User Experience and Conversion Rates

A client came to us frustrated. They had thousands of website visitors per day, yet their sales were flat. No matter how much they spent on ads or SEO, the revenue just wasn’t growing. The problem? Traffic isn’t the goal - conversions are. After diving into their analytics, we found several hidden conversion killers: A complicated checkout process – Too many steps and unnecessary fields were causing visitors to abandon their carts. Lack of trust signals – Customer reviews missing on cart page, unclear shipping and return policies, and missing security badges made potential buyers hesitate. Slow site speeds – A few-second delay was enough to make mobile users bounce before even seeing a product page. Weak calls to action – Generic "Buy Now" buttons weren’t compelling enough to drive action. Instead of just driving more traffic, we optimized their Conversion Rate Optimization (CRO) strategy: ✔ Simplified the checkout process - fewer clicks, faster transactions. ✔ Improved customer testimonials and trust badges for credibility. ✔ Improved page load speeds, cutting bounce rates by 30%. ✔ Revamped CTAs with urgency and clear value propositions. The result? A 28% increase in sales - without spending a dollar more on traffic. More visitors don’t mean more revenue. Better user experience and conversion-focused strategies do. Does your ecommerce site have a traffic problem - or a conversion problem? #EcommerceGrowth #CRO #DigitalMarketing #ConversionOptimization #WebsiteOptimization #AbsoluteWeb

Most teams pick metrics that sound smart… But under the hood, they’re just noisy, slow, misleading, or biased. But today, I'm giving you a framework to avoid that trap. It’s called STEDII and it’s how to choose metrics you can actually trust: — ONE: S — Sensitivity Your metric should be able to detect small but meaningful changes Most good features don’t move numbers by 50%. They move them by 2–5%. If your metric can’t pick up those subtle shifts , you’ll miss real wins. Rule of thumb: - Basic metrics detect 10% changes - Good ones detect 5% - Great ones? 2% The better your metric, the smaller the lift it can detect. But that also means needing more users and better experimental design. — TWO: T — Trustworthiness Ever launch a clearly better feature… but the metric goes down? Happens all the time. Users find what they need faster → Time on site drops Checkout becomes smoother → Session length declines A good metric should reflect actual product value, not just surface-level activity. If metrics move in the opposite direction of user experience, they’re not trustworthy. — THREE: E — Efficiency In experimentation, speed of learning = speed of shipping. Some metrics take months to show signal (LTV, retention curves). Others like Day 2 retention or funnel completion give you insight within days. If your team is waiting weeks to know whether something worked, you're already behind. Use CUPED or proxy metrics to speed up testing windows without sacrificing signal. — FOUR: D — Debuggability A number that moves is nice. A number you can explain why something worked? That’s gold. Break down conversion into funnel steps. Segment by user type, device, geography. A 5% drop means nothing if you don’t know whether it’s: → A mobile bug → A pricing issue → Or just one country behaving differently Debuggability turns your metrics into actual insight. — FIVE: I — Interpretability Your whole team should know what your metric means... And what to do when it changes. If your metric looks like this: Engagement Score = (0.3×PageViews + 0.2×Clicks - 0.1×Bounces + 0.25×ReturnRate)^0.5 You’re not driving action. You’re driving confusion. Keep it simple: Conversion drops → Check checkout flow Bounce rate spikes → Review messaging or speed Retention dips → Fix the week-one experience — SIX: I — Inclusivity Averages lie. Segments tell the truth. A metric that’s “up 5%” could still be hiding this: → Power users: +30% → New users (60% of base): -5% → Mobile users: -10% Look for Simpson’s Paradox. Make sure your “win” isn’t actually a loss for the majority. — To learn all the details, check out my deep dive with Ronny Kohavi, the legend himself: https://lnkd.in/eDWT5bDN

