User Flows And Pathways

My experience of working at Urban Company taught me one key lesson about Indian consumers: Convenience may be tempting, but control wins every single time! You see, I was a product guy at UC then. - And Abhiraj (the founder and CEO) had tasked me and my colleague Sripad (now heads product at Dezerv) to improve new user conversions - In our shoes, most people would have thought of shortening the flow by selecting a few options by default, so the user would have to make fewer decisions or taps - But, we went by the approach of adding more options that the user could choose. This was because we wanted to make the user feel way more in control and in charge - Driving the calls And this worked wonders for our conversion rates. Why? Because customer trust went up massively. Thus, when launching the feature to schedule weekly bookings for our Dubai business, we actually added a step, elongating the flow. And again, we saw conversions go up! This taught me: - You can cut a user journey by two clicks, nail a sleek UI, and still see drop-offs. Why? The user didn’t feel in charge - In a country where ration shops and bank queues have taught people to expect friction, control is power. Control is trust And anything that makes the user feel that they hold the decision making, the control - IT WINS! And this shouldn’t be surprising. I’ve seen users manually enter OTPs over auto-read because typing feels safer. They skip recommendations to re-search, ensuring they’re not tricked. That’s not inefficiency - it’s defence. Ignore this, and your retention tanks. A good example is that of a fintech (I won’t name) which launched an “auto-invest” feature - It ended up driving away 20% of users who felt sidelined. But apps like PhonePe thrived with the same product with “confirm payment” prompts. - It’s pretty simple and logical. Every flow should ask: “Where does the user say ‘I’m in charge’?” - A “you can change this later” label, a manual toggle, or a “review before submit” step builds comfort Zomato’s customisable delivery instructions are one more example. These trust signals scale because they align with India’s psyche, where almost every user prefers double-checking. Thus, I now always recommend to founders in my circles, if you are building for Indian audiences, audit for control points. Add confirmations, transparent labels like “No hidden fees.” Don’t force automation - offer manual options. Test retention, not just conversion. Study PhonePe or Paytm’s “over-communicative” designs. Those extra prompts aren’t accidents - they’re trust engines. They’re not hurdles - they’re well planned and well placed handshakes. What do you think? Do share below. Best, Shiv

🤖 How To Design Better AI Experiences. With practical guidelines on how to add AI when it can help users, and avoid it when it doesn’t ↓ Many articles discuss AI capabilities, yet most of the time the issue is that these capabilities either feel like a patch for a broken experience, or they don't meet user needs at all. Good AI experiences start like every good digital product by understanding user needs first. 🚫 AI isn’t helpful if it doesn’t match existing user needs. 🤔 AI chatbots are slow, often expose underlying UX debt. ✅ First, we revisit key user journeys for key user segments. ✅ We examine slowdowns, pain points, repetition, errors. ✅ We track accuracy, failure rates, frustrations, drop-offs. ✅ We also study critical success moments that users rely on. ✅ Next, we ideate how AI features can support these needs. ↳ e.g. Estimate, Compare, Discover, Identify, Generate, Act. ✅ Bring data scientists, engineers, PMs to review/prioritize. 🤔 High accuracy > 90% is hard to achieve and rarely viable. ✅ Design input UX, output UX, refinement UX, failure UX. ✅ Add prompt presets/templates to speed up interaction. ✅ Embed new AI features into existing workflows/journeys. ✅ Pre-test if customers understand and use new features. ✅ Test accuracy + success rates for users (before/after). As designers, we often set unrealistic expectations of what AI can deliver. AI can’t magically resolve accumulated UX debt or fix broken information architecture. If anything, it visibly amplifies existing inconsistencies, fragile user flows and poor metadata. Many AI features that we envision simply can’t be built as they require near-perfect AI performance to be useful in real-world scenarios. AI can’t be as reliable as software usually should be, so most AI products don’t make it to the market. They solve the wrong problem, and do so unreliably. As a result, AI features often feel like a crutch for an utterly broken product. AI chatbots impose the burden of properly articulating intent and refining queries to end customers. And we often focus so much on AI that we almost intentionally avoid much-needed human review out of the loop. Good AI-products start by understanding user needs, and sparkling a bit of AI where it helps people — recover from errors, reduce repetition, avoid mistakes, auto-correct imported files, auto-fill data, find insights. AI features shouldn’t feel disconnected from the actual user flow. Perhaps the best AI in 2025 is “quiet” — without any sparkles or chatbots. It just sits behind a humble button or runs in the background, doing the tedious job that users had to slowly do in the past. It shines when it fixes actual problems that it has, not when it screams for attention that it doesn’t deserve. Useful resources: AI Design Patterns, by Emily Campbell https://www.shapeof.ai AI Product-Market-Fit Gap, by Arvind Narayanan, Sayash Kapoor https://lnkd.in/duEja695 [continues in comments ↓]

