UX Design For Customer Support Tools

👀 This dashboard is not flashy. Quite the opposite. It is clean, simple, and helps the reader find the most important information quickly. That comes from a few small but intentional design choices: 1️⃣ Dynamic slicer label “Data shown as on Jan-19 ending” tells the user that the dashboard shows the position up to the selected month, not just the numbers for January. 2️⃣ Red dot alert 🔴 The red dot beside the customer name immediately shows that this customer still has unpaid invoices. 3️⃣ Title used as a legend Billing is written in red and Receipts in blue, matching the chart lines. No separate legend needed. 4️⃣ A header that gives context Instead of simply saying “Invoices”, the header tells you there are 40 invoices, 5 are unpaid, and the outstanding balance is 7,825. 5️⃣ Subtle checkmarks ✔ The checkmarks make it easy to identify which invoices have been fully paid without adding another text column. 6️⃣ Thin green indicators 🟩 The green lines beside the Receipts column quickly show the months in which payments were received. 7️⃣ Plenty of white space Probably the most important design choice. It keeps the report readable and allows the numbers that matter to stand out. None of these things is impressive on its own. But together, they reduce cognitive load, improve clarity, and make the dashboard much easier to use. That is good dashboard design. Not adding colours, boxes, and shadows everywhere, but making every element useful. If you’re building dashboards, this is the kind of polish that sets your work apart.

📌 Most Dashboards Fail Because of Bad UX Here’s the hard truth: You can have the cleanest data and the most advanced models… But if your dashboard is confusing, cluttered, or hard to navigate? Nobody will use it. BI isn’t just about data. It’s about experience. Dashboards are in fact UX products and should be treated that way. Great dashboards don’t just “show data.” They guide attention. Simplify decisions. Reduce friction. And just like any great product, they follow strong UX principles: → Clear layout → Logical flow → Minimal cognitive load → Built for the user, not the developer Let’s break down the 3 dashboard principles that make this possible 👇 1️⃣ 𝐃𝐞𝐬𝐢𝐠𝐧 𝐖𝐢𝐭𝐡 𝐭𝐡𝐞 𝐄𝐧𝐝 𝐔𝐬𝐞𝐫 𝐢𝐧 𝐌𝐢𝐧𝐝 This is where most dashboards go wrong. They’re built from a technical perspective and not a business one. Before touching a single chart, ask: → Who is this dashboard for? → What do they care about? → What action do they need to take from it? → What single question should this dashboard answer? If a dashboard tries to do everything for everyone, it ends up doing nothing for anyone. Treat your dashboard like a product. Build it around one user persona and one decision-making flow. 2️⃣ 𝐆𝐮𝐢𝐝𝐞 𝐭𝐡𝐞 𝐄𝐲𝐞 𝐰𝐢𝐭𝐡 𝐚 𝐂𝐥𝐞𝐚𝐫 𝐋𝐚𝐲𝐨𝐮𝐭 A great dashboard feels effortless to use. You don’t need to explain how to read it because it guides the user by design. Here’s how to do it: 1) Follow a natural reading pattern (top-left to bottom-right) 2) Use consistent spacing, alignment, and visual hierarchy 3) Group related charts and KPIs together 4) Avoid visual noise (limit to 5–7 key visuals per view) Think of your dashboard like a story It should unfold logically and lead the user to an insight without them having to look for it. 3️⃣ 𝐔𝐬𝐞 𝐭𝐡𝐞 𝐑𝐢𝐠𝐡𝐭 𝐕𝐢𝐬𝐮𝐚𝐥 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐉𝐨𝐛 Just because you can use a radar chart or sunburst doesn't mean you should. The best dashboards use simple, familiar visuals that communicate clearly. Here’s a cheat sheet I use: ⤷ To show progress or results → Use Scorecards or KPIs ⤷ To show trends over time → Line Charts or Area Charts ⤷ To compare parts of a whole → Pie Charts or Bar Charts ⤷ To analyze distributions → Histograms or Bell Curves ⤷ To show multivariate complexity → Heatmaps, Bubble Charts, or Pivot Tables Here what you need to remember is prioritizing clarity over creativity. Your dashboard isn’t a dribble a piece of art. It’s a decision tool. The bottom line is: Dashboards aren’t “data displays.” They’re interfaces for decision-making. And just like a product interface, design is everything. ☑ Good UX = Faster insights ☑ Good flow = Higher adoption ☑ Good visuals = Better decisions Build with purpose. Structure with clarity. Design for people. That’s how Business Intelligence becomes actual business impact. #DataStrategy #BusinessIntelligence #DataAnalytics