Conversion optimization pros won't like this: but listening to your users is more valuable than 90% of the experiments I review. I've made this mistake too. My teams spent years running dozens of "high-impact" experiments to improve our signup rate. It helped, but we knew something was missing. Then, we started running a survey on the high-intent pages of our site that changed everything... The question was simple: "Hey, thanks for visiting the site. Mind sharing what's stopping you from creating a free account today?" But the answers were super helpful. "I don't understand the product" "Not sure if I'm your ICP" "The pricing model is confusing" "I need to see what it looks like first" "Can't figure out if you solve for [specific use case]" Some were painful to read. But they refocused us on solving the right problems for our users. Instead of running blind experiments based on what WE thought the problem was, we started brainstorming new impactful ways to improve our conversions: copy changes, video updates, image adjustments, page layout changes - based on the THEIR feedback. Not sure how to take your signup rate to the next level? Try asking some flavor of this question. You’ll get some incredible insights. PS we used Hotjar | by Contentsquare to run the survey, but there's plenty of other tools out there to do this. It's about getting input from real people. That's where the magic happens.

Here's the exact framework our clients use to tie UX research directly to revenue. It's called the I.M.P.A.C.T method: 👉 I - Identify high-friction touchpoints Systematically gather data on where customers struggle most in your product journey. Focus on high-traffic areas first. 👉 M - Measure the business cost Calculate the direct cost of each friction point: - Conversion drop-offs - Support ticket volume - Churn related to specific features 👉 P - Prioritize by revenue potential Rank issues by potential revenue impact, not just severity or ease of fix. 👉 A - Act with evidence-based solutions Design solutions based on actual user behavior, not assumptions. 👉 C - Communicate in business terms Present findings as "This issue is costing us $X per month" rather than "Users are confused by this flow." 👉 T - Track improvements continuously Measure the before/after impact of changes in business terms. With this, you can move the perception of a UX team to a strategic partner When you can say "We increased conversion by 22% through research-driven changes," executives listen differently Does your team have a framework for tying research to revenue? I'd love to hear about it!

👀 Lessons from the Most Surprising A/B Test Wins of 2024 📈 Reflecting on 2024, here are three surprising A/B test case studies that show how experimentation can challenge conventional wisdom and drive conversions: 1️⃣ Social proof gone wrong: an eCommerce story 🔬 The test: An eCommerce retailer added a prominent "1,200+ Customers Love This Product!" banner to their product pages, thinking that highlighting the popularity of items would drive more purchases. ✅ The result: The variant with social proof banner underperformed by 7.5%! 💡 Why It Didn't Work: While social proof is often a conversion booster, the wording may have created skepticism or users may have seen the banner as hype rather than valuable information. 🧠 Takeaway: By removing the banner, the page felt more authentic and less salesy. ⚡ Test idea: Test removing social proof; overuse can backfire making users question the credibility of your claims. 2️⃣ "Ugly" design outperforms sleek 🔬 The test: An enterprise IT firm tested a sleek, modern landing page against a more "boring," text-heavy alternative. ✅ The Result: The boring design won by 9.8% because it was more user friendly. 💡 Why It Worked: The plain design aligned better with users needs and expectations. 🧠 Takeaway: Think function over flair. This test serves as a reminder that a "beautiful" design doesn’t always win—it’s about matching the design to your audience's needs. ⚡ Test idea: Test functional designs of your pages to see if clarity and focus drive better results. 3️⃣ Microcopy magic: a SaaS example 🔬 The test: A SaaS platform tested two versions of their primary call-to-action (CTA) button on their main product page. "Get Started" vs. "Watch a Demo". ✅ The result: "Watch a Demo" achieved a 74.73% lift in CTR. 💡 Why It Worked: The more concrete, instructive CTA clarified the action and benefit of taking action. 🧠 Takeaway: Align wording with user needs to clarify the process and make taking action feel less intimidating. ⚡ Test idea: Test your copy. Small changes can make a big difference by reducing friction or perceived risk. 🔑 Key takeaways ✅ Challenge assumptions: Just because a design is flashy doesn’t mean it will work for your audience. Always test alternatives, even if they seem boring. ✅ Understand your audience: Dig deeper into your users' needs, fears, and motivations. Insights about their behavior can guide more targeted tests. ✅ Optimize incrementally: Sometimes, small changes, like tweaking a CTA, can yield significant gains. Focus on areas with the least friction for quick wins. ✅ Choose data over ego: These tests show, the "prettiest" design or "best practice" isn't always the winner. Trust the data to guide your decision-making. 🤗 By embracing these lessons, 2025 could be your most successful #experimentation year yet. ❓ What surprising test wins have you experienced? Share your story and inspire others in the comments below ⬇️ #optimization #abtesting

