UX Design For Customer Support Tools

🔎 How To Redesign Complex Navigation: How We Restructured Intercom’s IA (https://lnkd.in/ezbHUYyU), a practical case study on how the Intercom team fixed the maze of features, settings, workflows and navigation labels. Neatly put together by Pranava Tandra. 🚫 Customers can’t use features they can’t discover. ✅ Simplifying is about bringing order to complexity. ✅ First, map out the flow of customers and their needs. ✅ Study how people navigate and where they get stuck. ✅ Spot recurring friction points that resonate across tasks. 🚫 Don’t group features based on how they are built. ✅ Group features based on how users think and work. ✅ Bring similar things together (e.g. Help, Knowledge). ✅ Establish dedicated hubs for key parts of the product. ✅ Relocate low-priority features to workflows/settings. 🤔 People don’t use products in predictable ways. 🤔 Users often struggle with cryptic icons and labels. ✅ Show labels in a collapsible nav drawer, not on hover. ✅ Use content testing to track if users understand icons. ✅ Allow users to pin/unpin items in their navigation drawer. One of the helpful ways to prioritize sections in navigation is by layering customer journeys on top of each other to identify most frequent areas of use. The busy “hubs” of user interactions typically require faster and easier access across the product. Instead of using AI or designer’s mental model to reorganize navigation, invite users and run a card sorting session with them. People are usually not very good at naming things, but very good at grouping and organizing them. And once you have a new navigation, test and refine it with tree testing. As Pranava writes, real people don’t use products in perfectly predictable ways. They come in with an infinite variety of needs, assumptions, and goals. Our job is to address friction points for their realities — by reducing confusion and maximizing clarity. Good IA work and UX research can do just that. [Useful resources in the comments ↓] #ux #IA

Following user feedback is a Product Management virtue. Is there an actual way to implement it, between all the noise, bugs, and stakeholder requests? Well… Most teams claim they are customer-driven. Yet the moment you open Zendesk, App Store reviews, survey results, and Slack threads, you instantly remember why everyone quietly avoids this work. Feedback is everywhere, contradictory, emotional, duplicated, and nearly impossible to turn into decisions.  It is chaos disguised as “insights.” This is why the new Amplitude AI Feedback release caught my attention and made it all the easier to decide to partner with them on this update. It successfully connects what users say with what they actually do, in one workflow. No extra tools.  No extra tabs. You see their words, frustrations, and praise. You see their behavior. And AI transforms it into ranked themes, rising trends, top requests, and complaints. Noise turns into clarity. Opinions turn into patterns. Patterns turn into action. And because it is native inside Amplitude, it kills the biggest problem in feedback work: Fragmentation. Everything flows into analytics, session replay, and cohorts, creating a full loop from insight to fix. You can trace why an issue matters, how many users care, how it impacts behavior, and which actions you should take. Finally, a single source of truth for PMs, UX, CX, and marketing. I’m also genuinely impressed with the supported sources of feedback: App Store, Google Play, Zendesk, Intercom, Freshdesk, Salesforce Service, Gong, Trustpilot, G2, Reddit, Discord, and X. Slack arrives in Q1, and there will be more! If you ever felt overwhelmed by feedback, this is one of the first attempts I have seen that genuinely solves the operational pain, not just the reporting part. It launches… Today! Take a look: https://lnkd.in/dAJKeTez What was the most successful update you know that came from the product’s users? Let me know in the comments. #productmanagement #productmanager #userfeedback