Power BI filter panels are often harder to use than they should be. Why does the one on the right work better? 𝐆𝐫𝐨𝐮𝐩𝐢𝐧𝐠  Slicers are broken into sections instead of one long block. Best if you can do it in a logical order or just space them out evenly. It makes the selection options easier to scan and less overwhelming. 𝐒𝐩𝐚𝐜𝐢𝐧𝐠 It’s hard to see which label belongs to which slicer when everything is cramped. Adjusted vertical gaps make it instantly clearer. 𝐇𝐮𝐦𝐚𝐧 𝐫𝐞𝐚𝐝𝐚𝐛𝐢𝐥𝐢𝐭𝐲 HasCreditCard and IsActiveMember are true/false fields with 0/1 values and the default field names. The viewers have to decode them in their head. Make them human-readable instead: • Credit Card Holders: Holding / Not Holding • Active Members: Active / Inactive Also why use dropdown just for two options? Show them directly. 𝐈𝐦𝐩𝐫𝐨𝐯𝐞 𝐮𝐬𝐚𝐛𝐢𝐥𝐢𝐭𝐲 The panel on the left closes only when you click the filter button again. There is zero indication for that. Add buttons to interact directly on the filter panel: • Two visible options to close the panel (top-right X and bottom Close button), so users can pick the shorter path. • As a third option you can also add a scrim (overlay behind the panel) with a close action. Users can close the panel clicking anywhere on the dashboard. This is often an expected behavior from other tools and apps. • A clear filter button to reset everything with one click. It makes the users’ life easier. 𝐓𝐲𝐩𝐨𝐠𝐫𝐚𝐩𝐡𝐲 • Replace pure black with softer grays to reduce visual contrast and give an easier feel. • Deemphasize slicer labels (lighter, smaller) so they don’t compete with the values. • Keep the selection values darker and larger so they stand out more. • The Clear button is red to indicate a “destructive” action. When you click it, you lose the filter selection. These are small changes but together they add up to a much better experience. If you want to build this in Power BI I shared the tutorial in the comments. 👇 #powerbi #dataviz #reportdesign #dashboarddesign #uidesign

🧠 𝗚𝗶𝘃𝗲 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿𝘀 𝗪𝗵𝗮𝘁 𝗧𝗵𝗲𝘆 𝗥𝗲𝗮𝗹𝗹𝘆 𝗡𝗲𝗲𝗱 – 𝗦𝗺𝗮𝗿𝘁 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗶𝗻 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜! What I often see in dashboarding is that people deliver charts and expect consumers to somehow figure things out on their own, spending time extracting insights. That’s not how I envision the perfect dashboard. 𝗔 𝗴𝗿𝗲𝗮𝘁 𝗱𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱 𝘀𝗵𝗼𝘂𝗹𝗱 𝘀𝗽𝗲𝗮𝗸 𝗳𝗼𝗿 𝗶𝘁𝘀𝗲𝗹𝗳 𝗮𝗻𝗱 𝗴𝗶𝘃𝗲 𝗰𝗹𝗲𝗮𝗿 𝗮𝗻𝘀𝘄𝗲𝗿𝘀 - 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗺𝗮𝗸𝗶𝗻𝗴 𝘁𝗵𝗲 𝘂𝘀𝗲𝗿 𝘀𝗲𝗮𝗿𝗰𝗵 𝗳𝗼𝗿 𝘁𝗵𝗲𝗺. 💡 One technique I often use to ensure the client gets the answers they're seeking is something I like to call 𝘚𝘮𝘢𝘳𝘵 𝘐𝘯𝘴𝘪𝘨𝘩𝘵𝘴. It’s essentially a text summary of what’s in a specific section of the report. It highlights the most important information for the end user and explains the cause behind certain results. So instead of the user going back and forth, comparing results, or exporting charts and tables to Excel for their own analysis, they get a quick, clear summary of what’s happening. ⚡ This text can be 𝗳𝘂𝗹𝗹𝘆 𝗱𝘆𝗻𝗮𝗺𝗶𝗰, adjusting based on selected measures or other slicers - essentially anything that shifts the perspective of what's happening. If you use an HTML custom visual, it can also incorporate plenty of conditional formatting to meet user needs. 𝗜 𝗯𝗲𝗹𝗶𝗲𝘃𝗲 𝘁𝗵𝗲𝘀𝗲 𝘁𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 𝗮𝗿𝗲𝗻’𝘁 𝘄𝗶𝗱𝗲𝗹𝘆 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗲𝗱 𝗯𝗲𝗰𝗮𝘂𝘀𝗲: 1️⃣ It’s time-consuming, and unfortunately, developers often cut corners and settle for whats only "good enough". 2️⃣ Developers often don't know what consumers need and may hesitate to ask or lack the skills to find out. 𝗦𝗵𝗶𝗳𝘁 𝘆𝗼𝘂𝗿 𝗺𝗶𝗻𝗱𝘀𝗲𝘁 𝘁𝗼 𝗽𝘂𝘁 𝘁𝗵𝗲 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗳𝗶𝗿𝘀𝘁. 𝗟𝗼𝗼𝗸 𝗮𝘁 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂’𝘃𝗲 𝗰𝗿𝗲𝗮𝘁𝗲𝗱 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝘁𝗵𝗲𝗶𝗿 𝗲𝘆𝗲𝘀 𝗮𝗻𝗱 𝗮𝘀𝗸 𝘆𝗼𝘂𝗿𝘀𝗲𝗹𝗳 𝗶𝗳 𝗶𝘁 𝘁𝗿𝘂𝗹𝘆 𝗮𝗱𝗱𝗿𝗲𝘀𝘀𝗲𝘀 𝘁𝗵𝗲𝗶𝗿 𝗻𝗲𝗲𝗱𝘀! #analytics #data #powerbi #datavisualization #report #dashboard #reporting #visualization #microsoftpowerbi #pbicorevisuals #svg #html #customvisuals #UXDesign

