Heuristic Evaluation In UX

Clean code is nice. But scalable architecture? That’s what makes you irreplaceable. Early in my journey, I thought “writing clean code” was enough… Until systems scaled. Teams grew. Bugs multiplied. That’s when I discovered Design Patterns, and things started making sense. Here’s a simple breakdown that can save you hundreds of hours of confusion. 🔷 Creational Patterns: Master Object Creation These patterns handle how objects are created. Perfect when you want flexibility, reusability, and less tight coupling. 💡 Use these when: You want only one instance (Singleton) You need blueprints to build complex objects step-by-step (Builder) You want to switch object types at runtime (Factory, Abstract Factory) You want to duplicate existing objects efficiently (Prototype) 🔷 Structural Patterns: Organise the Chaos Think of this as the architecture layer. These patterns help you compose and structure code efficiently. 💡 Use these when: You’re bridging mismatched interfaces (Adapter) You want to wrap and enhance existing objects (Decorator) You need to simplify a complex system into one entry point (Facade) You’re building object trees (Composite) You want memory optimization (Flyweight) You want to control access and protection (Proxy, Bridge) 🔷 Behavioural Patterns: Handle Interactions & Responsibilities These deal with how objects interact and share responsibilities. It’s about communication, delegation, and dynamic behavior. 💡 Use these when: You want to notify multiple observers of changes (Observer) You’re navigating through collections (Iterator) You want to encapsulate operations or algorithms (Command, Strategy) You need undo/redo functionality (Memento) You need to manage state transitions (State) You’re passing tasks down a chain (Chain of Responsibility) 📌 Whether you're preparing for interviews or trying to scale your application, understanding these 3 categories is a must: 🔹 Creational → Creating Objects 🔹 Structural → Assembling Objects 🔹 Behavioral → Object Interaction & Responsibilities Mastering these gives you a mental map to write scalable, reusable, and testable code. It’s not about memorising them, it's about knowing when and why to use them. #softwareengineering #systemdesign #linkedintech #sde #connections #networking LinkedIn LinkedIn News India

From "Raw" streams to "Refined" insights! Explore the 9 essential design patterns every data engineer must master. 𝗜𝗻𝗴𝗲𝘀𝘁𝗶𝗼𝗻 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 Highway, local road, and service lane. → Batch = scheduled trucks arriving at night with bulk goods. → Streaming = live traffic flowing constantly through highways. → CDC = only sending changed parcels instead of whole trucks. 𝘊𝘩𝘰𝘰𝘴𝘦 𝘣𝘢𝘴𝘦𝘥 𝘰𝘯 𝘭𝘢𝘵𝘦𝘯𝘤𝘺 𝘯𝘦𝘦𝘥𝘴, 𝘯𝘰𝘵 𝘩𝘺𝘱𝘦.  • 𝗦𝘁𝗼𝗿𝗮𝗴𝗲 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 Warehouse, open yard, and hybrid mall. → Data lake = huge yard storing everything as‑is. → Warehouse = neatly labeled shelves for fast picking. → Lakehouse = yard + warehouse with rules and performance. 𝘚𝘵𝘰𝘳𝘢𝘨𝘦 𝘤𝘩𝘰𝘪𝘤𝘦 𝘥𝘦𝘧𝘪𝘯𝘦𝘴 𝘥𝘰𝘸𝘯𝘴𝘵𝘳𝘦𝘢𝘮 𝘱𝘦𝘳𝘧𝘰𝘳𝘮𝘢𝘯𝘤𝘦 𝘢𝘯𝘥 𝘤𝘰𝘴𝘵.  • 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 Kitchen prep before vs after storage. → ETL = wash and cut vegetables before refrigerating. → ELT = store groceries first, prep when needed. → Incremental = only recook what actually changed today. 𝘌𝘓𝘛 𝘧𝘰𝘳 𝘤𝘭𝘰𝘶𝘥 𝘴𝘤𝘢𝘭𝘦, 𝘌𝘛𝘓 𝘧𝘰𝘳 𝘤𝘰𝘮𝘱𝘭𝘪𝘢𝘯𝘤𝘦-𝘩𝘦𝘢𝘷𝘺 𝘥𝘰𝘮𝘢𝘪𝘯𝘴.  • 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 Factory manager and sensors. → DAGs = manager’s checklist: step‑by‑step run order. → Event‑driven = machines start when a sensor is triggered. 𝘌𝘷𝘦𝘯𝘵-𝘥𝘳𝘪𝘷𝘦𝘯 𝘥𝘦𝘤𝘰𝘶𝘱𝘭𝘦𝘴 𝘴𝘺𝘴𝘵𝘦𝘮𝘴, 𝘋𝘈𝘎𝘴 𝘦𝘯𝘴𝘶𝘳𝘦 𝘰𝘳𝘥𝘦𝘳.  • 𝗥𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 Crash‑safe roads. → Idempotent jobs = same route, same result, even if retried. → Retries + dead‑letter queues = tow truck and parking yard. → Backfills = replaying yesterday’s route to fix delivery errors. 𝘋𝘦𝘴𝘪𝘨𝘯 𝘧𝘰𝘳 𝘧𝘢𝘪𝘭𝘶𝘳𝘦 𝘧𝘳𝘰𝘮 𝘥𝘢𝘺 𝘰𝘯𝘦.  • 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 & 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 Building codes and inspectors. → Validation = safety checks before people move in. → Schema evolution = renovating without collapsing the building. → Lineage = city map showing every road from source to square. 𝘎𝘰𝘷𝘦𝘳𝘯𝘢𝘯𝘤𝘦 𝘦𝘯𝘢𝘣𝘭𝘦𝘴 𝘵𝘳𝘶𝘴𝘵, 𝘯𝘰𝘵 𝘣𝘶𝘳𝘦𝘢𝘶𝘤𝘳𝘢𝘤𝘺.  • 𝗦𝗲𝗿𝘃𝗶𝗻𝗴 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 Front desks and ticket counters. → Semantic layer = one menu of official dish names and prices. → APIs = ticket windows giving controlled, metered access. 𝘏𝘰𝘸 𝘥𝘢𝘵𝘢 𝘪𝘴 𝘤𝘰𝘯𝘴𝘶𝘮𝘦𝘥 𝘮𝘢𝘵𝘵𝘦𝘳𝘴 𝘢𝘴 𝘮𝘶𝘤𝘩 𝘢𝘴 𝘱𝘳𝘰𝘤𝘦𝘴𝘴𝘪𝘯𝘨.  • 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 Express lanes and parking zones. → Partitioning = dedicated lanes for different destinations. → Caching = small local store so you skip the long supermarket. → Tiered storage + on‑demand compute = pay only for busy hours. 𝘖𝘱𝘵𝘪𝘮𝘪𝘻𝘦 𝘲𝘶𝘦𝘳𝘪𝘦𝘴, 𝘯𝘰𝘵 𝘫𝘶𝘴𝘵 𝘪𝘯𝘧𝘳𝘢𝘴𝘵𝘳𝘶𝘤𝘵𝘶𝘳𝘦. Illustration Credits: Shalini Goyal Stay tuned for more such data engineering concepts and analogies.😉

Sorted Array. Constraint: O(n). Question: Pair Sum. If your brain didn't scream "Two Pointers" immediately, you are memorizing, not recognizing. DSA patterns are not meant to be memorized.  They are meant to be detected based on signals. Based on the "3-Prong Pattern Detection System," here is the technical breakdown of how to map signals to solutions: 1. The Input Signal (What data structure is this?) - Sorted Array: Immediately consider Two Pointers (for pair sums) or Binary Search (if you need to find a boundary or the search space shrinks). - Tree/Graph: If it's hierarchical, think Binary Tree/BST or Recursion. If it's about connections or reachability, think BFS/DFS. - Linked List: If you need in-place edits without index access, use the Fast & Slow Pointers technique. 2. The Question Signal (What is being asked?) - "Top K elements" or "Kth largest": This is a hard trigger for a Heap (Priority Queue). - "Subarray" or "Contiguous elements": This almost always points to a Sliding Window. - "Permutations," "Subsets," or "Explore all choices": You are looking at Backtracking. - "Shortest Path" in an unweighted graph: This is BFS. 3. The Constraint Signal (What is the speed limit?) - O(log n): You must cut the search space in half. Binary Search. - O(n): You likely need a single pass. Two Pointers, Sliding Window, or Hashing. - O(1) Lookup: You need a Hash Map or Set. If you see a problem asking for the "Longest substring with distinct characters," run the system: - Input: String. - Question: Longest substring (contiguous). - Constraint: Efficiency. - Pattern: Sliding Window. Stop guessing. Start detecting. What is the one pattern you struggle to identify the most? ♻️ Repost to save this technical framework for your next interview.