We recently wrapped up usability testing for a client project. In the fast-paced environment of agency culture, the real challenge isn’t just gathering insights—it’s turning them into actionable outcomes, quickly and efficiently. Here’s how we ensured that no data was lost, priorities were clear, and progress was transparent for all stakeholders: 1️⃣ Organized Documentation: We broke the barriers— and documented on Excel sheet to categorize all observations into usability issues, enhancement ideas, and general comments. Each issue was tagged with severity (critical, high, medium, low) and frequency to highlight trends and prioritize fixes. 2️⃣ Action-Oriented Workflow: For high-severity and high-frequency issues, immediate fixes were planned to minimize potential impact. Ownership was assigned to specific team members, with timelines to ensure quick resolutions, in line with our fast-moving development cycle. 3️⃣ Client Transparency: A summarized report was shared with the client, showing the issues identified, the actions taken, and the progress made. This kept everyone aligned and built confidence in our iterative design process. Previously, I’ve never felt the level of confidence that comes from having such detailed and well-organized documentation. This documentation not only gave us clarity and streamlined our internal processes but also empowered us to communicate progress effectively to the client, reinforcing trust and showcasing the value of our iterative approach. It’s a reminder that thorough documentation isn’t just about organizing data—it’s about enabling smarter, faster decision-making. In agency culture, speed matters—but so does precision. How does your team balance the two during usability testing?

I redesigned my entire UX/UI process with AI. It’s not about “use ChatGPT to brainstorm.” I mean, I rebuilt the whole pipeline. From product idea to prototype. What used to take months? Now gets done in days. Here’s what it looks like step-by-step: 1. Instant User Flows I drop rough product ideas into ChatGPT. (It's not the public one; it's a custom GPT trained on how I think.) It gives me: - Sitemap - User journey - Logic flows All in less time than it takes to make coffee. 2. Wireframes Without Drawing I stopped sketching. I describe the layout in plain English, and Magician does the rest. "Hero. CTA. Testimonials." Boom. Wireframe. No more dragging boxes like it’s 2015. 3. AI-Built Design System Spacing? Typography? Button styles? I just describe the vibe. Tools like Relume and Uizard take that and build me a full design system. This used to take WEEKS. Now it’s done before lunch. 4. Smarter Figma Time Now everything moves to Figma. But I don’t waste time pixel-pushing. AI plugins handle: - spacing - responsiveness - and accessibility. I just make the ideas click. 5. Prototyping = Auto-On Final step? Auto-connect flows with Figma’s AI tools. Clickable. Shareable. Client-ready. Dev-approved. No extra buttons. No guesswork. Here’s the real punchline: AI didn’t replace my work. It replaced the boring parts, so I can focus on design thinking. It’s not about working faster. It’s about designing smarter. We’re not in 2015 anymore. Let’s build like it’s 2030. What part of your UX workflow do you still do manually? Curious to hear.