“Why don’t our customers use the Help Center?” It’s one of the most common frustrations I hear from CS and Support teams. They think: 𝘐𝘵’𝘴 𝘴𝘰 𝘮𝘶𝘤𝘩 𝘦𝘢𝘴𝘪𝘦𝘳! 𝘐𝘵’𝘴 𝘢𝘷𝘢𝘪𝘭𝘢𝘣𝘭𝘦 24/7! 𝘐𝘵’𝘴 𝘳𝘪𝘨𝘩𝘵 𝘵𝘩𝘦𝘳𝘦 𝘪𝘯 𝘧𝘳𝘰𝘯𝘵 𝘰𝘧 𝘵𝘩𝘦𝘮! 𝘚𝘰 𝘸𝘩𝘺 𝘥𝘰 𝘤𝘶𝘴𝘵𝘰𝘮𝘦𝘳𝘴 𝘬𝘦𝘦𝘱 𝘦𝘮𝘢𝘪𝘭𝘪𝘯𝘨 𝘮𝘦 𝘰𝘳 𝘤𝘢𝘭𝘭𝘪𝘯𝘨 𝘴𝘶𝘱𝘱𝘰𝘳𝘵 𝘪𝘯𝘴𝘵𝘦𝘢𝘥? 𝗛𝗲𝗿𝗲’𝘀 𝘁𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺: most teams think about Help Center usage as an adoption issue. But it’s not about 𝘢𝘥𝘰𝘱𝘵𝘪𝘰𝘯. It’s about 𝘴𝘶𝘤𝘤𝘦𝘴𝘴. Customers won’t adopt your Help Center just because it exists. They’ll adopt it after they experience success with it once. So instead of pushing adoption, we need to engineer success. 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝟯 𝘄𝗮𝘆𝘀 𝗜’𝘃𝗲 𝘀𝗲𝗲𝗻 𝘁𝗵𝗶𝘀 𝗱𝗼𝗻𝗲: 𝟭. 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲 𝗶𝘁 𝗶𝗻𝘁𝗼 𝗼𝗻𝗯𝗼𝗮𝗿𝗱𝗶𝗻𝗴. When a customer asks a question, pull up the Help Center with them. Let them see it solve their problem in real time.    𝟮. 𝗨𝘀𝗲 𝗶𝘁 𝗮𝘀 𝗮 𝗹𝗲𝗮𝘃𝗲-𝗯𝗲𝗵𝗶𝗻𝗱. At the end of onboarding calls, when you assign homework, leave them on the exact Help Center articles they’ll need to complete it.    𝟯. 𝗠𝗼𝗱𝗲𝗹 𝘁𝗵𝗲 𝗯𝗲𝗵𝗮𝘃𝗶𝗼𝗿 𝘆𝗼𝘂𝗿𝘀𝗲𝗹𝗳. In support or CS conversations, don’t just answer, show the Help Center article you rely on. When customers see you use it, they’ll start trusting it too. The principle is simple: Adoption doesn’t drive success. Success drives adoption. When your customers experience success through the Help Center (or your AI chatbot), they’ll 𝘱𝘶𝘭𝘭 it into their workflow on their own. And that’s how you scale both support and customer success. Help your customers succeed once, and adoption will take care of itself. #customersuccess

When you're deploying AI agents for a CX function, having a good Knowledge Base is a non-negotiable. Why? When optimized, it can empower your AI agents to deliver fast, accurate responses. When neglected, it can leave customers frustrated and agents underperforming. If you want to make sure your help center actually HELPS, here are 5 strategies you can deploy: 1. Structure your content in a Q&A format with clear headings and concise instructions to make it easy for both customers and AI to find relevant information. 2. Use precise keywords. If you have membership tiers, explicitly say which tier you're talking about. 3. Update content regularly with release dates for new features and remove outdated articles. 4. Use visuals (carefully). Reference images and annotations can improve usability—just make sure you have the bandwidth to keep them accurate. 5. Make agents accessible by providing a clear link to the AI agent channels for when customers need help beyond the answers available to them. A lot of companies view help centers as a nice-to-have but the truth is, the ROI is massive. And if you're thinking of using (or already use) AI agents for your customer support, you need to keep it well maintained so the agents can: → Identify knowledge gaps → Make suggestions to make your documentation easier to understand When your help center is optimized, AI agents can perform at their best, which translates to happier customers and less workload for your team. Read the full article for more strategies we recommend—link in the comments! 👇

I used to think user research was easy. But then I switched to B2B. And oh boy... reality hit hard Back when I was working on a B2C product, I could run 10 user interviews in a day. Users would happily spend 45 minutes answering questions and testing new designs. I thought this was just regular product design. Turns out, I was riding a perfect wave of continuous discovery without even realizing it. Then I switched to B2B. And I admit it really felt scary at first. Users were just too busy to pick up my phone calls. It took 3 weeks to schedule 5 calls. Some users left a bad CSAT score with barely any comment. Damn. How can we build anything serious without ever talking to users? At that time, it really felt like an impossible task. And any way I tried to put it, there were just no efficient process to get those users on the phone. But then it hit me. What if the best discovery touch points weren’t designers or PMs at all? What if they were already happening… in sales calls, support chats, internal Slack threads? And we had this feedback scattered across tools, threads, and people. But no one was making sense of it. So we built a Feedback Management System. We plugged every feedback into a single source of truth directly in Notion: - Intercom conversations and Modjo calls with customers - Internal tickets from sales and support to discuss user pain points or feature requests - User feedback forms submitted on the platform All filtered and organized per team through Notion automations. Each designer spends 2 hours per week turning raw feedback into structured insights. Then each team reviews it together weekly, and it feeds product decisions and the roadmap. It’s simple. It’s scalable. And it changed everything. Product designers no longer design based on shaky assumptions or partial data. They're now the source of customer truth and alignment. In B2B, discovery doesn’t happen in a lab. It happens in the wild. You just need to know where to listen. #productdesign #uxdesign #userresearch

