User Experience Case Studies

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

Zomato faced a big problem: How can we turn app browsers into loyal customers? The goal was clear, improve the user experience with personalized restaurant suggestions. But there were a few challenges too: 🔴 Understanding user preferences from massive data. 🔴 Combining multiple data sources for meaningful insights. 🔴 Developing accurate recommendation algorithms. 🔴 Processing data in real time to keep users engaged. 🔴 Building trust in the recommendations to ensure they felt helpful, not intrusive. To tackle this, Zomato used a structured approach: 🟢 Data Collection and Cleaning - They collected user behavior data (searches, clicks, abandoned carts). - They analyzed restaurant details (cuisine types, delivery times, ratings). - Past orders were also analyzed for trends. 🟢 User Segmentation - Users were grouped based on age, location, past orders, and browsing habits. - This helped them identify patterns and preferences. 🟢 Developing the Recommendation System - Combined collaborative filtering (what others like you prefer) and content-based filtering (what matches your past orders). - Fine-tuned algorithms with ongoing testing for better accuracy. 🟢 Implementation and Testing - They rolled out the recommendations and tested them through A/B experiments. - Adjusted based on user feedback and data performance. 🟢 Continuous Improvement - Introduced feedback loops for real-time adjustments. - Regular updates ensured the system stayed relevant to evolving user needs. And, the impact was impressive: ⬆️ 35% more time spent on the app by users receiving personalized suggestions. ⬆️ 28% higher click-through rates, showing better engagement. ⬆️ 22% increase in orders per user per month due to tailored suggestions. ⬆️ 18% boost in retention rates, turning occasional users into loyal customers. ⬆️ 12% higher average order value, leading to revenue growth. ⬆️ 15% jump in monthly revenue, proving personalization works! I see this as the perfect example of using data to deepen customer relationships. It's not just about the tech—it’s about understanding people and making their experience smoother and more personal. 📊 Data is the secret to building trust and loyalty. What do you think? Can other industries learn from Zomato’s success? How can personalization improve your industry? #zomato #deepindergoyal

I was browsing Netflix recently and I discovered a genre called “cerebral”. Curiosity got the best of me and I started researching about this genre and where it came from as it did not exist before. I learned that the "cerebral" genre in entertainment focuses on thought-provoking, intellectually stimulating content that challenges the audience's perception, often exploring complex themes, intricate narratives, and deep psychological or philosophical concepts. this category seems to have emerged from data insights that showed viewers gravitating towards similar content. Netflix leaned into this, proving that understanding your audience can lead to entirely new categories of experience using design thinking. The shift to personalized recommendations was the result of constant prototyping, testing, and iterating. Netflix tracked user habits — what viewers clicked on, how long they watched and even the type of content they binged late at night. By focusing on these insights, they designed an experience tailored to individual tastes, setting a new standard for customer engagement. Design thinking is about innovation through empathy. When businesses take the time to truly listen, they open doors to improvements that users didn’t even know they needed. #CerebralContent #Netflix #Innovation #DesignThinking #CustomerExperience #Personalization #UserInsights #EmpathyInDesign

Have you ever spent endless hours on a project just to end up realising that a more straightforward method would have been more effective? This common mistake, referred to as over-engineering, can cause needless complexity and inefficiency when developing new products. Understanding Over-engineering > Over-engineering happens when a solution gets more difficult than it needs to be, usually by adding features or functionalities that do not directly meet the needs of customers. > This can lead to higher costs, longer development cycles, and less user-friendly products. Real-World Example: The Juicero The Juicero, a high-tech juicing machine, was released in 2016. It cost $700 and was designed to squeeze proprietary juice packets with considerable force. Later on, though, it was found that the costly machine was not essential because the same juice bags could be squeezed by hand. The company was eventually shut down as a result of the public outcry following this disclosure. My Own Story: The Overly Complex Website I was in a team early in my career that was assigned with creating a company website. We included the newest interactive elements and design trends in an effort to wow. Feedback received after the launch, however, indicated that visitors found the website overwhelming and challenging to use. In our pursuit of innovation, we had failed to realise the website's main purpose, which is to provide easily comprehensible information. I learnt the importance of simplicity and user-centred design from this experience. Useful Tips to Prevent Over-Engineering 1. Pay attention to the essential needs: Focus on key features that meet user needs and clearly explain the issue you're trying to solve. Don't include features that aren't directly useful. 2. Adopt Incremental Development: Begin with an MVP that satisfies the fundamental specifications. By using this method, you may get user input and decide on new features with knowledge. 3. Put Simplicity First: Use the KISS philosophy, which stands for "Keep It Simple, Stupid." Simpler designs are frequently easier to use and more efficient. 4. Verify Assumptions: Talk to users to learn about their wants and needs. This guarantees that the things you create will actually be useful to them. 5. Promote Open Communication: Create an environment where team members are at ease sharing thoughts and possible difficulties. Over-engineering tendencies can be recognised and avoided with the support of this collaborative environment. Have any of your initiatives involved over-engineering? How did you respond to it? Post your thoughts and experiences in the comments section below!

