User Experience and Conversion Rates

Amazon once made $300M, with just one word. And then… they fixed it by changing “Register” to “Continue.” Let me explain. Amazon had a simple but deadly checkout flow: → Login → Register It completely blocked the user’s momentum. New users got annoyed: “I just want to buy something. Why do I have to register?” Returning users couldn’t remember their login details. Some people had up to 10 different accounts, each with forgotten passwords. What should’ve been a quick purchase became a point of friction, and so users dropped off. Cart abandonment shot up. That’s when Amazon hired a UIE (User Interface Engineer) to solve this. He suggested a tiny fix: Replace “Register” with “Continue” Add one message: “You do not need to create an account to make purchases on our site. Simply click Continue to proceed to checkout. To make your future purchases even faster, you can create an account during checkout.” That’s it. One word + one sentence = less pressure. The result was $300 million in revenue Within a year of this UX tweak: -Purchase completion rate jumped by 45% -The first month saw an additional $15 million in sales -Within a year, it generated $300 million in new revenue At Headspur Technologies, we live for these moments. Quiet, compounding wins are born from deep user understanding. Because great UX is often about removing the one thing that makes people pause. In this case, it was the pressure to “sign up” :) #UX #change #growth

🪜 Designing Better Progress Steps UX. With practical techniques to help people navigate and complete complex forms with or without progress steps ↓ ✅ Progress steps break a long form into small, manageable parts. ✅ They show where users are and how much they have left to go. 🤔 But: they are often overlooked and don’t scale well on mobile. 🤔 Difficult to design for dynamic flows with conditional sections. ✅ For simple forms, always start without a progress indicator. ✅ Tell users what they need and how much time it will take. ✅ Show progress as “Step [X] out of [Y]” with a text label. ✅ Add a drop-down to support quick jumps between steps. 🚫 For complex forms, don’t rely on visual progress bar alone. ✅ Always include text labels under each step for easy, precise jumps. ✅ Underline labels to make it clear that users can use them to navigate. ✅ Design 6 states: incomplete, active, complete, error, disabled, warning. 🤔 You can rarely display 5+ progress steps on mobile. ✅ Keep active label visible but hide future and past steps. ✅ Show a Back link on the top, Next button at the bottom. ✅ For long forms, repeat the Back link at the bottom, too. ✅ On desktop, vertical progress steps often work better. ✅ Set up an overview page with links to single steps (“task list”). ✅ Allow users to expand and collapse all steps and sub-steps. ✅ Don’t forget to highlight error status in the progress step. Only few things are more frustrating than a progress bar that seems to be stuck. Complex forms often have conditional sections, so users end up going in circles, staying on the same step as they move between sections. It’s a common problem with horizontal layout, and a common reason why people leave. With a vertical layout, we can always show all sections with all sub-steps, explaining to users where they are and what’s coming up next. We can expand and collapse some steps and support fast navigation and quick jumps. We can also highlight all errors or missing data and explain what’s actually missing. A few smaller pages usually perform better than one long page. One column layout usually causes fewer errors as multi-column layout. Give users one column with vertical progress steps, and you might be surprised how much faster they get through the entire form, despite its complexity and length. Useful resources: Stepped Progression, by Goldman Sachs https://lnkd.in/eHbB5EFu Multi-Step Wizard, by GE HealthCare https://lnkd.in/ezA__z_E Task List Pattern, by Gov.uk https://lnkd.in/eb3PzTEJ Form Design: From Zero to Hero, by Adam Silver https://lnkd.in/eTBQxBXg Wizards Design Recommendations, by Raluca Budiu https://lnkd.in/eYYxUUDM Loading And Progress Indicators, by Taras Bakusevych https://lnkd.in/e5KFPiiq #ux #design

