Enhancing User Experience on Websites

7 out of 10 of my projects start with fixing what most people ignore. This includes: - making copy easier to read - making images informational - making product name impactful Simple, but yet forgotten. In this post, using URturms example, I'll be sharing 11 underestimated changes that can increase your website sales. 1. Adding breadcrumbs. Important if you drive ad traffic to the PDP directly. They take shopper to the parent category page. Reducing bounce rate. 2. Adding a badge. Like "Bestseller", "Most Loved", "Few Left". This reassures the shopper that they're making the right decision. 3. Making images easier to swipe. Add a sneak peek of the next image along with navigation dots that show the count. Cap them at 8. 4. Making the product name impactful. Add key USPs. Show your current product name to 10 people. Do they understand what it is? 5. Add a short description below product name. Keep it in 1 line. Highlight it's most important feature here. 6. Consider adding an offer close to price. This motivates the shopper as they see some potential savings or benefit. 7. Highlight key product strengths in bullets or with icons. Avoid sentences. Keep this before the add to cart CTA. 8. Keep your add to cart CTA full width. Don't combine it with quantity or another CTA next to it. Make sure it's readable and prominent. 9. Highlighting shipping time or return policy below the CTA. This solves for common questions - when will I get it? can I return it? 10. Cross-selling complementary products. Like bottoms with tops. Earrings with necklace. Do this close to the add to cart CTA. 11. Adding 'Benefits' to your accordion. This gets a higher click through rate, while helping shoppers understand why they should buy this. Other UX/UI changes I did: - Removed quantity button - Made the information bar non-moving - Removed log-in, moving search next to cart - Changed the font for product name and CTA - Increased font size in places for better readability Found this useful? Let me know in the comments! P.S. If you want to maximize your PDP’s potential, start by understanding your visitor's behavior and the gaps. Get heat maps for your site (Microsoft Clarity is free). Observe what they like to (and don't like to) interact with.

Great marketing is rooted in psychology: - How do your customers buy? And why? - What triggers make them say "YES"? - What are they trying to avoid? You need to understand your buyers' psychology. Otherwise, all your marketing actions will be arbitrary. - "Maybe they will react to this" → Uneducated guess - "I think they bought for this" → Biased analysis Here's the thing; If you had 1 hour to convince someone to buy my offer, you should spend 50 minutes asking questions, and 10 minutes pitching. In order to do this, you should use the IHE process: 1. Run customer Interviews You cannot invent the answers to the questions listed above. Plan at least 5 interviews with customers, and 5 interviews with prospects. Try to understand why and how they buy. 2. List educated Hypotheses Consolidate the answers from your customers, and analyze the patterns. Put a great emphasis on the psychological aspects of their answers. Then turn the elements into hypotheses to be tested. (Example: you identify a strong confirmation bias) 3. Run Experiments to validate hypotheses Plan your experiments in weekly or monthly sprints (give enough time to generate results). Use psychology at the heart of your experiments (scarcity, social proof, loss aversion, authority bias...). (Example: you address the confirmation bias in your homepage H1) *** With this IHE process, psychology concepts become more practical. You go from "I understand psychology" to "I'm using psychology concepts in marketing".

An ecommerce company recently approached my team to do an email audit as they were facing challenges with low open and click-through rates. After analyzing their email account, here are our main recommendations to revive their email marketing channel: 1. Strategic Email Segmentation: Currently, your emails lack personal relevance due to a one-size-fits-all approach. This is a crucial area to address. Action Plan: Implement segmentation based on purchase history, engagement levels, browsing behavior, and demographic information. 2. Personalized Content Creation: Generic content won't cut it. Your audience needs to feel that each email is crafted for them. Action Plan: Develop emails specifically tailored to the different segments. This includes curated product recommendations, personalized offers, and content that aligns with their interests. 3. Subject Line A/B Testing: Your current subject lines aren't doing their job. You need to be implementing ongoing A/B subject line tests, as this is low-hanging fruit to improve your open rates. Action Plan: Regularly test different subject line styles and formats to identify what resonates best with each segment. Keep track of the metrics to inform future campaigns. 4. Mobile Optimization: A significant portion of your audience reads emails on mobile devices. Neglecting this is causing a decrease in your email engagement rates. Action Plan: Ensure all emails are responsive and visually appealing on various screen sizes. Test your emails on multiple devices before sending them out. Additional Campaign Strategies We Recommend: - Launch a Monthly Newsletter: This should include new arrivals, style guides, and user-generated content. It’s an excellent way to keep your brand in the minds of your customers. - Seasonal Campaign Integration: Tailor your campaigns to align with holidays and seasons. This approach can significantly boost engagement and sales during key periods. - Re-Engagement Campaigns: Specifically target subscribers who haven't interacted with your brand recently. Offer them unique incentives to rekindle their interest. Next steps: 1. If you found this helpful, please leave a comment and let me know. 2. If you own/run/work at an Ecommerce company doing at least $1 million in annual revenue, message me so my team can audit your email channel to see if there's a good fit for working together.