Some of you disagreed with my last post. Fair. Let's talk. Let me explain the topic a bit more and give you a deep dive into how I see the new process. The old way: Think → Research → Wireframe → Design → Spec → Hand off → Build → Test → Iterate Weeks. Sometimes months. Before anyone touches real code. The new way: 👉 Step 1: Start with a problem, not a doc. I don't need a full PRD. I need one thing. Example: "𝘗𝘦𝘰𝘱𝘭𝘦 𝘴𝘵𝘳𝘶𝘨𝘨𝘭𝘦 𝘵𝘰 𝘨𝘦𝘵 𝘩𝘰𝘯𝘦𝘴𝘵 𝘧𝘦𝘦𝘥𝘣𝘢𝘤𝘬 𝘰𝘯 𝘵𝘩𝘦𝘪𝘳 𝘱𝘰𝘳𝘵𝘧𝘰𝘭𝘪𝘰." That's it. That's the brief. 👉 Step 2: Build the ugliest working version. I open Lovable or Cursor and prompt my way to a prototype. Not a mockup. Not a Figma file. A real, clickable, functional thing. 30 minutes. Maybe an hour. 👉 Step 3: Use it. Don't refine it. Don't show it to anyone yet. Use it yourself like a real user would. Click every button. Try to break it. Feel where it's awkward. 👉 Step 4: Now design. This is where design skill actually matters. You're not guessing what the experience should feel like. You already know because you felt it. Now you fix what's broken, remove what's unnecessary, and polish what works. Maybe pivot or try other solutions. 👉 Step 5: Show it, don't spec it. Instead of a 20-page spec, I send a link. "Here, try this. What's confusing?" Real feedback on a real thing beats hypothetical feedback on a hypothetical thing every single time. 👉 Step 6: Iterate in minutes, not weeks. Here's where this workflow really pulls ahead. Someone says, "This flow is confusing." You don't update a Figma file, write a ticket, and wait for the next sprint. You open Cursor, fix it, and send a new link. Same conversation. Same day. The feedback loop goes from weeks to hours. Sometimes minutes. And each round gets sharper because you're iterating on something real. 3-4 rounds of this, and you have something more validated than most products get after months of traditional process. 👉 Step 7: Document what you built, not what you plan to build. Documentation becomes a record, not a prediction. It's accurate because the thing already exists. You can do it at the end or during the process. Why this works: You make decisions with information instead of assumptions. You eliminate 80% of the back-and-forth. You design from experience, not imagination. And you iterate at the speed of conversation, not the speed of sprints. Why it feels wrong at first: Because we were trained to think before we build. And thinking first felt responsible. But we did that because we couldn't build. Now we can. And I don't think it's about ignoring thinking. (𝘔𝘢𝘯𝘺 𝘰𝘧 𝘺𝘰𝘶 𝘢𝘤𝘤𝘶𝘴𝘦𝘥 𝘮𝘦 𝘰𝘧 𝘵𝘩𝘢𝘵) I believe it's about doing it at every step. Refining it based on real feedback. Insights you can get internally and from user testing. If you're still reading this, let me know what you think about it all. ✌️