8 years back, India's top rideshare brand's acquisition funnel was like this. - 100 users install their app  - 35 users signed up with Phone no. & Email  - 8 users booked a ride on the app successfully within 7 days of install They were the market leaders. Yet, it had a lousy acquisition funnel. Then, the cost per install(CPI) for the rideshare industry used to be $0.5 or INR40 at scale. With this acquisition funnel, the cost of acquisition(CAC) was $6 or INR500. The average order value(AOV) was $2 or INR150. At a 20% gross margin, it took more than 17 rides to break even at this CAC level. A clear recipe for disaster. Then, we made a simple change in the acquisition flow. It increased the new user conversion rate by ~100%, reducing the CAC by ~50%. Removing the Email ID requirement in the signup flow. - Install to signup rate increased from 35% to 60%  - Install to booking rate improved from 8% to 15% After the ride completion, promoting the user to add an email ID to receive the invoice got us the email ID from most users who had one. This is an incremental change that yielded an outsized outcome. Today, most brands use this "phone no. only" flow. Not then, because most of the acquisition flow is inspired by the Western counterparts. This improvement becomes quite pronounced as the brand expands to the T3+ cities and older age segment. Another great idea to test in the acquisition flow is moving the signup prompt to the end. By Installing the app, the user makes a small investment in the brand. What If we let the user see the available cabs or browse the product immediately? Without the need for you to sign up. When they are about to book or make a purchase, prompt them to sign up. At this stage, the user invested additional time in the platform. Even for a free platform, we can let the user browse the content catalog and prompt them to sign up when they decide to consume. More investment means more likely to convert. Trying this will undoubtedly improve the install-to-activation/purchase rate for all brands.

👀 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

✨ What does iterative multivariate testing look like? Take a look at the chart below. A couple of years ago we helped a client make a big decision: should they enter a new market? Serious investment would be required, and the company’s board wanted evidence that the company could generate demand in a market with a lot of established competitors. The company had ZERO knowledge of the new market (and the market had no knowledge of the company). Together, we developed hypotheses about what might work to position the company for success. I want to note the plural in that last sentence: HYPOTHESES. That’s how multivariate testing works. You test MULTIPLE hypothetical strategies at once with MULTIPLE audiences. It’s very different from how most people approach strategy, which is to test (if they test at all) that one perfect strategy. Multivariate testing of strategy is incredibly powerful. In the chart below, you can see the results of the first set of tests—those first lumps of traffic and revenue on the left. Clearly there’s something there, but nobody’s killing it, right? Wrong. Averages are deceiving. In the second wave of testing, we dropped the losing strategies and audiences and focused on the winners. Things started to pick up. By the third wave of testing (which was really a series of mini-waves), we weren’t just finding what worked—we were optimizing it. We call this sort of testing HEAT-TESTING, because it finds the ‘hot spots’ between strategy and audience. What does heat-testing tell you? Which audiences are most receptive How large those early audiences are How to position your product Which user flows are most productive in generating interest or revenue The cost to acquire a customer Whether you should move to the next step of a big investment I’ve been a strategist my entire career and here’s what I know: no amount of competitive analysis, focus groups, and surveys will deliver the one perfect strategy. Testing multiple strategies, ideally in an environment that gives you real-life, behavioral feedback, gives you raw material to iterate your way to a validated strategy. Always be testing.

Writing for conversions is different from writing for traffic (Here’s why) Earlier, I used to Google the main keyword, Check the top 5 results on SERP, Write the content, and then publish. But in 2025, my writing approach changed. Before writing content, 👉I talk to the founder. 👉I talk to the sales team to know if there is demand for the product. Do people actually need it? Do they have challenges that our product solves but competitors don’t? I note down those words and phrases to use in the content. I try using the product myself. 👉Get the demo from the product team. Understand the features, ask them questions. Then, turn those into benefits. 👉I go to Reddit. Join SubReddits and see what people say about competitor products. Are they talking about pros, cons, or making “I wish this feature was there” statements like this? 👉 I read books to understand the concept deeply. Sometimes, analogies are the best way to link a product to a concept. P.S. – Stop writing for search engines. Start writing for a real audience. People will love your content if you start solving their problems. Result = More conversions. P.P.S - Do you write for traffic first or conversions first?

Excited to share my comprehensive crash course on Adobe Customer Journey Analytics Experimentation. In this detailed guide, I break down everything you need to know about leveraging CJA's powerful Experimentation Panel for A/B testing and multivariate experiments. This guide covers essential topics for data analysts, marketers, and product managers looking to make data-driven decisions with confidence. 𝐊𝐞𝐲 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: 📊 Deep dive into the mathematical foundations of Anytime Valid Inference 🔍 Step-by-step walkthrough of experimentation analysis 📈 Understanding statistical confidence and lift interpretation 🛠️ Practical tips for implementing and analyzing experiments 𝐊𝐞𝐲 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲𝐬: • Learn how experiment data flows from AEP to CJA • Master flexible panel configuration options • Understand robust statistical methodologies • Interpret visualization and performance metrics effectively Check out the full blog to enhance your experimentation practices: https://lnkd.in/gHnBN_vd #AdobeCJA #AdobeCustomerJourneyAnalytics #AdobeExperiencePlatform