More traffic means nothing if you can’t turn visitors into customers. We helped an eCommerce client grow revenue by 72% and 3X’d their organic traffic along the way. Turns out, when you give users a better experience and convert them, Google will naturally send you more free traffic. Here’s exactly how we did it, and how you can too. 👇 1️⃣ Your blog should actively drive sales, not just rank. Find your top-selling products in Google Analytics. Go to Reports → Monetization → Ecommerce purchases. Sort by revenue or units sold. These are your “money” pages. Next, add contextual internal links from relevant blog posts. Link naturally within sentences using descriptive, keyword-rich anchor text. 2️⃣ Add product widgets at strategic points in your content: Place widgets in 3 key areas: - Within articles when discussing related items - Sidebar for consistent visibility - Bottom of posts for subtle promotion Try these proven widget types: - Popular/bestselling products  - Recently viewed items  - New arrivals  - Special offers/discounts  - "People also buy" recommendations Make sure your widgets have:  - Clear, clickable images  - Direct "Add to Cart" buttons when possible  - Quick view functionality 3️⃣ Keep visitors engaged with related posts sections at the end of each article. Not all your visitors will buy right away. Extract SEO value from them using related posts. WordPress users can try Contextual Related Posts plugin, while Shopify stores can use Related Blog Posts Pro. Longer site visits mean more product discovery opportunities. 4️⃣ Never use generic product descriptions. Create unique descriptions at scale with ChatGPT’s (free) Ecommerce SEO Product Description Writer. Feed in your product name, features, and what makes it different. Then enhance the output with customer-focused benefits, clear features, and FAQs. 5️⃣ Structure descriptions into scannable sections: - Brief overview for busy shoppers - Detailed info for researchers - Feature lists with practical benefits - Clean technical specs - How-to sections for complex products The key here is to give Google exactly what it wants - unique content that satisfies user intent AND converts. The result of these tweaks? • 204% increase in organic traffic (9,107 → 27,699 monthly sessions) • 72% increase in monthly revenue • 1.75x more keywords in top 10 positions (2K → 3.5K)

Stop measuring your A/B testing programme solely by 'winning' tests. Experimentation does not generate revenue in and of itself. Rather, it aids you in making more informed decisions about other revenue-generating activities. Hence, it acts as a form of decision-support and risk-management, and needs to be measured in an appropriate manner. Here are some examples of more effective measures and KPIs for experimentation: > What percentage of front-end website changes/features pushed to production were previously validated with experiments? What are the development costs associated with these two groups of tests? > What is the TOTAL potential revenue impact of the ideas evaluated through experiments? This should include not just the positive changes that will make revenue but also significant negative changes where revenue has been saved, plus the revenue impact of any actions taken as a result of learning from any test, regardless of the outcome. Remember, this isn't revenue generated by the testing programme; rather, it's revenue 'at stake' that can be protected through informed decision-making. > How much in development costs has been saved by testing ideas rather than immediately pushing them into production? If done correctly, experimentation is far cheaper and quicker than building in production. This is due to obvious efficiency reasons but also because you should be breaking big ideas down into simpler concepts for the purposes of validation. > On average, how quickly are new ideas transformed into live experiments? Think like a risk management team, not a tactical marketing channel. #cro #experimentation #ecommerce #digitalmarketing #ux #userexperience