Customer support is highly personalized, requiring empathy and nuanced understanding—qualities that many believe AI cannot replicate. As part of our course, AI in Business Applications, my team and I worked on a project that leverages Generative AI to enhance, not replace, the human aspect of customer support. By combining Large Language Models (LLMs) with human oversight, we created a scalable, efficient, and context-aware system tailored for support-heavy environments. ▶️The Reality of AI in Personalized Support AI tools like LLMs are not here to replace human agents but to complement them. However, skepticism remains due to the following limitations of LLMs: 1. Lack of Empathy: AI struggles to understand emotional nuances, which are often critical in support scenarios. 2. Generic Responses: LLMs may offer answers that lack the deep personalization customers expect. 3. Hallucinations: AI can occasionally generate inaccurate or misleading responses when context is unclear. 4. Complexity of Issues: AI might fall short in handling multi-layered or highly sensitive customer queries. 💡Our Solution: Human-AI Collaboration To address these challenges, we implemented a hybrid system that leverages AI’s efficiency and human agents’ empathy and expertise: Fine-Tuning for Accuracy: By training the AI on domain-specific data (e.g., product manuals, FAQs, past conversations), we ensured it could handle routine inquiries with precision. Retrieval-Augmented Generation (RAG): This framework enhances the AI’s reliability by pulling accurate, up-to-date information from a structured knowledge base before generating responses. Escalation to Human Agents: For personalized or emotionally charged cases, the AI seamlessly hands off the conversation to a human agent, ensuring customers feel heard and valued. 🎯How This Enhances Customer Support Efficiency: AI handles repetitive, straightforward queries, freeing human agents to focus on complex, high-value interactions. Scalability: With AI assisting in routine tasks, businesses can scale support operations without compromising quality. Empowered Human Agents: By providing agents with AI-curated insights, they can deliver faster, more informed, and empathetic solutions. Round-the-Clock Support: AI ensures customers receive instant responses to basic queries, even outside business hours. ⚖️A Balanced Approach The key takeaway? AI is not a replacement but a tool to enhance human capabilities. While it streamlines processes and improves efficiency, the human touch remains central in building trust and loyalty with customers. This project deepened my understanding of how AI can solve business challenges while respecting the personalized nature of customer support. By combining Generative AI with thoughtful design and human collaboration, we can create systems that are both powerful and people-centric. #AI #GenerativeAI #CustomerSupport #HumanAI #BusinessInnovation #HybridApproach #AIinBusiness

“Wait, the information was there? I just couldn’t find it.” This is one of the common moments in UX research. A user fails to complete a task, but when we review the product, the content technically exists. The help article is there. The policy is there. The product detail is there. The answer is somewhere in the system. So why did the user miss it? Information foraging models have a useful explanation for this: users do not navigate toward information itself. They navigate toward cues that seem to promise information. Links, labels, headings, categories, snippets, and menu items all act as signals. If those signals suggest that the answer is nearby, users keep going. If the signals are weak, vague, or misleading, users backtrack, switch strategies, search, or leave. Findability is not just about whether the content exists. A page can contain the right answer and still fail if the path to that page does not “smell” right. In Information Foraging Theory, users are treated as adaptive information seekers. Like animals foraging for food, they balance expected value against effort. They are constantly making small judgments: Does this label sound relevant? Is this category worth exploring? Is it worth continuing here, or would another path be better? The key concept is information scent. A link or label has strong scent when it helps users infer that they are moving closer to their goal. Weak scent explains why users ignore the correct path even when it is visible. Misleading scent explains why users confidently follow the wrong path. High path cost explains why users abandon before reaching the answer, even when each individual step seems defensible from a design perspective. SNIF-ACT makes this even more useful for UX research because it models navigation as a sequence of cognitive decisions. It helps explain which link users are likely to select, how long they evaluate a page, when they backtrack, when they switch strategies, and when they abandon the path. In other words, it gives us a way to explain failure as a breakdown in cue evaluation, scent accumulation, or threshold crossing. This lens is useful for information architecture, taxonomy testing, menu labels, help centers, support portals, knowledge bases, e-commerce discovery, and enterprise systems where people need to locate policy or procedural information. So when users say, “I couldn’t find it,” and the team says, “But it was there,” the question is not just whether the content existed. But : Did the path smell like the answer?

Pylon has long been on my radar as an industry trendsetter and thought leader. Scaling support is often an afterthought for many startups, so it’s refreshing to see a guide that assesses these challenges early on. That said, I’d like to add some clarifications regarding one critical area: managing knowledge effectively as you scale. First, while the piece rightly justifies the need for a Help Center, it describes it as a "giant FAQ". However, a true Help Center (or Knowledge Base) should be much more than that. It's not just about answering repetitive questions but about empowering users with a structured, searchable repository of comprehensive guides, troubleshooting tips, and best practices. This is the foundation of your customer self-service strategy. Here are a few points I believe could enhance the perspective: 1. Waiting until you're answering 20+ questions per day is a reactive approach. Instead, begin building your knowledge base as soon as patterns emerge in customer queries. It’s much easier to scale a well-laid foundation than to backfill a disorganized structure. 2. When multiple people contribute to documentation, style inconsistencies can creep in. Establishing a style guide early- covering tone, formatting, and terminology - ensures that the Help Center feels cohesive and professional, no matter who writes the content. 3. Articles need regular reviews and updates to stay relevant. As your product evolves, your Help Center should evolve too. Assign ownership to specific team members and create a review cadence to ensure nothing becomes outdated. 4. Meeting customers "where they are" with Slack or chat support is great, but a Help Center should be the go-to for common queries. A well-designed, user-friendly Help Center doesn’t just deflect support tickets - it enhances the customer experience by enabling them to find answers independently. 5. Invest in tagging, categorization, and analytics tools from the start. This will make scaling easier as your needs grow and support AI-driven search or predictive assistance when you reach Series B. Pylon’s guide is a fantastic resource and a great starting point for this conversation. Managing knowledge effectively is just as important as hiring the right team and choosing the right channels. I wonder how others are approaching knowledge management at the Series A stage. From my on-site experience discussing knowledge management at Web Summit, it seems this area is often overlooked. #KaterynaTracksUpdates