𝗖𝗹𝗼𝘀𝗶𝗻𝗴 𝗼𝘂𝘁 𝘁𝗵𝗲 𝘄𝗲𝗲𝗸 𝘄𝗶𝘁𝗵 𝘀𝗼𝗺𝗲 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗱𝗲𝘀𝗶𝗴𝗻 𝗶𝗻𝘀𝗽𝗶𝗿𝗮𝘁𝗶𝗼𝗻 Dashboards don’t have to be walls of charts. You can use 𝘤𝘢𝘳𝘥𝘴 𝘢𝘯𝘥 𝘸𝘪𝘥𝘨𝘦𝘵𝘴 to create a modular, product-like experience that communicates faster. Looking at the example below, here are a few principles worth applying in your own builds: 𝗛𝗲𝗮𝗱𝗹𝗶𝗻𝗲 + 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝗽𝗮𝗶𝗿𝗶𝗻𝗴 Each card leads with a single KPI (e.g. $34,914 𝘛𝘰𝘵𝘢𝘭 𝘚𝘢𝘭𝘦𝘴), immediately followed by a small trend statement (e.g. 𝘥𝘦𝘤𝘳𝘦𝘢𝘴𝘦𝘥 𝘣𝘺 –$4,266). This balances clarity with context. 𝗠𝗶𝗰𝗿𝗼-𝘁𝗿𝗲𝗻𝗱𝘀 𝗺𝗮𝘁𝘁𝗲𝗿 Small arrows and deltas (+356 / –273) give instant feedback without forcing users to compare multiple charts. These can be implemented in Power BI using measures with conditional formatting. 𝗖𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝘁 𝗺𝗼𝗱𝘂𝗹𝗮𝗿 𝗹𝗮𝘆𝗼𝘂𝘁 Notice how every card has the same structure: KPI → supporting visual → legend/context. This consistency reduces cognitive load. In Power BI, use containers and grids to lock in spacing. 𝗠𝗶𝘅 𝗼𝗳 𝗰𝗵𝗮𝗿𝘁 𝘁𝘆𝗽𝗲𝘀, 𝗻𝗼𝘁 𝗰𝗵𝗮𝗿𝘁 𝗰𝗹𝘂𝘁𝘁𝗲𝗿 You see bar, donut, progress ring, and column charts - but each serves a clear role (composition, progress, forecast vs actual). The key is restraint: one message per widget. 𝗦𝗲𝗴𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗵𝗶𝗲𝗿𝗮𝗿𝗰𝗵𝘆 From customer segmentation to country performance, each card answers a narrow question. Think of them as 𝘮𝘪𝘤𝘳𝘰-𝘢𝘱𝘱𝘴 inside the report. Users scan, not explore. 👉 𝗙𝗼𝗿 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀: build KPIs as reusable measures with calculation groups, field parameters etc so you can drop them into any card without rework - or, why not give the new UDF feature a go! 👉 𝗙𝗼𝗿 𝗰𝗼𝗻𝘀𝘂𝗹𝘁𝗮𝗻𝘁𝘀: push clients toward 𝘸𝘪𝘥𝘨𝘦𝘵-𝘴𝘵𝘺𝘭𝘦 𝘭𝘢𝘺𝘰𝘶𝘵𝘴 if they want reports that behave like products. 👉 𝗙𝗼𝗿 𝗮𝗻𝗮𝗹𝘆𝘀𝘁𝘀: focus each card on a question, not a dataset. When you design with this mindset, your reports feel less like static dashboards and more like interactive products. #PowerBI #UIUX #DataViz