🔮 State Of AI UX For Designers 2026 (free 3h video recording, video + all slides) (https://smashed.by/humans), on UX challenges of AI, how people use AI, accessibility challenges and voice UX, AI design patterns and what designers need to know about AI’s limitations and constraints. Kindly hosted by yours truly last week. 🔴 Full video recording (3h): https://lnkd.in/eDX9FSBR 🗃️ All slides: https://lnkd.in/eiyNp6vm 🌱 Google Doc resources: https://smashed.by/humans 🧭 AI Solution Canvas (PDF): https://lnkd.in/ej9aGMUT My key takeaways from the session: 1. AI hype makes people tired and confused about AI (AI fatigue) 2. "AI-generated” = cheap, fast, unreliable, untrustworthy, cheating. 3. Humans are bad at writing prompts → ask AI to write it for you 4. People think AI makes them productive, but data doesn't show it 5. AI-generated work often creates more work for other people. 6. Illusion of productivity: code generation ↑, review time ↓, bug rate ↓. 7. Bottleneck didn't disappear: it moved from writing to reviewing code. 8. AI typically can get you 90% of the way, but 10% require expertise. 9. Traffic is a vanity metric in times of AI; brand exposure is critical. 10. Not every AI is LLM, and not every AI-product needs LLMs. 11. You can't get fast response times and reliability with LLMs alone. 12. Frequent architecture: Non-AI software + AI feature + tiny LLM layer. 13. Interpreting user's intent is difficult, requires context and data. 14. Quiet vs. Visible AI → augment AI capabilities into user journeys. 15. AI adds value where users struggle (pain points) or succeed. 16. Best AI-powered products aren’t “AI-first”, but “AI-second”. 17. AI often creates accessibility issues, rather than solving them 18. Voice has tremendous advancements, with new capabilities 19. Add abstraction to your AI UI: sliders, knobs, filters, buttons (!). 20. Most AI tools need 2 levels of prompting: global + contextual. 21. As designer, you need to know what AI capabilities you can use. 22. Frame frequent prompts as task and suggest presets/templates. 23. Map voice and tone on how AI should speak depending on tasks. 24. Beware of AI hype: AI features aren't useful if they aren't used. If you'd like to dive deeper: 🔮 Design Patterns For AI → https://lnkd.in/e5XsMXwm 🏎️ Live AI UX training (Maven) → https://maven.com/web- adventures/design-patterns-ai-interfaces?promoCode=FAST25 🗃️ All slides and resources: https://smashed.by/humans Thank you so much for joining, everyone — and I hope you'll find it useful in your work! Feel free to share with your colleagues, friends and strangers! And as always, thank you so much for your kind support and interest — I sincerely appreciate it from the bottom of my heart. ❤️ #ux #design