 Interview Question: "With 100 pages, do you create 100 Page Objects?" The answer reveals how well you understand design patterns in test automation. Here's my approach using patterns every automation engineer should know: Singleton Pattern - Think of WebDriver/Configurations file as your single key. I use Singleton to ensure only one instance exists throughout the test execution. No matter how many pages I create, they all share the same instance - no confusion, no conflicts. (Quick Note: If WebDriver is used with SingleTon pattern - it restricts parallel execution) Page Components Pattern - Real websites have repeating pieces - headers, footers, search bars, product cards. I create reusable components like HeaderComponent and ProductCardComponent that can be shared across multiple pages. Think LEGO blocks - build once, use everywhere. Feature-Based Pages - Instead of creating LoginPage, RegisterPage, ForgotPasswordPage separately, I create an AuthenticationPage that handles all login-related features. Same logic applies to ShoppingPages, AccountPages, and CheckoutPages. Group by functionality, not by URL. Builder Pattern - When creating complex page objects or test data, Builder pattern makes it elegant. Instead of messy constructors with 10 parameters, I chain methods: new UserBuilder().withName("John").withEmail("test@email.com").build() - much cleaner and readable. Fluent Interface - This makes your page interactions read like natural language: loginPage.enterUsername("user").enterPassword("pass").clickLogin().verifyDashboard() - each method returns the page object, allowing smooth chaining. (Quick Note: Fluent and Builder patterns introduce Tight Coupling) Common Utilities (BasePage & BaseTest) - BasePage contains shared functionality like wait methods, screenshot capture, and common element interactions. BaseTest handles driver setup, teardown, and reporting. These base classes eliminate duplicate code across your framework. Page Object Model (POM) - This is your foundation pattern. Instead of scattering element locators across test methods, POM creates a clean separation where each page becomes a class with its own elements and methods. But here's the key - you don't need 100 classes for 100 pages. The Magic Result: 100 pages become just 8-10 well-designed classes that handle everything efficiently. Your framework becomes a Swiss Army knife - compact but incredibly powerful. Remember: Great automation isn't about having more classes - it's about having smarter patterns that scale effortlessly. What's your favorite design pattern for test automation? Share below! 👇 -x-x- Crack your next SDET Coding Round with guided video sessions: https://lnkd.in/ggXcYU2s #japneetsachdeva

💡 Practical Heuristic Evaluation Checklist Heuristic evaluation is a usability inspection practice where experts assess a user interface against a set of established principles. It’s a cost-effective way to uncover usability problems early in the design process, without full-scale testing. Below is a checklist I’ve created using Jakob Nielsen’s 10 Usability Heuristics: Visibility of System Status ☐ Does the system provide immediate feedback for user actions (clicks, taps, form submissions)? ☐ Are loading states, progress indicators, or success confirmations clearly shown? ☐ Is the system status updated in real time where needed? Notes / Issues: Add notes with examples and suggested fixes. Severity: 0 = Cosmetic, 1 = Minor, 2 = Major, 3 = Critical Match Between System and Real World ☐ Does the interface use terminology familiar to the target audience? ☐ Are icons, symbols, and visuals intuitive and culturally appropriate? ☐ Does the flow mimic real-world processes where applicable? Notes / Issues: Severity: User Control & Freedom ☐ Can users easily undo or redo actions? ☐ Is there a clear way to cancel ongoing operations? ☐ Can users backtrack without losing progress or data? Notes / Issues: Severity: Consistency & Standards ☐ Are similar elements and