How to Visualize Customer Data Effectively

I've been doing analytics for 13+ years. Here's how I would learn data visualization fast if I started again from zero. (The second thing might surprise you) 1) I would focus on data analysis. I've learned that the best data visualizations help the viewer understand what's going on: For myself. For my data story audience. For executives using my dashboards. This is way more important than the technology. Which leads to... 2) I would start with Microsoft Excel. Here's why: - Just about every professional has it. - Excel supports many visualizations. - PivotCharts are fantastic. - Python in Excel. Even in 2025, you can't go wrong learning to analyze data with Microsoft Excel visually. So what to learn? 3) Start with histograms. If you're like me, you first learned histograms in a statistics course. And then promptly forgot about them. It took me years to realize that histograms are wildly useful for analyzing columns of numbers. Oh, and Excel can make histograms. 4) Box and whisker plots. Commonly called box plots, this visualization allows you to analyze a column of numbers by category. For example, how do the amounts of sales orders vary across company geographies? Combining histograms and box plots is powerful. And Excel supports both. 5) Use multiple dimensions. Visualizations are more powerful when you use multiple columns (dimensions) at the same time. Excel PivotCharts can create these visualizations. Also, Python in Excel has plotnine, the best way to make these visualizations. 6) Multidimensional bar charts. Bar charts are the go-to visual for categorical data. But, most professionals don't create them with multiple columns. Excel PivotCharts are great for this. Plotnine with Python in Excel is even better. Be sure to explore related columns and see what pops. 7) Fall in love with line charts. Line charts are the best visualization in business analytics. Because every business process has a time element. Line charts allow you to see: Trends Variability Cycles Rate of change Exceptions This is what executives care about! 8) Use stacked area line charts. Stacked area line charts add the power of seeing relative proportions over time. For example, sales over time by product line or geography. Stacked area line charts are a go-to in my data story PowerPoint decks. They're easily understood and powerful. 9) Get some good resources. Here are two of my favorite books to get you started: To learn visual analysis, "Now You See It" by Stephen Few. To learn how to make your visuals look good, "The Wall Street Journal Guide to Information Graphics" by Dona Wong.

I spent years creating "beautiful" dashboards that executives ignored. Then I discovered 4 strategies that turn complex charts into decision drivers. Here's how to make your data impossible to ignore: It all started with an insight from Storytelling with Data by Cole Nussbaumer Knaflic. Your tools don't know your story. You must bring it to life. 𝗕𝗲𝗳𝗼𝗿𝗲: Hours creating fancy charts with gradients and random colors. 𝗔𝗳𝘁𝗲𝗿: Simple visuals that stakeholders actually use. 4 Core Visualization Principles: 1. Strip Chart Junk ↳ Remove unnecessary gridlines ↳ Delete pointless labels 2. Focus Single Message ↳ One insight per chart ↳ Everything else creates noise 3. Strategic Color Usage ↳ Highlight only critical data ↳ Gray out supporting information 4. Clear Takeaways ↳ State conclusions upfront ↳ Make messages obvious The transformation results in improved attention, understanding, and taking action. Your Implementation Plan: 1. Delete pointless gridlines 2. Remove unnecessary labels 3. Choose one color for key highlights 4. Write titles that state your conclusion Small adjustments create a massive impact. Which visualization principle will you implement first? Share your approach below! 📚 Resource: Storytelling with Data: https://amzn.to/4fHenmA ♻️ Repost to help others create impactful data stories