The dashboard looked modern. But users were lost. I watched someone export a report: 7 clicks. 4 wrong turns. 3 minutes wasted. "Where is anything?" ↓ 𝐓𝐇𝐄 𝐂𝐇𝐀𝐎𝐒: Clean design. Total confusion. • Reports? Settings • Analytics? Top menu • Export? Three levels deep • Profile? Different spot each time Each team added their own menu. Nobody asked if it made sense. 𝐓𝐇𝐄 𝐅𝐈𝐗: We didn't redesign. We restructured. Modular Dashboard System: ✔ Grouped related actions (All reports together, not scattered) ✔ Unified layout logic (Same pattern every screen) ✔ Reusable UI patterns (Consistent everywhere) 𝟓 𝐖𝐄𝐄𝐊𝐒 𝐋𝐀𝐓𝐄𝐑: 📉 Support tickets: -35% 📈 NPS: +22 points ⚡ Tasks: 3x faster User feedback: "I can actually find things now." THE TRUTH: Dashboards don't need new paint. They need structure. Pretty doesn't fix lost. Logic does. Quick test: Find your main feature. Count clicks. More than 3? Users struggle daily. Dashboard feel like a maze? Comment "FLOW" 👇 Get my Dashboard Structure Framework. #DashboardDesign #ProductDesign #UXDesign #SaaS #UserExperience

✈️ Most dashboards are designed like airplane cockpits…when what you really need is a Control Tower. Too many BI dashboards try to show everything at once: KPIs, segments, raw data — all mashed together. It overwhelms users and kills decision speed. Instead, think about your dashboards as a Control Tower. The top of the tower offers a clear, panoramic view. You’re scanning for major movements and disruptions. When needed, you can zoom in with instrumentation or speak directly to pilots, but that's not your default. By managing your information hierarchy in layers, you can start simple and progressively reveal complexity. Here’s how it works: 📊 L1: The Tower View – high-level KPIs, trends, and alerts. What’s happening? 🔍 L2: Segment View – explore segments and categories. Where is it happening? 🧾 L3: Transaction View – detailed records and raw data. Why is it happening? Each level is built for a specific cognitive mode. Mixing them forces your brain to multitask and that’s where insight gets lost. 🧠 Rule of thumb: Dashboards should optimize for low cognitive load at entry. Users should never have to reconcile different zoom levels simultaneously. Control Tower dashboards allow users to scan, zoom, and act without overwhelming them. By designing dashboards to reflect human cognitive modes and information hierarchy, you create tools that are not just insightful but usable. #dataviz #dashboards #BI #uxdesign #analytics #productivity