How do you and your teams synthesise and select which customer needs or pains to progress in your #product, #design, or #innovation projects? Imagine you've just completed some great customer discovery research, including observing, interviewing and being the customer. You've built some good empathy for who your customers are, what is important to them, what pains them, and what delights them. Then you unpack your findings into some form of empathy map, and you've got 100s of sticky notes everywhere. You've then started to narrow them down to the most promising and interesting observations, but this still leaves you with a sizeable collection and you want to add some rigour to your intuition on which ones to take forward first. Well, here are 3 different methods that I’ve used and iterated over the years: Number One – The Opportunity Scale This first one is the simplest and is inspired by how Alexander Osterwalder et al rank jobs, pains and gains in their book Value Proposition Design, 2014. As a team, you take your short list of observations from your empathy map and rank them from how insignificant/moderate to how important/extreme the need/pain is for the customer with the most important/extreme being prioritised to explore further first. Number two – The Opportunity Matrix A The opportunity matrix increases the rigour and confidence of your prioritizing by adding ‘strength of evidence’ as another dimension. Strength of evidence at this stage of journey can be determined by the number and type of data points. For example, if you heard from several customers that a pain point was extremely painful then you could be more confident this was worth solving than one highlighted by only one customer. Likewise, observing customers do something provides stronger evidence than customers saying they do something. Here you prioritise the most important needs with the strongest evidence first. Something to watch out for is when your team selects an observation that has strong evidence but isn’t that important of a need or pain to customers. Teams can be blinkered by numbers and end up over-investing in time wasting-opportunities. Number three – The Opportunity Matrix B The third method swaps out evidence for fulfilment of the need - how satisfied are customers with their ability to fulfil the need/solve the pain with the solutions they use today? By matching this with the importance of the need/pain we can select those observations that we understand to be the most important and unmet for our customers. You can then overlay the strength of evidence across this ranking to make your final selection even more robust. And to take it to a whole new level and really de-risk your selection you can test your prioritised observations, written as need statements, in quantitative research with customers. This is something that Antony Ulwick shares in his book Jobs To Be Done, 2016. I hope you find these methods useful. #designthinking #humancentreddesign

We solved half the problem & thought we bridged the gap. Ever worked on a solution that looked perfect on paper… but ended up creating more problems than it solved? That’s exactly what happened when I was called in to review a telehealth solution. It was well-designed, checked all the cybersecurity boxes, & allowed patients to consult doctors remotely. The project requirement was clear: enable remote consultations. And the solution delivered exactly that. But here’s the thing: While healthcare systems often operate in silos, patients experience their care as one continuous journey. And this solution missed critical parts of that journey: 🔸 No easy way to book follow-ups. Patients had to call, leading to missed care. 🔸 Medication collection still required hours of travel, making the platform’s convenience meaningless. 🔸 Administrative staff were overloaded, causing delays in care coordination. We solved one problem & unintentionally created three more. The solution was designed for the system’s convenience, not the patient’s journey. To shift the perspective, we expanded the conversation to include voices we hadn’t considered: 🔸 Pharmacists: To integrate medication delivery into the process 🔸 Community Health Workers: To provide local, hands-on support 🔸 Family Caregivers: To highlight logistical & emotional challenges at home 🔸 IT Teams: To automate follow-ups & reduce administrative burden 🔸 Local Transport Providers: To enable last-mile delivery of medications With these insights, we redesigned the solution into a comprehensive care experience: ✅ Patients could book follow-ups easily & get automated reminders ✅ Medications were delivered directly to their homes ✅ Caregivers & community workers ensured patients didn’t fall through the cracks I later learned that: 🔸 Missed follow-ups dropped by 40%. 🔸 Medication adherence & health outcomes improved significantly. The redesigned platform didn’t just connect patients to doctors, it completed the care journey. Next time you’re working on a solution, consider these points: 1️⃣ Patients see one journey While systems operate in silos, patients experience care as a unified process. 2️⃣ Identify all stakeholders Both direct & indirect voices like caregivers, pharmacists & community workers, are essential to closing gaps. 3️⃣ Design for continuity Address every touchpoint in the patient’s journey, ensuring nothing falls through the cracks. Have you worked on solutions where overlooked stakeholders made all the difference? What’s one gap you discovered that changed everything? #DigitalHealth #Innovation #HealthcareTransformation #PatientExperience #Collaboration 💡This post is part of 'Rethinking Digital Health Innovation' (RDHI), empowering professionals to transform digital health beyond IT and AI myths. 💡Find the ongoing series and resources on our companion website (URL in comments). 💡 Repost if this message resonates with you!