actions consistent in appearance and behavior? ☐ Does the design follow platform-specific guidelines? ☐ Are labels and terminology used consistently across the product? Notes / Issues: Severity: Error Prevention ☐ Are error-prone actions guarded by confirmations or warnings? ☐ Is form validation immediate and clear before submission? ☐ Are destructive actions reversible? Notes / Issues: Severity: Recognition Rather Recall ☐ Are options, menus, and controls visible without forcing users to remember information? ☐ Is necessary context displayed on the same screen where decisions are made? ☐ Are past actions and history visible where needed? Notes / Issues: Severity: Flexibility and Efficiency of Use ☐ Are there shortcuts, keyboard commands, or accelerators for power users? ☐ Can users personalize or customize settings? ☐ Is navigation optimized for both beginners and experts? Notes / Issues: Severity: Aesthetic and Minimalist Design ☐ Is the layout clean, with no unnecessary information or visual clutter? ☐ Are typography, spacing, and alignment used effectively for readability? ☐ Is visual hierarchy clear, highlighting the most important actions? Notes / Issues: Severity: Help Users Recognize, Diagnose, and Recover from Errors ☐ Are error messages in plain language? ☐ Do they clearly explain the cause of the problem and how to fix it? ☐ Are error messages visually distinct but non-intrusive? Notes / Issues: Severity: Help and Documentation ☐ Is help content easy to find within the interface? ☐ Are tooltips, inline hints, or guides available where needed? ☐ Is documentation concise, searchable, and up to date? Notes / Issues: Severity: 🖼️ 10 Heuristics by Maze #UX #UI #uxdesign #design

10 most common Design Patterns and when to use them (with Real-World examples): 1) 𝐒𝐢𝐧𝐠𝐥𝐞𝐭𝐨𝐧 𝐏𝐚𝐭𝐭𝐞𝐫𝐧: When you need a single instance of a class that's globally accessible. Example -> Database connections: Ensures only one connection throughout the app's life. 2) 𝐁𝐮𝐢𝐥𝐝𝐞𝐫 𝐏𝐚𝐭𝐭𝐞𝐫𝐧: When you need to construct complex objects step by step. Example -> Meal Builder: Create a builder for building customized meals with appetizers, main courses, sides, and desserts. 3) 𝐀𝐝𝐚𝐩𝐭𝐞𝐫 𝐏𝐚𝐭𝐭𝐞𝐫𝐧: When you need to make an interface of one class compatible with another class. Example -> Payment Gateway Integration: Adapt various payment gateways (PayPal, Stripe, Square) to a common interface for processing transactions. 4) 𝐅𝐚𝐜𝐭𝐨𝐫𝐲 𝐌𝐞𝐭𝐡𝐨𝐝 𝐏𝐚𝐭𝐭𝐞𝐫𝐧: When you want to create objects but leave the exact type to be determined by the subclasses. Example -> Notification Services: Create a factory method to produce notifications (email, SMS, push notifications) depending on the audience and content. 5) 𝐏𝐫𝐨𝐭𝐨𝐭𝐲𝐩𝐞 𝐏𝐚𝐭𝐭𝐞𝐫𝐧: When you want to create new objects by copying an existing object, known as the prototype. Example -> Game Character Cloning: Duplicate game characters with different attributes. 6) 𝐃𝐞𝐜𝐨𝐫𝐚𝐭𝐨𝐫 𝐏𝐚𝐭𝐭𝐞𝐫𝐧: When you want to add new functionalities to an object dynamically without altering its structure. Example -> Text Formatting: Add formatting like bold, italic, and underline to text. 7) 𝐎𝐛𝐬𝐞𝐫𝐯𝐞𝐫 𝐏𝐚𝐭𝐭𝐞𝐫𝐧: When you need one-to-many dependency between objects, so when one object changes state, its dependents are notified and updated automatically. Example -> Weather Station: Broadcast weather changes to various devices. 8) 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝐏𝐚𝐭𝐭𝐞𝐫𝐧: When you want to define a family of algorithms, encapsulate each one, and make them interchangeable. Example -> Sorting Algorithms: Swap sorting strategies (quicksort, mergesort) at runtime. 9) 𝐂𝐨𝐦𝐩𝐨𝐬𝐢𝐭𝐞 𝐏𝐚𝐭𝐭𝐞𝐫𝐧: When you want to compose objects into tree structures to represent part-whole hierarchies. Example -> Hierarchical menu: Organization’s structure. 10) 𝐅𝐚𝐜𝐚𝐝𝐞 𝐏𝐚𝐭𝐭𝐞𝐫𝐧: When you want to make your system easy to use by providing a simplified interface to a set of interfaces in a subsystem. Example -> Home Automation: Control smart devices (lights, thermostats) through one interface.