This is a quietly confident dashboard. And I mean that as a compliment. First thing you notice: contrast and hierarchy are doing the heavy lifting. Everything is readable. Your eyes go exactly where they should — KPIs first, then structure, then detail. No visual panic. No “where do I look?” moment. 1. The KPI layer — clean, familiar, useful. Plan vs actual, deltas vs previous month — nothing exotic, and that’s the point. This is what operators and managers actually need. One small nuance (and this is a designer-level nitpick): That plan/actual bar with the target line — the empty space between the bar and the target line made me pause. My brain instinctively expects something there: → a faint translucent bar → a shaded “to target” zone → or a clearer explanation of what that gap represents Right now, the gap asks a question it doesn’t answer. Not a dealbreaker — just a micro-clarity tweak. 2. The sectioned layout (Hubs / Drivers / Vehicles) — this is where the dashboard really shines. Big, clear thematic blocks. Each one tells a story, not just numbers: → capacity vs throughput → experience vs rating → active vs maintenance vehicles And importantly: the “See more” affordance is obvious and honest. You immediately understand: “I can go deeper here.” No hidden drilldowns, no Easter egg UX. One basic but important thing: Left-align the bar labels consistently. That’s table-stakes UX, and fixing it would make the whole section feel even more polished. Overall verdict — this isn’t a flashy dashboard. It’s a trustworthy one. It feels like something a real logistics team could use daily — not just admire in a portfolio. Strong hierarchy, sensible KPIs, clear navigation, and only minor polish left to turn it from “very good” into “great.” Subtle. Functional. Built for decisions — not demos. Exactly how operational dashboards should be built. Great job, Jennifer Eneh!

“Why won't customers use the dashboard?” This question is so common it has become a cliche. Frequently the answer is that the customer-facing reporting or dashboard was not treated like a product. Basic questions were missed in the rush to make the data available: What are my users pain points? How do we make their life better? The essential elements to a good data product aren’t a huge mystery. But they do take an empathetic, customer-focused perspective that is often lacking. Here are some of my key lessons: Lesson 1: Apps, not Dashboards. Multiple, small, focused data products are better than one comprehensive solution that tries to do too much. Many companies launch an “analytics dashboard” or “self-service portal” that is design to answer any and all questions. Of course it doesn’t, and is more confusing than useful. Lesson 2: Form Follows Function: A data product should be delivered and experienced by different audiences in different ways. For example, an executive audience is more interested in summarized insights delivered in static formats (PDF, PPT). Whereas analytical audiences may want an interactive, exploratory solution. Lesson 3: The Goal is Insights. To paraphrase James Carville, “It’s the Insights, Stupid!” The data, visualizations, dashboard…these are all vehicles to find and deliver useful insights. How are you guiding people to find insights, then share and act on those insights? Lesson 4: Lead with Actions. For many years, we designed analytical solutions that assumed users will drill into the data to find the information that was most relevant to them in the moment. It isn’t always the right starting point. If possible, lead with the To Dos or Actions. Lesson 5: The Right Starting Points. Initial settings and personalization are powerful tools in your design toolbox. A remarkable number of data product users (we’ve watched a lot videos of user behavior) will not click on anything to customize the views of the data. Lesson 6: Data Wrapped in Context. A data product needs to do much more than present data. It needs to explain the scope and purpose of the solution, guide the users through the experience, and provide help. Our solutions use images and text to put the data in context. Lesson 7: Secondary Audiences. Data product serve more than the direct users. The information in your product needs to travel to secondary audiences who may impact decisions. How can you ensure insights can get shared more broadly? Lesson 8: Selling is priority #1. This is comfortable territory for many data people. However, as creators of data products, we need to think about how to support the sales team, clarify the value points for customers, and deliver a premium, differentiated product. Lesson 9: Iterate on Feedback. A data product should be its least-good version on initial release. As you start to get (paying) customer feedback, you’ll learn more about what customers really want.

6 take-aways to sharpen your UX thinking for AI-driven Enterprise Dashboards : ① Make confidence a first-class output. Display data quality scores or completeness percentages next to results, so leaders can gauge risk before acting. ② Surface the “why” with the “what.” Pair every metric with an inline explanation of the key filters, joins, or assumptions the model used. Users gain context without hunting for it. ③ Let users poke holes fast. One-click drill-downs (e.g., “see calculation steps”) invite scrutiny and build trust without derailing flow. ④ Cache visual states for iteration. Allow users to bookmark a specific query + visualization. That speeds decision cycles. ⑤ Use smart autocomplete as a thinking aid. Guide users with predictive query suggestions that reflect their data model and past behavior. Done well, this shortens learning curves and nudges users toward better questions. ⑥ Design for the gray area. Flag partial answers—when data gaps prevent a definitive result, rather than forcing a yes/no or number. Transparency beats false precision. Build these mechanics in from day one; retrofitting trust and traceability is expensive and rarely done well.