Generative AI surveys: where your feedback is interactive, valued, and promptly discarded. But hey, at least it’s efficient! Sorry, I know it’s a bit early to be snarky. Seriously though, closing the loop with your customers on their feedback - solicited or unsolicited - is a game changer. Start by integrating customer signals/data into a real-time analytics platform that not only surfaces key themes, but also flags specific issues requiring follow-up. This is no longer advanced tech. From there, create a workflow that assigns ownership for addressing the feedback, tracks resolution progress, and measures outcomes over time. With most tech having APIs for your CRM, also not a huge lift to set up. By linking feedback directly to improvement efforts, which still requires a human in the loop, and closing the loop by notifying customers when changes are made, you transform a simple data collection tool into a continuous improvement engine. Most companies are not taking these critical few steps though. Does it take time, effort, and money? Yes it does. Can it help you drive down costs and drive up revenue? Also, a hard yes. The beauty of actually closing the loop is that the outcomes can be quantified. How have you seen closing the loop - outer, inner, or both - impact your business? #cx #surveys #ceo

founder learnings! part 8. A/B test math interpretation - I love stuff like this: Two members of our team (Fletcher Ehlers and Marie-Louise Brunet) - ran a test recently that decreased click-through rate (CTR) by over 10% - they added a warning telling users they’d need to log in if they clicked. However - instead of hurting conversions like you’d think, it actually increased them. As in - Fewer users clicked through, but overall, more users ended up finishing the flow. Why? Selection bias & signal vs. noise. By adding friction, we filtered out low-intent users—those who would have clicked but bounced at the next step. The ones who still clicked knew what they were getting into, making them far more likely to convert. Fewer clicks, but higher quality clicks. Here's a visual representation of the A/B test results. You can see how the click-through rate (CTR) dropped after adding friction (fewer clicks), but the total number of conversions increased. This highlights the power of understanding selection bias—removing low-intent users improved the quality of clicks, leading to better overall results.

I increased conversion rates by 71% for a wellness brand in 2 weeks by optimizing ‘the 3 C’s’ of a landing page. At the end of the day, even if you have really strong data and a high performing creative… If you’re not optimizing the web experience (especially on mobile) your conversion rates aren’t going to look very impressive. We’ve helped guide 100’s of our clients from a CRO perspective on what to do as far as their mobile web experience. These are the 3 most important things to think about when it comes to optimizing this component of the user journey: 1️⃣ Continuity Think about the landing page as an extension of the ad content. You absolutely have to make it a priority that there is continuity between the two. They clicked on your ad because they were interested in the exact content of the ad. So if everything looks, feels, and sounds different when they get to the landing page, they’re going to get confused and click off as fast as they can reach their cursor to the X button. Capitalize on their interest by keeping every variable consistent. 2️⃣ Content Like I mentioned previously, the user clicked on the ad because they were interested in hearing more. That’s why making sure your landing page has every single piece of information there is to know about the service is crucial. You don’t want to give them any reason to NOT convert. So: - Handle every objection - Highlight every benefit/feature - Make sure they understand everything about the process. 3️⃣ Call to action Make it easy for the consumer to progress on the page. You’d be surprised by how many people screw this up. They have a bunch of interested people visit the landing page, ready to buy… Just for them to click off because the CTA wasn’t clear enough. Tell them exactly what to do, and where to go if they want to proceed with the purchase. If you can really nail these 3 when it comes to this step in the customer journey, you’re going to convert a lot more of that traffic that you worked so hard to get with your ads. Again, the three C’s of landing pages: 1️⃣ Continuity 2️⃣ Content 3️⃣ Call to action Remember them and watch your conversion rate skyrocket.

