Display Performance Metrics

Power BI for Sales Performance Analysis Boosting Sales with Power BI: A Real-Life Success Story   Scenario: Challenge: Our sales team struggled with tracking performance metrics across different regions and product lines. The data was scattered across various sources, making it difficult to get a unified view.   Solution: We implemented Power BI to consolidate sales data from CRM, ERP, and other systems into a single, interactive dashboard.   Steps: 1. Data Integration:    Used Power BI's built-in connectors to pull data from multiple sources.   Example Query:     let         SalesData = Sql.Database("ServerName", "DatabaseName", [Query="SELECT * FROM Sales"])     in         SalesData     2. Data Modeling:   Created relationships between tables to allow for comprehensive analysis.   Example: Linked sales data with regional data to analyze performance by region.   3. Interactive Dashboards:   Designed dashboards to track key metrics like total sales, sales growth, and regional performance.   Features: Drill-down capabilities, slicers for filtering by date, product, and region.   Impact: Improved Visibility: Sales managers now have a clear, real-time view of performance metrics. Faster Decisions: Quick access to data enabled faster decision-making and strategy adjustments. Increased Sales: Identified high-performing regions and focused efforts on underperforming areas, resulting in a 15% sales increase.     Include screenshots of the Power BI dashboard, before-and-after performance metrics, and user testimonials. Have you used Power BI to transform your sales performance? Share your story in the comments!   #PowerBI #Sales #DataVisualization #BusinessIntelligence #TechInnovation #DataDriven

📌 33 Rules for Better Dashboard Design Dashboards should simplify decision-making, not make it harder. But too often, they end up cluttered, confusing, or ineffective. 👉 After building dozens of dashboards, I’ve identified 33 rules to follow for a better dashboard design that delivers real business value. 🔹 𝐏𝐥𝐚𝐧𝐧𝐢𝐧𝐠 & 𝐏𝐮𝐫𝐩𝐨𝐬𝐞 1) Define a single, clear purpose for your dashboard. 2)Identify your audience—what decisions do they need to make? 3) Prioritize key metrics over vanity metrics. 4 )Keep the number of KPIs to 5-7 max per view. 5) Avoid mixing multiple use cases in one dashboard (operational vs. executive) 🎨 𝐋𝐚𝐲𝐨𝐮𝐭 & 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 6) Follow the Z-pattern or F-pattern for readability. 7) Place the most critical information in the top-left corner. 8) Keep the layout consistent across multiple dashboards. 9) Use white space effectively—don’t cram everything together. 10) Avoid unnecessary grid lines, borders, or decorative elements. 📊 𝐃𝐚𝐭𝐚 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 11) Choose the right chart for the right data (e.g., line charts for trends, bar charts for comparisons). 12) Limit pie charts—use only if showing parts of a whole (and keep slices to 3-5 max). 13) Always provide context—use comparisons, benchmarks, or trends. 14) Use trend indicators (up/down arrows) for performance metrics. 15) Ensure every visual has a clear takeaway—avoid "chart for chart's sake." 🎨 𝐂𝐨𝐥𝐨𝐫 & 𝐃𝐞𝐬𝐢𝐠𝐧 𝐏𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞𝐬 16) Use a consistent color scheme—don’t go overboard. 17) Reserve bright colors for alerts or key insights. 18) Avoid using too many colors (stick to 3-5 primary colors). 19) Keep background colors neutral to improve readability. 20) Ensure accessibility—use colorblind-friendly palettes. 🔍 𝐅𝐢𝐥𝐭𝐞𝐫𝐬 & 𝐈𝐧𝐭𝐞𝐫𝐚𝐜𝐭𝐢𝐯𝐢𝐭𝐲 21) Provide filters for deeper exploration (date ranges, categories, regions, etc.). 22) Make sure filters are intuitive and easy to use. 23) Keep drill-downs logical—don’t hide critical insights. 24) Show summary insights first, then allow users to dive deeper. 25) Avoid unnecessary interactivity—not every dashboard needs it. 📈 𝐃𝐚𝐭𝐚 𝐈𝐧𝐭𝐞𝐠𝐫𝐢𝐭𝐲 & 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 26) Ensure data is fresh and reliable—stale data leads to poor decisions. 27) Show data granularity clearly (daily, weekly, monthly). 28) Highlight data limitations or potential anomalies. 29) Optimize performance—large datasets should load quickly. 30) Use caching or aggregations to improve speed for real-time dashboards. 📢 𝐔𝐬𝐚𝐛𝐢𝐥𝐢𝐭𝐲 & 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 31) Get user feedback early—don’t design in isolation. 32) Test with real users—ask if they can find key insights in under 5 seconds. 33) Keep iterating—dashboards should evolve as business needs change. 👉 What’s one dashboard mistake you see all the time? Drop it in the comments! #DataVisualization #BusinessIntelligence #DataAnalytics

How well does your product actually work for users? That’s not a rhetorical question, it’s a measurement challenge. No matter the interface, users interact with it to achieve something. Maybe it’s booking a flight, formatting a document, or just heating up dinner. These interactions aren’t random. They’re purposeful. And every purposeful action gives you a chance to measure how well the product supports the user’s goal. This is the heart of performance metrics in UX. Performance metrics give structure to usability research. They show what works, what doesn’t, and how painful the gaps really are. Here are five you should be using: - Task Success This one’s foundational. Can users complete their intended tasks? It sounds simple, but defining success upfront is essential. You can track it in binary form (yes or no), or include gradations like partial success or help-needed. That nuance matters when making design decisions. - Time-on-Task Time is a powerful, ratio-level metric - but only if measured and interpreted correctly. Use consistent methods (screen recording, auto-logging, etc.) and always report medians and ranges. A task that looks fast on average may hide serious usability issues if some users take much longer. - Errors Errors tell you where users stumble, misread, or misunderstand. But not all errors are equal. Classify them by type and severity. This helps identify whether they’re minor annoyances or critical failures. Be intentional about what counts as an error and how it’s tracked. - Efficiency Usability isn’t just about outcomes - it’s also about effort. Combine success with time and steps taken to calculate task efficiency. This reveals friction points that raw success metrics might miss and helps you compare across designs or user segments. - Learnability Some tasks become easier with repetition. If your product is complex or used repeatedly, measure how performance improves over time. Do users get faster, make fewer errors, or retain how to use features after a break? Learnability is often overlooked - but it’s key for onboarding and retention. The value of performance metrics is not just in the data itself, but in how it informs your decisions. These metrics help you prioritize fixes, forecast impact, and communicate usability clearly to stakeholders. But don’t stop at the numbers. Performance data tells you what happened. Pair it with observational and qualitative insights to understand why - and what to do about it. That’s how you move from assumptions to evidence. From usability intuition to usability impact. Adapted from Measuring the User Experience: Collecting, Analyzing, and Presenting UX Metrics by Bill Albert and Tom Tullis (2022).