Beyond getting the code to work, a developer should step into the shoes of other roles to bridge the gap between 'Code and Customer value'.   Think like a, 🤔    👉 Product : 🔹 How is this feature helping users ? Is it solving a pain point ? 🔹 What is the impact it brings in - Is it engagement, retention or conversions ? 🔹 What success metrics looks like ? What are the measurable KPI's ? 🔹 Do we have the right instrumentation for measuring it in production ? 🔹 What is your A/B strategy ?    👉 Designer : 🔹 Is the design intuitive enough ? 🔹 Is it visually appealing to the user ? 🔹 Does it simplify or complicate the user journey ? 🔹 Are you using patterns that User are already familiar with ?    👉 QA Engineer : 🔹 What are all the edge cases beyond happy flows ? 🔹 How am I gracefully handling on all the errors, timeouts & failures ? 🔹 What is the impact to customer under high load ? 🔹 Is the experience same across different devices or network conditions ?   Most importantly,    👉 Be your own Customer : 🔹 Is the feature intuitive and straight forward to use ? 🔹 Are there any unnecessary steps, delays or friction ? 🔹 Is it Fast & Responsive ? 🔹 Is navigating from one screen to another seamless ? 🔹 Is data parity maintained throughout the App ? 🔹 Are the messages or nudges you see are clear and concise, but not too overwhelming ?   This mindset ensures that every feature not only functions correctly but also delivers a compelling user experience in the products we build. 🚀🚀   #tech #careergrowth #myntra

Bridging the Gap: Fixing the Online-to-Offline Disconnect for Gen Z Shoppers Retailers talk about “connected retail”—seamless experiences, digital integration, and meeting customers where they are. Yet, for Gen Z—the most digitally savvy yet least brand-loyal generation—there’s still a glaring disconnect between online discovery and in-store experience. The Problem: A Fragmented Shopping Journey Gen Z’s path to purchase isn’t linear: • They discover products on TikTok, Instagram, or Snap. • They engage—saving, sharing, or adding to cart. • They expect instant access—online or in-store. But the in-store experience fails to acknowledge their digital footprint: • No connection between online and offline – A shopper who engages online walks into a store with no guidance, wayfinding, or acknowledgment of their interest. • Lack of real-time insights for associates – Store staff don’t have access to customer browsing data, leaving shoppers to navigate alone. • Missed conversion opportunities – Instead of real-world nudges, retailers rely on email reminders, ignoring the potential of geo-triggered incentives. This disjointed approach frustrates Gen Z and drives lost sales. The Fix: Using Gen AI to Personalise In-Store Retailers already have the data—they just aren’t using it effectively. By leveraging Gen AI, in-store media, and real-time personalisation, stores can transform into intelligent, interactive spaces that bridge the online-to-offline gap. ✅ Connected mobile experiences – Geo-fenced notifications and social media integrations can remind shoppers: “That jumper you saved? Aisle 4, 20% off today.” ✅ AI-powered digital screens – Personalized displays show trending products based on online engagement. ✅ Smart carts & RFID tracking – Shopping carts recognise items and suggest related products based on past interactions. Personalising the In-Store Experience ✅ AI-powered clienteling – Store associates can access real-time customer data, making recommendations based on online browsing history. ✅ Dynamic promotions – Online cart abandoners receive exclusive in-store discounts upon arrival. ✅ AI-powered wayfinding – Shoppers use their phones for a personalised store map guiding them to saved items. The Future: From Siloed to Seamless For Gen Z, digital and physical retail are intertwined. The brands that integrate these experiences will win, while those that don’t will see foot traffic decline. The future of retail isn’t just about digital ads—it’s about: ✔ Using Gen AI to personalise the in-store journey ✔ Eliminating friction between online interest and in-store purchase ✔ Turning retail media into an in-store shopping assistant, not just an ad platform Retailers who get this right won’t just sell more—they’ll build lasting loyalty and turn Gen Z into lifelong brand advocates. It’s time to fix the disconnect. The future of retail is seamless, intelligent, and real-time. #digitalcommerce #immersivetech #retailtech