About a third of the dashboards I've designed have been funnel analysis. The biggest mistake I see people make is trying to show everything at the same time. The visualization pattern you choose depends on what questions your stakeholders are asking and what capabilities they have to influence the results. Here are four design patterns I use for Funnel Analysis: Spark Funnel A sparkline paired with a bar chart to show performance across time for each funnel stage. Use a dropdown to switch between metrics like step retention, conversion rate, and volume. Where I've seen this work: New or established products where cross-functional teams need to monitor trends. BANS + Decomp Each stage is shown in funnel order, the first and last shows volume, while each in between step shows the retention percentage. The decomp below provides comparison between segments. Where I've seen this work: Executive reporting where retention patterns are more important than volume, especially post-launch weeks when numbers are still small. Sankey + Table A flow diagram maps the user journey with line thickness representing volume between steps, paired with a reference table showing segment breakdowns and additional metrics. Where I've seen this work: Funnels with many steps where a map helps stakeholders understand the complete journey. Retention Heatmap Focuses on post-acquisition retention rather than funnel stages. Each cell is a cohort's retention rate at a specific time interval, with color intensity showing churn patterns. Where I've seen this work: Established subscription products where improving retention has more impact than adding volume. The pattern you choose depends on which part of the customer journey your stakeholders can influence. Marketing fills the funnel, Product keeps people engaged, Operations maintains support, and Executives orchestrate resources across all of it. Over time your analysis will evolve and your visualizations need to keep up with the maturity and sophistication of your audience to diagnose and communicate the health of their business. #DataAnalytics

🧠 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

I published a new blog on how to get the most out of Adobe Customer Journey Analytics by designing Data Views that actually work for different teams. One of the biggest challenges I see organizations face is not collecting data but making that data useful for the people who need it. Marketing teams need attribution insights. Product teams want feature adoption metrics. CX teams are tracking journey friction points. And they're all working from the same underlying data. The solution? Strategic Data View design. In this post, I walk through five real-world case studies showing how organizations have configured specialized Data Views for marketing, product, customer experience, executive, and operations teams. Each example demonstrates practical configuration choices, including attribution models and derived fields, calculated metrics, and session settings, that transform raw customer data into focused, actionable insights. The goal isn't just technical optimization. It's about aligning your analytics implementation with how your teams actually work and what they need to understand about customers. If you're implementing or refining your CJA setup, these examples should spark ideas for making your data more accessible and valuable across your organization. I'd love to hear how you're approaching Data View design in your own implementations. Check out the full blog here: https://lnkd.in/gmNhwUfj #AdobeCustomerJourneyAnalytics #AdobeCJA #AdobeExperiencePlatform

Confessions of a Data Scientist: Today, I spent two hours optimizing a visualization that could have been a table 🫠 *BUT* before you @ me, here's why this matters for cognitive load and decision-making speed in real-world applications: Short answer: Your brain can spot geographical and seasonal patterns 60,000X faster in colors than in numbers. (Yes, that's a real cognitive psychology stat, fight me) Long answer: I analyzed Brazilian e-commerce data to prove a point about regional-seasonal buying patterns, and the results were pretty neat. Swipe to see both visualizations → The first shows daily ordering patterns (spot those lunch breaks!), while the second reveals how seasonal buying behavior varies across Brazil's diverse regions. What you're seeing: - Clear daily patterns showing peak ordering times (you can actually see Brazil's lunch breaks in the data!) - Regional variations that flip traditional seasonal expectations (because Brazil spans multiple climate zones) - Some states showing completely opposite seasonal patterns from their neighbors - Hidden patterns that would've been buried in a 168-cell table (24 hours × 7 days) The technical breakdown + code for fellow data nerds: https://lnkd.in/gx4upkux Business impact (AKA here is what this visualization can help with): - Optimal customer service staffing (those 2PM spikes need coverage!) - Region-specific inventory timing - Targeted marketing campaign scheduling - Data-driven fulfillment center capacity planning P.S. Yes, I used a colorblind-friendly palette. And yes, I spent an extra hour making sure the color scale perfectly represented the percentage differences. Some hills are worth dying on. 🎨 #ConfessionsOfADataScientist #DataVisualization #DataScience #Python #Analytics

