of traffic, watch conversion, expand if positive, roll back if negative. Second, they establish guard rail metrics (checkout abandonment, payment success rate, form completion time) that trigger automatic rollback if they degrade. Third, they ship one change at a time so they can attribute the cause of any conversion movement.
This operational discipline is not trivial. It requires A/B testing infrastructure, analytics instrumentation, and team process. Retailers without that foundation are right to be cautious about checkout changes. The answer is to invest in the testing infrastructure first, then make optimization a continuous practice. Magento, Shopify, and BigCommerce all support testing patterns that make this manageable.
"Our Checkout Friction Is Coming From Payment Processor, Not Our Site"
Sometimes true, usually incomplete. Payment processor friction (declined cards, 3DS challenges, timeout errors) is real and causes measurable checkout drops. But it's almost never the only friction. Retailers who blame the processor for everything usually have three or four other friction points that are bigger contributors.
The useful diagnostic is to segment dropoff by step. If dropoff is concentrated at the payment submission step, processor friction is a likely contributor. If dropoff is distributed across multiple steps—shipping, address entry, account creation—the processor isn't the main problem. Most mid-market retailers have distributed dropoff patterns, which means distributed optimization opportunities.
That said, payment processing friction deserves investigation. Stripe's 2024 payment performance data shows that network-level auth rates vary by 10-15 points across processors for the same card portfolios. If your decline rate is high, processor-level work (retry logic, network tokenization, smart routing) can produce meaningful lift independent of UX optimization.
"Guest Checkout vs. Account Creation — Isn't That a Solved Question?"
The conventional wisdom—"always offer guest checkout"—is mostly right but incomplete. Baymard's research does show that forced account creation is a major friction point. But the way guest checkout is presented matters enormously.
The common failure pattern: retailers add guest checkout as an afterthought, present it as a secondary option after "create account," and lose the users who would have guest checked out because they don't notice the option. The successful pattern: present "continue as guest" as equal or primary to "create account," and offer "create account at the end" as a one-click post-purchase option. That framing typically moves checkout completion 3-6%.
Retailers who've already added guest checkout but haven't reviewed the framing in a year or two usually have a modest but real lift available from a simple presentation update.
Checkout Optimization Objections vs. Reality
| Objection | Underlying Truth | Resolution Pattern |
|---|---|---|
| 70% is the industry baseline | Mixes intent and non-intent sessions | Measure checkout completion rate instead |
| Already optimized two years ago | Payment, device, expectation patterns have shifted | Run funnel diagnostic, pick 2-3 top friction points |
| Changes risk conversion | Bad changes can hurt | A/B testing infrastructure + guard rail metrics |
| Processor is the problem | Partially true, rarely complete | Segment dropoff by step, work multiple levers |
| Guest checkout is solved | Framing affects adoption more than existence | Audit presentation; move "guest" to primary option |
Where the Revenue Actually Lives
The specific friction patterns that consistently produce meaningful checkout conversion lift:
- Removing unnecessary form fields (often 15-20% of the form is optional data that increases friction)
- Implementing address autocomplete (Google Places, Loqate, SmartyStreets — reduces form time 40-60%)
- Adding Apple Pay, Google Pay, and wallet support for one-tap checkout
- Fixing validation patterns (inline validation, clear error messaging, graceful recovery from errors)
- Shortening multi-page checkout to single-page or two-page where feasible
- Adding buy-now-pay-later options for relevant AOV ranges
- Fixing mobile-specific friction (keyboard handling, input types, viewport behavior)
Each of these is specific, measurable, and achievable through standard engineering work on Adobe Commerce, Shopify, or BigCommerce. At Bemeir, we've shipped checkout optimization programs that bundled three to six of these improvements and consistently produced 8-15% checkout completion lift within a quarter.
The Strategic Case CFOs Miss
Beyond any single objection, the strategic case for checkout optimization is just revenue math. A retailer doing $25M in eCommerce with a 58% checkout completion rate is leaving meaningful money on the table that a competitor with a 67% rate is capturing. The ROI on checkout optimization work is typically 5-15x the engineering investment in the first year, and the gains compound as ongoing testing infrastructure makes future optimization cheaper.
The retailers who accept the industry benchmark keep leaving that money on the table. The retailers who push past it are the ones doing the work the objections tried to stop. The objections are worth answering honestly—and then the work starts.