In the past few months, I’ve reviewed 100+ of user research processes Most research isn’t failing because of lack of effort, but because it’s not designed to drive action. If your research isn’t influencing decisions, it’s just collecting dust. Here are the biggest mistakes I’ve seen and how to fix them 1. Treating research like a one-and-done project Stop running research in isolated phases and start embedding it throughout the entire product lifecycle. → Instead of conducting a single discovery study at the beginning of a project and moving on, build a lightweight rolling research program. Running bi-weekly customer interviews alongside the product roadmap can continuously feed insights into decision-making. → Rather than a one-time usability test before a big launch, implement a research checkpoint at each major development phase—discovery, prototyping, beta testing—to catch usability issues early and often. If research isn’t ongoing, insights will always arrive too late to influence decisions. 2. Focusing on methods instead of impact Stakeholders don’t care if you ran 30 interviews—they care about what changed because of them. → Instead of saying, “Users struggle with onboarding,” say, “Our research shows that simplifying the onboarding flow can reduce support tickets by 25%, saving the company $100K annually.” → Frame your findings in the context of business goals. Instead of focusing on frustration with navigation, highlight how improving navigation could lead to a 15% increase in product adoption over the next quarter. Your research should always tie back to revenue, retention, or efficiency, otherwise, it won’t be prioritized. 3. Drowning stakeholders in data Your job isn’t to dump everything you’ve learned—it’s to guide better decisions. Instead of handing over a 50-page report that no one reads, create a one-page executive summary that includes: - the problem identified - the impact on the business - 2-3 actionable recommendations - potential next steps If you’re running a usability study, instead of listing every issue found, prioritize the top three issues that, if fixed, will have the biggest impact on conversion rates. If stakeholders can’t find what matters quickly, they won’t act on it. 4. Working in silos Research isn’t a solo effort. Collaboration is key to making an impact. → Instead of presenting findings at the end of the project, run “research playback” sessions where stakeholders actively engage with raw findings helps teams internalize user challenges. → Co-creating research questions with your stakeholders ensures the insights align with their needs. Involving customer support teams in scoping research can help surface recurring pain points they hear daily. Make research a two-way conversation, not a broadcast. How do you think about and iterate on your research process? Join 10,000+ UXRs in becoming more strategic: https://lnkd.in/eR5M2geZ

So many product teams work on new features they believe will be a game-changer for users. But how do you really know if a feature will be adopted by users? This is where UX research comes in. As UX researchers, we can help identify the probability of feature adoption by digging deep into user needs, behaviors, and expectations. Here are some ways we measure and predict feature adoption: 1. User Interviews and Surveys: By speaking directly to users, we can gauge their interest in a new feature. Through surveys or interviews, we explore how they might use the feature, what problems it would solve for them, and how it fits into their current workflows. These qualitative insights give us an early understanding of potential adoption barriers. 2. Usability Testing: A feature may seem like a great idea on paper, but how do users actually interact with it? Conducting usability tests on prototypes allows us to see whether users understand the feature, how intuitive it is, and where they might get stuck. If the feature feels cumbersome, adoption rates will likely be lower. 3. Task Success Rate: This metric allows us to measure how easily users can complete tasks using the new feature. A low success rate indicates friction, and users are less likely to adopt a feature if it doesn’t make their experience easier. 4. User Journey Mapping: By mapping out the user journey, we can see where the new feature fits into the overall user experience. Does it make sense within the flow of their tasks? Are there unnecessary steps or points of confusion? A smooth, integrated feature is more likely to be adopted. 5. A/B Testing: Once a feature is live, we can run A/B tests to see if it’s driving the desired behavior. Does the feature increase engagement or task completion compared to the previous version? These quantitative insights allow us to measure real-world adoption and refine the feature based on user interactions. 6. Feature Feedback: After a feature is released, gathering feedback is key. By monitoring user comments, satisfaction scores, and support tickets, we can understand how users feel about the feature. Are they using it as intended? Are there any pain points that need addressing? As UX researchers, our role is to validate whether a feature truly meets user needs and fits within their daily tasks. We can predict adoption rates, identify potential issues early, and help product teams make informed decisions before launching a feature. How do you measure feature adoption in your research?