Why do customers keep emailing you instead of using your Help Center? It’s not laziness. And it’s not a “training problem.” A few months ago, I was reviewing metrics for a fast-growing SaaS client. Their Help Center was beautifully designed, packed with guides, FAQs and tutorials. They had invested heavily. Yet support tickets were spiking and CSMs were constantly firefighting instead of driving value. At first, everyone blamed adoption: “𝘊𝘶𝘴𝘵𝘰𝘮𝘦𝘳𝘴 𝘫𝘶𝘴𝘵 𝘥𝘰𝘯’𝘵 𝘸𝘢𝘯𝘵 𝘵𝘰 𝘳𝘦𝘢𝘥 𝘢𝘳𝘵𝘪𝘤𝘭𝘦𝘴.” “𝘛𝘩𝘦𝘺 𝘱𝘳𝘦𝘧𝘦𝘳 𝘵𝘢𝘭𝘬𝘪𝘯𝘨 𝘵𝘰 𝘴𝘰𝘮𝘦𝘰𝘯𝘦.” But when we dug deeper, the story was different. The Help Center existed but it didn’t deliver success the first time. Customers struggled to find what they needed, guessed which article applied and often failed before giving up. Every failed attempt became a support ticket and a missed opportunity for CS to drive adoption, engagement and outcomes. That’s when it clicked: 𝘈𝘥𝘰𝘱𝘵𝘪𝘰𝘯 𝘥𝘰𝘦𝘴𝘯’𝘵 𝘥𝘳𝘪𝘷𝘦 𝘴𝘶𝘤𝘤𝘦𝘴𝘴. 𝘚𝘶𝘤𝘤𝘦𝘴𝘴 𝘥𝘳𝘪𝘷𝘦𝘴 𝘢𝘥𝘰𝘱𝘵𝘪𝘰𝘯. Here’s how we fixed it, with CS and Support aligned and subtle AI helping where it mattered most: 1️⃣ Guide customers through success during onboarding. CS teams solved real problems using the Help Center with customers, showing its impact immediately. 2️⃣ AI to recommend the right content. Instead of leaving customers to search, AI surfaced the most relevant articles in their workflow, turning friction into fast wins. 3️⃣ Embed success into the workflow. Proactively place Help Center links and resources exactly where customers need them; in emails, in-app prompts and task reminders. When guidance is built into the workflow, customers experience success naturally, without chasing answers. 4️⃣ Turn early wins into habits. Celebrate and highlight every time a customer solves a problem using the Help Center. Share success stories in onboarding calls, check-ins or internal team updates. When customers see results and recognize the value, using the Help Center becomes a habit, not a task. Within weeks, customers started using the Help Center naturally. CS could focus on driving adoption, adoption led to better engagement and engagement created measurable outcomes. The principle is simple and often overlooked: 𝘛𝘰𝘰𝘭𝘴 𝘥𝘰𝘯’𝘵 𝘨𝘦𝘵 𝘢𝘥𝘰𝘱𝘵𝘦𝘥. 𝘚𝘶𝘤𝘤𝘦𝘴𝘴 𝘨𝘦𝘵𝘴 𝘢𝘥𝘰𝘱𝘵𝘦𝘥. 𝘏𝘦𝘭𝘱 𝘤𝘶𝘴𝘵𝘰𝘮𝘦𝘳𝘴 𝘴𝘶𝘤𝘤𝘦𝘦𝘥 𝘰𝘯𝘤𝘦 𝘢𝘯𝘥 𝘵𝘩𝘦𝘺’𝘭𝘭 𝘬𝘦𝘦𝘱 𝘤𝘰𝘮𝘪𝘯𝘨 𝘣𝘢𝘤𝘬. How are your CS and Support teams working together to create first-time success for your customers?