🔹 Day 21 – Product Manager Interview Prep Series 🔹 🎯 RCA-Based Question: “Your team just launched a new onboarding flow. Instead of increasing activation, it's led to a spike in churn. How would you analyze and resolve this issue?” 📌 Step-by-Step Breakdown – Root Cause Analysis (RCA) As a PM, your goal is to understand user behavior, pinpoint the friction, and fix the flow without compromising long-term retention. 1️⃣ Clarify the Problem 🔍 Define “churn”: Is it users dropping mid-onboarding? Or completing onboarding but not returning? Ask: -What’s the exact drop-off point in the new flow? -Is the churn immediate (same day) or delayed (after 1–2 days)? -What does churn look like compared to the previous flow? 2️⃣ Quantify & Segment the Impact 📊 Dive deep into analytics: 📈 Timeframe: When was the new flow launched? Sudden spike or gradual rise in churn? 👥 User Segments: Are new users from a particular platform (iOS/Android/Web) churning more? 🌐 Geo/Cohort Analysis: Are certain regions, age groups, or acquisition channels seeing higher churn? 🧪 AB Testing: Compare churn between users on old vs. new flows (if test is live). 3️⃣ Identify Potential Root Causes 🧠 UX/UI Issues: -Too many steps or confusing layout? -New permission asks too early (e.g., location, notifications)? -Value not shown quickly enough? 🔧 Technical Issues: -App crashes, lags, or slow load times? -Broken API, failed calls, or validation errors? 🧭 Psychological Friction: Users feeling overwhelmed or not understanding the benefits? High cognitive load in first interaction? 4️⃣ Talk to Stakeholders & Users 👂 User Feedback: - Session recordings (Hotjar/FullStory) - User interviews or feedback surveys - App store reviews post-launch 🤝 Internal Teams: - Engineering: Check for bugs, crashes, error logs. - Design: Walk through usability testing insights. - Data Science: Get funnel drop-off visualization. 5️⃣ Suggest Short-Term & Long-Term Improvements 🛠 Short-Term Fixes: - Roll back the most friction-heavy step. - Add in-line help or tooltips at high drop-off points. - Highlight core product value earlier. 🚀 Long-Term Initiatives: - Redesign onboarding based on user mental models. - Introduce progressive disclosure – don’t show everything at once. - Run usability tests before full rollout. 6️⃣ Measure Success Track: ✅ Increase in activation rate 📉 Drop in onboarding churn 🧠 User comprehension (measured via surveys or task success rate) 🎯 Retention metrics over Day 1, Day 7, Day 30 🔁 PM Mindset Tip: Onboarding is your first impression. Make it intuitive, not intimidating. Test thoroughly, talk to real users, and iterate until value is delivered with clarity and ease. 💬 How would YOU debug a broken onboarding flow? Let’s brainstorm in the comments 👇 #ProductManagement #PMInterview #RootCauseAnalysis #Onboarding #UserChurn #UserExperience #LinkedInDaily #ActivationStrategy #ProductDesign #LinkedInNewsIndia

I've been thinking about what DTC brands get wrong about omnichannel expansion recently. The temptation is to try to be everywhere at once. But the real winners are strategically aligning each channel to build a holistic growth engine. Here’s how to do it right → First, you must have channel-specific thinking. Every channel needs its own playbook. A helpful framework to structure your efforts... DTC Website: • Focus on basket building • Higher AOV targets • Full-price strategy • Data collection hub • Customer relationship building TikTok Shop: • Single-product purchase reality • Organic content engine • Lower AOV expectations • Limited data access • Treat as a retail channel Amazon: • Multi-pack strategy • Bundle economics • Marketplace presence • Competitive monitoring • Specialized management Next up, the Integration Challenge → The biggest mistake brands make is trying to force the same strategy across all channels. Example: One brand we spoke with increased shipping costs on TikTok Shop to push customers to their website. Instead of fighting the platform's natural behavior, they should have optimized for it. You must also consider your unit economics because each channel has its own cost profile. - TikTok Shop might be a loss leader but drive retail success. - Website sales might have better margins but higher customer acquisition costs. - Amazon might have lower margins but better operational efficiency. Here is the new omnichannel playbook: 1. Channel Optimization - Build channel-specific content - Adjust pricing strategies per platform - Create platform-specific bundles - Set realistic KPIs for each channel 2. Data Strategy - Accept data limitations on newer platforms - Focus on first-party data where possible - Build cross-channel customer profiles - Use creative solutions for retention 3. Team Structure - Specialized expertise per channel - Clear ownership of metrics - Flexibility to shift resources - Mix of in-house and agency support The brands that will win aren't the ones just running around trying to be everywhere - they're the ones being intentional about how they show up in each place. Success also isn't about ideal profit extraction across all channels. It's about understanding each channel's role in your broader ecosystem and optimizing accordingly. Key Takeaway: Don't try to make every channel work the same way. Start building channel-specific strategies that work together to drive overall growth.