🧠 If You Can Explain These 12 Patterns, You’re Interview-Ready Last week, a friend of mine had a system design interview at FAANG compay. Midway through the round, the interviewer asked: "Can you explain some common microservices design patterns and when you’d use them?" Silence. He knew microservices. He had worked with APIs. He had deployed services to production. But when asked to structure the answer, his mind went blank. Not because he didn’t know. Because he didn’t organize what he knew. So let’s make sure that doesn’t happen to you. Here’s a simple way to think about the most important microservices patterns, the kind interviewers love to hear. 1. API Gateway One entry point for all client requests. Handles routing, auth, throttling. 2. Saga Pattern Manages distributed transactions using step-by-step execution with rollback logic. 3. Event Sourcing Stores changes as events instead of just saving the latest state. 4. CQRS Separates read and write operations for scalability. 5. Strangler Pattern Gradually replaces a monolith by moving features into microservices. 6. Service Discovery Services dynamically find each other without hardcoded URLs. 7. Circuit Breaker Stops calls to failing services to prevent cascading failures. 8. Bulkhead Isolates services/resources so one failure doesn’t take down everything. 9. Database per Service Each service owns its own database for autonomy and loose coupling. 10. Sidecar Attaches helper services (logging, monitoring, security) alongside the main service. 11. Retry Retries failed calls before marking them as failed. 12. API Aggregation Combines responses from multiple services into one optimized result. The Lesson In interviews at tier 1 companies, they’re not just testing knowledge. They’re testing: Can you structure your thoughts? Can you explain clearly? Can you choose the right pattern for the right problem? If you’re preparing for system design interviews, master patterns like these. Clarity beats memorization every time. Ankit Pangasa

Product Analyst Guide: User Flow Analysis As a product analyst, I have to find out user drop offs in key flows. Identifying these drop-off points helps me to make specific changes that can boost engagement and conversion rates. Here's my step-by-step method to find and solve issues in user flows: 𝟭. 𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝘆 𝗞𝗲𝘆 𝗨𝘀𝗲𝗿 𝗙𝗹𝗼𝘄𝘀 ⤷ Pinpoint the main paths users follow, like checkout or registration. ⤷ Focus on flows that are critical to your objectives. 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: For an e-commerce site, tracking the checkout process is essential. >> Solving Drop-Off: ⤷ Use heatmaps to see where users click most and least. ⤷ Track the average time spent on each page to spot potential issues. 𝟮. 𝗔𝗻𝗮𝗹𝘆𝘇𝗲 𝗗𝗿𝗼𝗽-𝗢𝗳𝗳 𝗣𝗼𝗶𝗻𝘁𝘀 ⤷ Identify steps with high drop-off rates. ⤷ Compare drop-off rates at different stages to find problem areas. 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: Many users abandon their carts on the payment page. >> Solving Drop-Off: ⤷ Check if there are usability issues on the payment page. ⤷ Compare abandonment rates before and after recent changes. 𝟯. 𝗜𝗻𝘃𝗲𝘀𝘁𝗶𝗴𝗮𝘁𝗲 𝗖𝗮𝘂𝘀𝗲𝘀 ⤷ Examine potential issues such as confusing forms/slow load times. ⤷ Gather user feedback to understand their frustrations. 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: Users find the payment page too complex and confusing. >> Solving Drop-Off: Conduct user interviews or surveys to pinpoint specific problems. Test different versions of the payment page to find the most effective design. 𝟰. 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗖𝗵𝗮𝗻𝗴𝗲𝘀 ⤷ Make targeted improvements based on your findings. ⤷ Simplify processes, enhance form usability and improve page load times. 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: Revise the payment page to be more user-friendly and offer more payment options. >> Solving Drop-Off: ⤷ Streamline the payment form and reduce the number of required fields. ⤷ Add progress indicators and clarify error messages. 𝟱. 𝗜𝘁𝗲𝗿𝗮𝘁𝗲 𝗮𝗻𝗱 𝗜𝗺𝗽𝗿𝗼𝘃𝗲 ⤷ Continue monitoring and refining based on new data. ⤷ Address any new drop-off points that arise and keep enhancing the user experience. 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: After initial improvements, additional optimizations may be necessary. >> Solving Drop-Off: ⤷ Regularly review user feedback and behavior to spot emerging issues. ⤷ Make iterative changes and measure their impact on user flow. Read the document below for end-to-end process.. ------------------------------------------------------------- 👉 Free Data Analyst Template (https://lnkd.in/gxrngzVg) ♻️ Found this post useful? Repost it! #product #productanalyst