Customizable Customer Support Dashboards

🍱 How To Design Effective Dashboard UX (+ Figma Kits). With practical techniques to drive accurate decisions with the right data. 🤔 Business decisions need reliable insights to support them. ✅ Good dashboards deliver relevant and unbiased insights. ✅ They require clean, well-organized, well-formatted data. ✅ Often packed in a tight grid, with little whitespace (if any). 🚫 Scrolling is inefficient in dashboards: makes comparing hard. ✅ Start with the audience and decisions they need to make. ✅ Study where, when and how the dashboard will be used. ✅ Study what metrics/data would support user’s decisions. ✅ Explore how to aggregate, organize and filter this data.  ✅ More data → more filters/views, less data → single values. 🚫 Simpler ≠ better: match user expertise when choosing charts. ✅ Prioritize metrics: key insights → top left, rest → bottom right. ✅ Then set layout density: open, table, grouped or schematic. ✅ Add customizable presets, layouts, views + guides, videos. ✅ Next, sketch dashboards on paper, get feedback, iterate. When designing dashboards, the most damaging thing we can do is to oversimplify a complex domain, or mislead the audience. Our data must be complete and unbiased, our insights accurate and up-to-date, and our UI must match users’ varying levels of data literacy. Dashboard value is measured by useful actions it prompts. So invest most of the design time scrutinizing metrics needed to drive relevant insights. Bring data owners and developers early in the process. You will need their support to find sources, but also clean, verify, aggregate, organize and filter data. Good questions to ask: 🧭 What decisions do you want to be more informed on? (Purpose) 😤 What’s the hardest thing about these decisions? (Frustrations) 📊 Describe how you are making these decisions? (Sources) 🗃️ What data helps you make these decisions? (Metrics) 🧠 How much detail is needed for each metric? (Data literacy) 🚀 How often will you be using this dashboard? (Value) 🎲 What constraints should we know about? (Risks) And, most importantly, test dashboards repeatedly with actual users. Choose representative tasks and see how successful users are. It won’t be right the first time, but once you get beyond 80% success rate, your users might never leave your dashboard again. ✤ Dashboard Patterns + Figma Kits: Data Dashboards UX: https://lnkd.in/eticxU-N 👍 dYdX: https://lnkd.in/d6yvKS6G 👍 Ethr: https://lnkd.in/eSTzcN7V Orange: https://lnkd.in/ewBJZcgC 👍 Semrush Charts + Tables: https://lnkd.in/dnDRtG32 👍 UI Charts: https://lnkd.in/eJkyB6zS UKO: https://lnkd.in/ehvcSnuV 👍 Wireframes: https://lnkd.in/e-m3VQqs 👍 [continues in comments]

Here is how I made this gorgeous call center dashboard in Excel. This dashboard uses Excel pivot tables, data model, DAX, slicers and conditional formatting. 1) Set up the call data and customer data in 2 tables. 2) Using Excel's themes option, created a color scheme and font choice for the dashboard. 3) Created a "data model" (ie Power Pivot) from the data to link up customer data with call data. 4) Used DAX to calculate key call center KPIs like call count, purchase amount, total call duration, average satisfaction rating and % of happy callers (calls with 5* rating) 5) Made two pivots, one to show overall KPIs and another to show the same KPIs at selected representative level (using a slicer). 6) Set up text boxes and rectangle shapes on the dashboard sheet to show these values (ie KPI tiles) 7) Created a line chart with pivot charts to understand the call trend. Linked this to the rep slicer (made in step 6) 8) Added a calculation to the "calls" table to see the day of week. Used this column to explore the call trend by weekday. Wednesdays are busy! 9) Made two bar graphs to see the calls and amounts by rep. Added a "harvester pivot" to figure out which rep is active and then dynamically highlighted their bar in the graph with simple IF formulas. 10) Explored customer demographics by seeing the call volume by city and gender. Female callers from Cleveland are chatty. 11) Created a histogram to analyze the call satisfaction. Thank god, most callers are happy 😀 12) Added a detailed "table" style report to see the purchase patterns across our 15 key customers by rep. Linked this to the "slicer" via conditional formatting to so I can zoom in on a selected rep. 13) Using XLOOKUP fetched the picture of selected rep and showed few more stats for them (like call and amount ranks, % of calls they take). 14) Spent a couple of hours polishing the overall look and feel of the dashboard. You can grab the blank data file, completed dashboard and a 1+ hour video with all the details of the process from this link: https://lnkd.in/gFD4Ka6D PS: if this helped you a bit, feel free to share it with your network by reposting / sharing ♻

This dashboard helps you see your top customers (or any other dimension) dynamically based on selected metric. It also includes an additional "Others" row that neatly groups all the customers who aren't in your Top N. You can change the "N" (how many top customers you want to see) whenever you like. Here are a few important things considered when building it: ➡️ Handling Ties: In some cases, two customers might have the exact same value for a metric (like "Total Transactions" or "Total Items Ordered"). This could lead to them sharing the same rank. To avoid confusion and make sure each customer gets a unique rank, a secondary tie-breaker is added using their customer ID. This way, the number of rows you see in your Top N will always match the "N" you've selected. ➡️ Visualizing Data with Bars: There are two ways to show those helpful data bars next to each customer's data: ✅ Option 1: Bars compared to the whole picture - these bars show each customer's contribution against all customers, including the "Others" group. If you have many customers (hundreds or thousands), the "Others" bar will appear very long because it represents so many customers. This option works best when you don't have a massive amount of data and each customer's contribution is a noticeable part of the total. ✅ Option 2: Bars focused on top customers - these bars compare each customer's value only to the highest value among the top customers (excluding the "Others" group). This keeps the bars for your Top N customers looking more meaningful and prevents the "Others" group from skewing the visual scale. ➡️ With a dataset of about ~9,000 rows, I've observed no performance issues with this approach. You can find detailed instructions on how to create this dashboard on: - My website: https://lnkd.in/gKSwtFx7 - Medium: https://lnkd.in/g3DTwcvt Link to the interactive dashboard: https://lnkd.in/gEk8PNxy #powerbi #dashboard #report #datavisualization #businessintelligence #dataanalysis #datastorytelling #ui #ux #topN