The 𝐏𝐚𝐭𝐢𝐞𝐧𝐭 𝐑𝐨𝐨𝐦 𝐨𝐟 𝐭𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 (𝐏𝐑𝐨𝐅) teaching case shows how a large healthcare consortium and a small group of manufacturers collaborated to rethink innovation in a highly regulated sector. At its core, the case demonstrates how PRoF turned the interaction between two very different communities into its main innovation engine. The large consortium represents the healthcare user community: nurses, doctors, caregivers, patients, and hospital managers who express the lived reality of care. Their contribution is experiential and value-based. Through structured “brainwave sessions,” they surface latent needs and convert them into broad keywords such as comfort, privacy, dignity, or anti-loneliness. These keywords form a shared language that avoids technical jargon and allows hundreds of users with diverse perspectives to converge around common priorities. The small consortium consists of manufacturers, architects, and designers who have the capabilities to transform these user insights into concrete room concepts. Their commercial goals are kept strictly outside the creative process, allowing trust to grow between the groups. Once the user community defines the keywords, the producer community develops prototypes, after which the large consortium returns to evaluate and refine them. This modular sequencing keeps tensions low, ensures rapid progress, and prevents commercial logic from dominating user needs. The interaction between these two communities solves a longstanding problem in healthcare innovation: suppliers often misunderstand user needs, while users lack the means to innovate. PRoF bridges this gap by letting users drive ideation and letting producers translate that insight into solutions. What emerges is a genuinely user-oriented innovation ecosystem in which neither community could succeed alone, but together they generate concepts that reshape expectations of care design. You can find the case study at HBSP: https://lnkd.in/e6nxTFM7 #UserCentricInnovation #Collaboration #OpenInnovation #CrossCommunityCollaboration #HealthcareEcosystems #CoCreation #Ideation

Blinkit quietly did something brilliant on their home page “Single Mode.”😲 And honestly, this is one of the smartest examples of empathetic UX design I’ve seen recently. Instead of assuming everyone is celebrating occasions the same way, Blinkit adapts the entire home experience based on emotional context. If you’re single, the app doesn’t push couples, gifts-for-two, or romantic clichés. It reframes the narrative self-care, personal treats, no-sharing snacks, plans for one. That’s not just personalization, that’s respecting user mindset. 💡From a UX lens, this works because - The feature is optional, not forced - The copy is light, funny, and non-judgmental - The UI adapts visually without breaking familiarity - And most importantly, it reduces emotional friction while browsing Yes, it’s personal 😅 But that’s exactly why it works. Great UX isn’t always about complex flows or flashy animations. Sometimes it’s about saying, “We see you.” Well played, Blinkit.🙌 This is thoughtful design, strong UI storytelling, and a solid reminder that context-aware UX builds trust. What is your opinion about this?🤔 #UXDesign #UIDesign #ProductDesign #UXWriting #DesignThinking #UserCentricDesign #Blinkit #UXInspiration #DigitalProductDesign #UXIndia #uiux #linkedin #creator #concepts #learning #designcommunity

Two years ago, I helped a startup launch a new conversational AI feature. At launch, every metric looked good: DAUs and engagement up, longer sessions, ‘Helpfulness’ was scoring 4.2/5. But four weeks later, we discovered the problem:   14D and 30D 𝗿𝗲𝘁𝗲𝗻𝘁𝗶𝗼𝗻 was <5% 𝗧𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺? While, the product answered ALL of the users’ questions. And the answers 𝘴𝘰𝘶𝘯𝘥𝘦𝘥 correct. But they weren’t accurate enough to be useful. As a result, users never came back. We had spent six figures and four months building a feature that users abandoned immediately. That’s when we realised what went wrong: We focused on 𝗽𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴, did “𝙫𝙞𝙗𝙚 𝙘𝙝𝙚𝙘𝙠𝙨”, and thought metrics like “𝗵𝗮𝗹𝗹𝘂𝗰𝗶𝗻𝗮𝘁𝗶𝗼𝗻” and “𝘁𝗼𝘅𝗶𝗰𝗶𝘁𝘆” were enough. But the real challenge was not prompting. It was 𝗲𝘃𝗮𝗹𝘀 We didn’t have an evaluation strategy. We didn’t even know how to build one. That’s when I took Hamel H. and Shreya Shankar's course, "𝗔𝗜 𝗘𝘃𝗮𝗹𝘀 𝗳𝗼𝗿 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 & 𝗣𝗠𝘀." What I learned in that course shaped how I build AI products. Here are my top learnings: 1. Top 1% of AI teams master one skill: 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻-𝗖𝗲𝗻𝘁𝗿𝗶𝗰 𝗘𝘃𝗮𝗹𝘀.     2. They build systems that tell them exactly WHERE the product fails to meet user needs.     3. Asking "Is it good?" is not enough.     4. Use 𝗘𝗿𝗿𝗼𝗿 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 to find the 20% of failures causing 80% of churn.     5. You need to 𝗱𝗲𝗳𝗶𝗻𝗲 𝘄𝗵𝗮𝘁 “𝗴𝗼𝗼𝗱” 𝗺𝗲𝗮𝗻𝘀. And then align your whole team on the definition.     6. 𝗕𝘂𝗶𝗹𝗱 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗟𝗟𝗠-𝗮𝘀-𝗷𝘂𝗱𝗴𝗲 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗼𝗿𝘀 𝘆𝗼𝘂 𝗰𝗮𝗻 𝘁𝗿𝘂𝘀𝘁. Then validate them against human judgment to correct for bias. This enables testing at scale. Every AI feature you ship without evals is a 𝗴𝗮𝗺𝗯𝗹𝗲. One failed feature can cost you 𝘀𝗶𝘅 𝗳𝗶𝗴𝘂𝗿𝗲𝘀. This course costs $𝟮,𝟱𝟬𝟬. Even if it saves you from just one failure, the ROI is great. That’s the tradeoff. And it’s obvious. Link in comments. P.S. Get a 𝟯𝟱% 𝗱𝗶𝘀𝗰𝗼𝘂𝗻𝘁 on the course with the link below P.P.S Attached: cheat sheet with top my top learnings