We fixed a UX issue that quietly kills conversions. (And most websites never notice it.) This one’s sneaky. It’s not a broken button. Not bad colors. It’s confusing. In the first 5 seconds. Here’s what users really think when they land: - Can this team actually build what I need?” - And if the answer isn’t obvious? - They bounce. So we redesigned the Fully Built construction website from scratch. Here’s how we turned vague into valuable: 1. Cut the fluff. Clarity first. No buzzwords. No bloated intros. We answered the real question upfront: “Here’s what we do, how we help, and why you should trust us.” Because vague = risk. Clear = confident. 2. Designed for quick scans, not deep reads. We simplified the structure with: - Clean hierarchy - Strong visual breaks - Skimmable sections So users don’t search for info, they spot it instantly. 3. Built trust into the layout We ditched the cookie-cutter look. And gave it: - Warm, professional aesthetics - Real project proof - Clear process breakdown Because trust doesn’t come from “modern.” It comes from feeling reliable. 4. Focused on decisions, not decoration Pretty ≠ persuasive Trendy ≠ trustworthy UX isn’t about looking nice. It’s about guiding serious users to say yes. 5. Treated it like a rescue, not a facelift The old site wasn’t just messy. It was confusing, hesitant, and hard to trust. We didn’t polish it. We rethought the entire experience. Now it’s conversion-ready. Before: Wordy, vague, forgettable After: Clear, confident, high-converting Because great UX doesn’t just “look” better. It makes more sense to the people who matter. ⚠️ Final thought: If your website can’t pass the 5-second test, ↳ you’re leaking leads quietly. Want a breakdown on how we mapped clarity into every section? Ask me anything about it.

A/B Testing/Experimentation almost always is just regression under the hood. Basic A/B tests, Multivariate tests/ANOVA, interaction checks between simultaneous A/B tests, checks if different user segments respond differently to treatments, and variance reduction - all them can be performed with regression. Based on the choice of the data coding scheme and what/if additional data to include in the model the following can be easily recast/performed with basic regression: 1) Pairwise t-tests - this is the basic difference in means A/B Test. Design Matrix coded with an Intercept and single dummy variable, with the 'Control' as reference class. 2) MVT/Factorial ANOVA/f-tests - Design Matrix Effects coding such that the grand mean is 'reference'. 3a) User Segment Treatment Effects via partial F-test. Two Design Matrices: a) Main effect dummy variables only; b) A Fully interacted Design Matrix. F-test is ratio of residual sum of squares of main effects model over interacted model. 3b) Pairwise A/B Test interaction Partial F-test (see 3a). 4 Covariate Adjustment - CUPED/ANCOVA. Design matrix includes treatment dummy, mean centered pre experiment covariate(s) and dummy interaction term of covariate with treatment dummy* AND, it turns out that ALL of these test design matrices can be constructed from K-Anonymous data! That means that not only do all of these disparate tasks roll up into one general approach, but with a bit of upfront thought, all of them can be performed when following privacy by design. * technical note that unless Na=Nb, one prob should account for heteroskedastic errors rather than use the OLS st errs.

Most apps lose users because their workflows frustrate, confuse, or overwhelm them. Avoid these 5 pitfalls, and you’ll retain more users and boost satisfaction. 1. Cluttered Home Screen 🚫 Overwhelms users with too many choices upfront. ✅ Do this instead: Prioritize the most critical actions for users. Apply the “Fewer Choices Principle” to guide attention effectively. 2. Confusing Navigation 🚫 Users can’t find what they need quickly. ✅ Do this instead: Use universally recognized labels and icons. Organize content into clear, logical categories. 3. Lengthy Processes 🚫 Every additional step increases drop-offs. ✅ Do this instead: Conduct task analysis to reduce unnecessary steps. Implement features like autofill and a one-click checkout. 4. Slow Loading Times 🚫 1-second delay = 7% fewer conversions. ✅ Do this instead: Compress assets (images, videos). Leverage CDNs and lazy loading to speed up performance. 5. Poor Mobile Optimization 🚫 70% of users will abandon apps with poor mobile UX. ✅ Do this instead: Design for touch gestures like swiping and tapping. Test usability across screen sizes and operating systems. A seamless user flow isn’t just good design; it’s a growth strategy. By prioritizing simplicity and usability, you create apps that users want to return to. Have thoughts or questions? Drop them below or message me, let’s simplify user experiences together!