It surprises me how many e-commerce brands pretend to offer a personalized storefront, but show the same store to everyone. The attached visual that shows what a modern storefront actually looks like behind the scenes, which is a simple system that reacts in real time. Thought it would be useful to break this down into three stages with the recommended tech stack below: Stage 1: Signals (data in) You capture (live) what’s already happening the moment someone arrives. How they got there, what they’re doing, what device they’re on, and whether they’ve bought before. Typical stack: • Segment or RudderStack for event capture • Shopify events and customer data • Google Tag Manager • Meta / TikTok UTMs for paid context Focus on clean, real-time signals without overengineering identity. Stage 2: Decisions (what to show) Those signals get turned into a simple decision immediately. Which message, which products, which path makes sense for this visitor right now. If it’s not fast enough to change the first screen, it doesn’t count. Typical stack: • Dynamic Yield or Nosto • Vercel edge logic • Cloudflare Workers • Simple rules or light models, not heavy AI Remember, speed beats sophistication. Stage 3: Experience (what changes) The storefront responds on arrival. The hero, first product grid, and primary CTA change instantly so the site feels relevant from the first moment. Typical stack: • Shopify Hydrogen or native Shopify sections • Contentful or Optimizely • Server-side or edge-rendered changes, not client-side flicker Important, personalize above the fold first. A returning high-value customer sees new arrivals and a faster path to checkout. A first-time visitor from paid sees a clearer offer and fewer choices. A deal-driven shopper sees bundles and savings upfront. Everything else comes later. If you want to start without overengineering: • Pick the two audiences that matter most • Personalize only the hero and first product grid • Measure lift on conversion rate and revenue per session • Add complexity only after this works Start simple: focus on one working example that proves the storefront can adapt in real time in a way customers actually feel.

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.

Does putting pricing on your landing page help or kill your funnel? We ran a 60-day A/B test for a client. Real traffic. Real leads. Real consequences. Here’s how it went down: THE HYPOTHESIS: If buyers know the price upfront, they’ll self-qualify. Fewer tire-kickers = less time wasted by sales. That all sounds good but it also means less leads and more "Why are the leads down?!" emails. TEST SETUP: 🔹 Variant A (Control) • Standard demo request page • No pricing anywhere, classic “Talk to sales” vibes 🔹 Variant B (Test) • Identical page but added a full pricing table alongside the form • Clear pricing tiers, no surprises THE RESULTS: • Leads dropped 8.7% on the page • BUT lead to opportunity rates jumped 21.4% • Average deal size held steady • Net pipeline value? Statistically flat—within a ±3% margin Translation: You get fewer leads, but way better ones. The pricing filter scared off the browsers… but it attracted buyers who were ready to talk budget and timeline, not just features. Don’t hide the price. Lead with it. You’ll filter in serious buyers, and your sales team will thank you.

How Implementing Live Chat Transformed Customer Engagement for Client of Mine. Case Study: Improving Customer Engagement and Efficiency with Live Chat Implementation A client of mine, a mid-sized E-commerce business, approached me as they were struggling with a few common pain points: ❌️Slow response times to customer inquiries ❌️Low engagement and high abandonment rates on their website ❌️A high customer service cost due to reliance on phone and email support The solution? Live Chat. After analysing their customer journey, I identified strategic points on their website where live chat could make the most impact: on product pages, checkout pages, and the support section. Here’s how I did it: 1. Selecting the Right Live Chat Tool: We implemented a robust, scalable live chat platform with automation capabilities. 2. Training the Customer Service Team: We provided live chat training focused on quick responses, effective communication, and upselling tactics. 3. Setting Up Chatbots for FAQs: For common questions, we designed a chatbot to handle simple inquiries, allowing human agents to focus on complex issues. The Result: ✔️ Response Times Improved by 80%: ✔️ Increased Sales Conversion by 25%: ✔️ Cost Savings of 30%: ✔️ Customer Satisfaction Score (CSAT) increased to 95%: In just three months, their customer experience and saw measurable improvements in engagement, satisfaction, and sales. Key Takeaway: If your business relies heavily on customer interactions, live chat can be an option to explore. Whether you’re in eCommerce, B2B, or services, adding live chat is one of the most effective ways to evolve. Considering live chat for your business? Let’s chat about how to make it work for you!

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?