For consumer-facing platforms, delivering relevant and personalized recommendations isn’t just about convenience—it’s key to enhancing the traveler experience. In a recent blog post, Expedia Group's Data Science team shared how they’ve refined their property search ranking algorithm to better match user intent and provide more meaningful results. Expedia’s recommendation system is traditionally designed for destination searches, where travelers enter a location and filter to find suitable lodging. In this case, the algorithm ranks properties based on their overall relevance. However, another common scenario is property searches, where users arrive on the platform looking for a specific hotel—often through external channels like search engines. If that property is unavailable, simply displaying top-ranked hotels in the area isn’t the best solution. Instead, the system needs to recommend accommodations that closely match the traveler’s original intent. To tackle this, the Data Science team enhanced their machine learning models by incorporating property similarity into the ranking process. They improved data preprocessing by focusing on past property searches that led to bookings, ensuring the model learns from real traveler behavior. Additionally, they introduced new similarity-based features that compare properties based on key factors like location, amenities, and brand affiliation. These improvements allow the system to suggest highly relevant alternatives when a traveler’s first choice isn’t available, making recommendations feel more intuitive and personalized. While broad recommendation systems lay the foundation for personalization, adapting them to specific user behaviors can greatly improve satisfaction. Expedia’s approach highlights the power of fine-tuning machine learning models to better address evolving business needs. #MachineLearning #DataScience #Algorithm #Recommendation #Customization #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gFZSXpMQ

How User Calls and a Simple Text Update Led to a 3.25% Increase in CTR [A/B Experiment] 📈 I was recently working on a project looking for low-performing categories where CTR is low with high search volumes and significant revenue potential.💲 One such category was "Hospitals." Given the variety of reasons someone might search for a Hospital—whether for inquiries, appointment scheduling, or specialty information—it became clear that understanding user intent was key 🤔 To gain deeper insights, we conducted user calls to better understand why users were landing on our platform. Through these calls, we discovered that many users were attempting to book appointments, even though Justdial is an aggregator platform that does not offer direct booking for all healthcare providers. Booking functionality is available only for certain paid businesses 🏥 To address this, we needed to better guide users on how to proceed. While users could either call or submit an enquiry through our platform, engagement was still low. 📉 To improve this, we made a simple yet impactful change to the text on our CTAs. We updated the primary CTA from "Call Now" to "Call to Book" and the secondary CTA from "Send Enquiry" to "Check Availability" 📞 This small change resulted in a 3.25% increase in click-through rates. Knowing the context and nudging users at the right time can lead to better conversions. Solves problems for both the user & the business. 💡✅ #prodcutmanagement #experiment