How To Use Data To Optimize Ecommerce Checkout Process

I analyzed 150+ ecommerce checkouts this year including luxury giants Garmin, Michael Kors, and Tiffany. What’s shocking is that even billion-dollar brands are bleeding revenue at checkout through amateur mistakes. The forcing account creation before purchase is the #1 killer. My data shows brands offering guest checkout with optional account creation at confirmation seeing 25% higher completion rates without fail. Other costly checkout errors destroying your revenue: • Hiding order summaries (customers abandon when they can't verify purchases) • Cluttering pages with navigation bars (each unnecessary element drives drop-offs) • Using unconventional form fields (cognitive friction kills sales) • Lacking progress indicators (uncertainty breeds abandonment) The best checkout experience provides absolute clarity about where customers are in the process, eliminating hesitation and creating the confidence needed to complete the purchase. Remember: Every second your customer spends thinking is a second they might leave forever.

🚨The greatest drop-off is from Product Details Page To Cart Page, so we must improve our Product Details Page! Not so fast ✋ In today's age of data obsession, almost every company has an analytics infrastructure that pumps out a tonne of numbers. But rarely do teams invest time, discipline & curiosity to interpret numbers meaningfully. I will illustrate with an example. Let's take a simple e-commerce funnel. Home Page ~ 100 users List Page ~ 90 users Product Display Page ~ 70 users Cart Page ~ 20 users Address Page ~ 15 users Payments Page ~12 users Order Confirmation Page ~ 9 users A team that just "looks" at data will immediately conclude that the drop-off is most steep between Product Details Page & Cart Page. As a consequence they will start putting in a lot of fire power into solving user problems on Product Display Page. But if the team were data "curious", would frame hypothesis such as "do certain types of users reach cart page more effectively than others?" and go on to look at users by purchase buckets, geography, category etc and look at the entire funnel end to end to observe patterns. In the above scenario, it's likely that the 20 cart users were power users whilst new & early purchasers don't make it to this stage. The reason could be poor recommendations on the list page or customers are only visiting the product display page to see a larger close up of the product. So how should one go about looking at data ? Do ✅ Start with an open & curious mind ✅ Start with hypothesis ✅ Identify metrics & counter metrics that will help prove/disprove hypothesis ✅ Identify the various dimensions that could influence behaviours - user type, geography, category, device type, gender, price point, day, time etc. The dimensions will be specific to your line of business. ✅ Check for data quality and consistency ✅ Look at upstream and downstream behaviour to see how the behaviour is influenced upstream and what happens to the behaviour downstream. ✅ Check for historical evidence of causality Dont ❌ Look at data to satisfy your bias ❌ Rush to conclude your interpretation ❌ Look at data in isolation - - - TLDR - Be curious. Not confirmed. #metrics #analytics #productmanagement #productmanager #productcraft #deepdiveswithdsk

Crowning a New Term: “Iceberg Metrics” 🧊 ✨ I’m calling it: Iceberg Metrics represent KPIs that only reveal the tip of what’s really happening below the surface. Metrics like abandoned carts seem simple but often mask much more—checkout friction, hidden costs, trust issues, and more. To truly understand and optimize, we need to dig deeper. Here’s how to dive into the “iceberg” of abandoned cart rates: 1. Establish Baseline Metrics: Start by gathering data on current abandoned cart rates, session times, and bounce rates using heat maps and session recordings to see where users drop off. 2. Segment the Audience: Analyze users by behavior (first-time vs. repeat visitors, mobile vs. desktop) and traffic source (organic, paid, email). 3. Experiment Hypotheses: Develop hypotheses for abandonment reasons—shipping costs, checkout friction, distractions, or lack of trust signals—and test them. 4. Run A/B Tests: Test variations like simplifying the checkout process, showing shipping costs earlier, adding trust badges, or retargeting abandoned cart emails. 5. Use Heat Maps & Session Recordings: Examine user behavior in real time. Look for confusion or hesitation, where users hover, and whether they engage with key information. 6. Contextualize Results: Analyze how changes impact overall user flow. Did simplifying checkout help, or did other metrics like bounce rate increase? 7. Ecosystem Approach: Examine how tweaks affect the full journey—from product discovery to checkout—balancing short-term improvements with long-term goals like lifetime value. 8. Iterate: Refine solutions based on experiment findings and continuously optimize the customer journey. This one’s mine, folks! #IcebergMetrics #OwnIt #DataDriven #EcommerceOptimization #NewMetricAlert Cheers, Your cross-legged CAC and CLV buddy 🤗