What CR doesn’t tell you But 7 components do You fixed the Conversion Rate, but nothing changed. Because CR is just the tip of the iceberg. It doesn’t explain the customers' journey. And definitely not the drop-offs. With Nick Valiotti, PhD we mapped 7 elements of conversion that reveal where your funnel actually leaks. That's what's under the water: 1/ 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁 𝗥𝗮𝘁𝗲 = Product page views / Sessions Shows if users are landing on high-interest products or generic pages. 2/ 𝗖𝗮𝗿𝘁-𝘁𝗼-𝗩𝗶𝗲𝘄 𝗥𝗮𝘁𝗲 = Add to carts / Product views Reveals product appeal + pricing clarity. 3/ 𝗖𝗮𝗿𝘁 𝗢𝗽𝗲𝗻 → 𝗖𝗵𝗲𝗰𝗸𝗼𝘂𝘁 𝗦𝘁𝗮𝗿𝘁 = Checkout starts / Carts opened Do people commit after opening the cart? 4/ 𝗦𝗵𝗶𝗽𝗽𝗶𝗻𝗴 𝗠𝗲𝘁𝗵𝗼𝗱 → 𝗣𝘂𝗿𝗰𝗵𝗮𝘀𝗲 = Purchases / Shipping method selected Highlights issues with delivery cost, speed, or trust. 5/ 𝗣𝗮𝘆𝗺𝗲𝗻𝘁 𝗠𝗲𝘁𝗵𝗼𝗱 → 𝗣𝘂𝗿𝗰𝗵𝗮𝘀𝗲 = Purchases / Payment method selected Do people quit after choosing how to pay? 6/ 𝗣𝗿𝗼𝗺𝗼 𝗖𝗼𝗱𝗲 → 𝗣𝘂𝗿𝗰𝗵𝗮𝘀𝗲 = Purchases / Promo code applied Reveals whether discounts drive actual commitment. 7/ 𝗣𝘂𝗿𝗰𝗵𝗮𝘀𝗲-𝘁𝗼-𝗩𝗶𝗲𝘄 𝗥𝗮𝘁𝗲 = Purchases / Product views The real conversion beyond CR. These metrics tell you why CR changed. Not just that it did. 🤓 Save this if you want to audit your funnel like a pro

Align your UX metrics to the business KPIs. We've been discussing what makes a KPI in our company. A Key Performance Indicator measures how well a person, team, or organization meets goals. It tracks performance so we can make smart decisions. But what’s a Design KPI? Let’s take an example of a design problem. Consider an initiative to launch a new user dashboard to improve user experience, increase product engagement, and drive business growth. Here might be a few Design KPIs with ways to test them: →  Achieve an average usability of 80% within the first three months post-launch. Measurement: Conduct user surveys and collect feedback through the dashboard's feedback feature using the User Satisfaction Score. →  Ensure 90% of users can complete key tasks (e.g., accessing reports, customizing the dashboard) without assistance. Measurement: Conduct usability testing sessions before and after the launch, analyzing task completion rates. →  Reduce the average time to complete key tasks by 20%. Measurement: Use analytics tools to track and compare time spent on tasks before and after implementing the new dashboard. We use Helio to get early signals into UX metrics before coding the dashboard. This helps us find good answers faster and reduces the risk of bad decisions. It's a mix of intuition and ongoing, data-informed processes. What’s a product and business KPI, then? Product KPI: →  Increase MAU (Monthly Active Users) by 15% within six months post-launch. Measurement: Track the number of unique users engaging with the new dashboard monthly through analytics platforms. →  Achieve a 50% feature adoption rate of new dashboard features (e.g., customizable widgets, real-time data updates) within the first quarter. Measurement: Monitor the usage of new features through in-app analytics. Business KPI: → Drive a 5% increase in revenue attributable to the new dashboard within six months. Measurement: Compare revenue figures before and after the dashboard launch, focusing on user subscription and upgrade changes. This isn't always straightforward! I'm curious how you think about these measurements. #uxresearch #productdiscovery #marketresearch #productdesign