Checkout optimization used to mean adding more payment methods. Today it’s about shaping the payment journey before friction ever shows up. Fintech Adyen just launched Personalize inside its Uplift suite. The headline feature is real-time Dynamic Identification, trained on trillions of transactions across its network. Why it matters: 37% of shoppers abandon when checkout takes too long. 72% of businesses say transaction fees are pressuring margins. Static checkout flows treat every buyer the same. Modern payment stacks can’t afford that. Personalize adjusts the experience in real time. It can: • Prioritize cost-efficient payment rails • Suppress unnecessary authentication • Surface risk signals before authorization • Route transactions based on identity and context Early data: • 9.4% lower payment costs on eligible traffic in year one of Uplift • 42% reduction in false positives • +1.19% average conversion lift, up to 6% for some merchants • Pilots showing up to 3% lower transaction costs • Tebi: 4.26% cost savings and 0.8% conversion lift This is not incremental CRO. The real shift is architectural. Checkout is becoming a data and feedback loop problem, not a front-end design problem. The platforms that unify acquiring, issuing, risk, and identity inside one system will compound advantages over time. If you’re running payments at scale: Are you optimizing a page… or optimizing a network?

Why Some of the Best-Converting Checkouts Don’t Look Like Checkouts We’ve spent the last decade obsessing over “optimising the checkout page”, tweaking button colours, shuffling fields, shaving milliseconds off load time. Yet the highest-converting commerce flows I see today have no stand-alone page at all. They dissolve the hand-off between browsing and paying until shoppers hardly notice a boundary. Why is that working? – Speed sets the floor. Internal tests at several large merchants echo a recent Mastercard study: every extra 100 ms in payment latency drags conversion down by roughly 1 percent. If the entire journey fits inside the product page or app screen, you scrap the redirect and gain those precious milliseconds. – Stored credentials do the heavy lifting. Network tokens keep card data fresh, PAR keeps it linked, and orchestration decides in real-time which acquirer is most likely to approve. The result: fewer declines, fewer “update your card” pop-ups, higher lifetime value. – Context drives trust. A Deloitte survey found 7 in 10 consumers are more comfortable paying where they already are, a ride-hail app, a food-delivery chat, a social feed, than in a generic web form. When the payment choice is rendered in the same UI language as the rest of the experience, drop-off falls sharply. – Local options appear only when relevant. The best flows don’t show a wall of logos. They detect device location, BIN, and order value, then surface iDEAL in The Netherlands, Pix in Brazil, or ACH in the U.S., all without adding clicks. – Invisible risk controls stay invisible. Device intelligence, behavioural signals, and 3-DS exemptions fire in the background. According to Riskified, merchants that pre-screen for fraud before checkout save up to 14 seconds per order without raising chargebacks. – Failover is automatic. If a wallet token soft-declines, the platform retries with a fallback credential or routes to a secondary processor. Customers never see the rescue act; they just see “Payment successful”. Put simply, modern checkout is less a page and more an intent-to-cash layer woven throughout the journey. It’s why brands like Uber, Amazon, and Alipay convert north of 90 percent on mobile while traditional web carts still leak 60 percent at the final step. So, where do we go from here? I see three priorities for enterprise merchants in 2025: 1. Embed the pay button wherever intent happens, not just at the end. 2. Own your tokens and routing logic; don’t lock them inside one PSP, like with IXOPAY. 3. Measure friction in sub-second increments, because customers already do. What’s your take? Are we ready to retire the standalone checkout page, or will it always have a place? Let me know in the comments. P.S. For more in-depth Payments Strategy check out my newsletter https://lnkd.in/e6eXZrF9

Building your checkout flow is like crafting a sales conversation. Every element either moves customers closer to purchase or creates friction that drives them away. Most DTC brands obsess over ad creative but underestimate checkout design. Here's the truth: A well-designed checkout can lift revenue more than your best-performing ad. 3 critical areas to master: 🥵 Cognitive Load → Every question, field, or decision point in checkout adds mental friction. Your job? Remove unnecessary thinking. If a customer has to calculate free shipping thresholds or wonder about the order’s arrival day, that’s friction. 👍 Trust Signals → First-time buyers need different reassurance than repeat customers. Your checkout should adapt. New customers might need reviews and press features. Loyal customers want their status acknowledged and rewarded. 💎 Value Perception → Shipping costs hit differently at various price points. A $7 shipping fee on a $30 order feels expensive. The same fee on a $100 order? Barely noticeable. The problem is even when brands know these principles, they struggle to implement and test them effectively. That's where smart checkout optimization comes in. At Obvi, we've been methodically testing these elements. Our latest focus is reducing cognitive load around free shipping thresholds (FSTs)... Using PrettyDamnQuick with Avi Moskowitz, we tested adding a simple note showing exactly how much more a user needed for free shipping. No complicated math for customers. No uncertainty about what the threshold is or how to reach it... The results after 25 days →  • +$0.78 more revenue per customer (meaning the messaging IS pushing people to add more to their cart) • Better conversion rates • Higher average order values across the board This nicely illustrates why checkout optimization matters. One small friction point removed = real revenue impact.

Cart Abandonment Was Killing Sales. Here’s how we cut it by 37% in 28 days. After 1 month of deep CRO testing across 3 brands, Here are 4 checkout tweaks I wish we did sooner: 1. Eliminate decision fatigue upfront Most abandoned carts start before checkout even begins. • Consolidate product variants • Pre-select popular choices • Remove surprise fees before final step 🧠 Clarity wins more than cleverness. 2. Shortened the checkout flow Every extra field = more friction. We cut it to 2 pages: Page 1: Shipping + email Page 2: Payment + order review → Result: 11% boost in completed checkouts 3. Added real-time shipping transparency Static “Shipping calculated at checkout” killed trust. We integrated dynamic rates + estimated delivery dates. Conversions jumped, especially for first-time buyers. 4. Used urgency without being pushy No fake countdown timers. Just: “Orders ship by 2PM today” “Only 4 left at this price” (live inventory) → Result: +7% conversion lift The most underrated? ➡️ Dynamic shipping transparency. Trust = the missing lever most brands ignore. What’s working now: • Mobile-first design (80%+ of checkouts are mobile) • Post-purchase upsells, not pre-checkout clutter • 1-click checkout integrations like Shop Pay, or PayPal Save this post if you’re: • An ecommerce brand with 100+ monthly orders • A DTC founder struggling with abandoned carts • Scaling paid ads but leaking sales at checkout What’s your #1 checkout leak right now? 👇

Last month, a jewelry client increased their conversion rate by 32.7% and boosted revenue by 35.7% after implementing a CRO program based on shopper behavioral data in GA4. When they started with us back in September they had almost no data in GA4, and they had some concerns about the investing in Google Analytics implementation: ❌ "What is this going to tell me that my TripleWhale and Northbeam doesn't?" ❌ "Even if I have the insights, who is going to run CRO? Me?!!" ❌ "What if engagement increases but doesn’t translate into sales?" All valid concerns… But we showed them how behavioral research guides the way to greater conversions with statistics and an engineering approach increasing conversions —just by collecting the right data and using our AI to analyze behavior and get test suggestions. So we got to work: 🔹 Implemented tracking on the most important shopping behaviors 🔹 Ran through analysis of what shopping behaviors were correlated to transations 🔹 A/B tested the visibility of features ENCOURAGING those behaviors on PLP pages, measuring whether early exposure influenced conversion rates 🔹 Measured revenue impact to ensure I wasn’t just increasing engagement, but driving real sales Since we did that (+ some consistency), they’ve: ✅ Increased conversion rates +32.7% ✅ Generated 35.7% more revenue in that category. ✅ Built a repeatable, data-backed strategy for using what we learned across the entire website. If you're an eCommerce brand struggling with low conversion rates or uncertain about how to use shopper behavior effectively to run your CRO program. 📩 comment below, and I’ll share with you our templates for how we did it! #EcommerceGrowth #Clickvoyant #ConversionOptimization #googleAnalytics #MarketingAnalytics 🚀

Here’s how a customer we work withincreased ROAS 99% with a data-led approach And how you can do the same for your brand by cutting fluff & focusing on the metrics that move the needle. These are the exact 5 steps they used: ↳ Track the right metrics They used PenPath’s Purchase Intent Rate (PIR) dashboard as a guiding metric. Instead of relying solely on ROAS or CVR, they analyzed customer buying signals: - Adding to cart - Begin Checkout - Site searches - Email signups ↳ Clean up campaign data Set up clean campaign naming conventions to make data analysis easy & actionable. Specifically making things segmented by prospecting, retargeting, and by product category. ↳ Optimize by funnel stage Measured PIR by source, medium, and campaign to understand baselines for each stage of the funnel to measure interest for each traffic source and by product categories. ↳ Focus on what’s working For TOF effort with high PIR, they scaled or kept them even when ROAS was not performing and cut the rest. For BOF, they cut any campaign with low ROAS or PIR. This is an over simplification but that was the general approach. ↳ Scale high-intent audiences Lastly, they used purchase intent data to created improved retargeting audiences on Google and Meta. The Results? ✅️ ROAS skyrocketed from 1.35x to 2.69x (+99.555) in three months ✅️ Ad spend increased by 243% --- with no wasted dollars Pro Tip: Map your customer journey with intent-driven metrics. Focus on actions that align with each stage of your funnel (TOF, MOF, BOF) to uncover where customers drop off—and where to double down on winning strategies. If you’re an ecommerce decision maker, what data have you used to scale ROAS as quickly as possible? #Dataanalysis #Ecommercetips #Adspend #Ecommercesolutions