One of the biggest challenges in data visualization is deciding 𝘸𝘩𝘪𝘤𝘩 chart to use for your data. Here’s a breakdown to guide you through choosing the perfect chart to fit your data’s story: 🟦 𝗖𝗼𝗺𝗽𝗮𝗿𝗶𝘀𝗼𝗻 𝗖𝗵𝗮𝗿𝘁𝘀 If you’re comparing different categories, consider these options: - Embedded Charts – Ideal for comparing across 𝘮𝘢𝘯𝘺 𝘤𝘢𝘵𝘦𝘨𝘰𝘳𝘪𝘦𝘴, giving you a comprehensive view of your data. - Bar Charts – Best for fewer categories where you want a clear, side-by-side comparison. - Spider Charts – Great for showing multivariate data across a few categories; perfect for visualizing strengths and weaknesses in radar-style. 📈 𝗖𝗵𝗮𝗿𝘁𝘀 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗢𝘃𝗲𝗿 𝗧𝗶𝗺𝗲 When tracking changes or trends over time, pick these charts based on your data structure: - Line Charts – Effective for showing trends across 𝘮𝘢𝘯𝘺 𝘤𝘢𝘵𝘦𝘨𝘰𝘳𝘪𝘦𝘴 over time. Line charts give a sense of continuity. - Vertical Bar Charts – Useful for tracking data over fewer categories, especially when visualizing individual data points within a time frame.    🟩 𝗥𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽 𝗖𝗵𝗮𝗿𝘁𝘀 To reveal correlations or relationships between variables: - Scatterplot – Best for displaying the relationship between 𝘵𝘸𝘰 𝘷𝘢𝘳𝘪𝘢𝘣𝘭𝘦𝘴. Perfect for exploring potential patterns and correlations. - Bubble Chart – A go-to choice for three or more variables, giving you an extra dimension for analysis. 🟨 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 𝗖𝗵𝗮𝗿𝘁𝘀 Understanding data distribution is essential for statistical analysis. Use these to visualize distribution effectively: - Histogram – Best for a 𝘴𝘪𝘯𝘨𝘭𝘦 𝘷𝘢𝘳𝘪𝘢𝘣𝘭𝘦 with a few data points, ideal for showing the frequency distribution within a dataset. - Line Histogram – Works well when there are many data points to assess distribution over a range. - Scatterplot – Can also illustrate distribution across two variables, especially for seeing clusters or outliers. 🟪 𝗖𝗼𝗺𝗽𝗼𝘀𝗶𝘁𝗶𝗼𝗻 𝗖𝗵𝗮𝗿𝘁𝘀 Show parts of a whole and breakdowns with these: - Tree Map – Ideal for illustrating hierarchical structures or showing the composition of categories as part of a total. - Waterfall Chart – Perfect for showing how individual elements contribute to a cumulative total, with additions and subtractions clearly represented. - Pie Chart – Suitable when you need to show a single share of the total; use sparingly for clarity. - Stacked Bar Chart & Area Chart – Both work well for visualizing composition over time, whether you’re tracking a few or many periods. 💡 Key Takeaways - Comparing across categories? Go for bar charts, embedded charts, or spider charts. - Tracking trends over time? Line or bar charts help capture time-based patterns. - Revealing relationships? Scatter and bubble charts make variable correlations clear. - Exploring distribution? Histograms or scatter plots can showcase data spread. - Showing composition? Use tree maps, waterfall charts, or pie charts for parts of a whole.

Designing Effective Dashboards📈📊 (links below) A well-crafted dashboard should be intuitive, efficient, and capable of delivering insightful information. Here is a checklist to ensure you incorporate the key features of effective dashboard design: ✔ Focus on key information. Rather than displaying all available data, identify and present what is most relevant to the dashboard's objectives. ✔ Choose the right visualizations. Opt for charts, graphs, and tables that accurately convey your data in an easily digestible format. ✔ Prioritize the conveyance of insights over the mere presentation of raw data. ✔ Highlight critical data points and insights prominently. ✔ Arrange the content logically for easy navigation. Apply the principle of proximity by grouping related data visualizations close to each other. ✔ Ensure each chart communicates a single, clear message. ✔ Enable detailed exploration through drill-down functionality, allowing users to delve into more specific data by clicking or tapping on visual elements. ✔Test and Iterate 📖 Guides: ✔ What you should know before designing a dashboard (by Mimi) https://lnkd.in/diyVTWbj ✔ Data visualisation principles (by Kamila Giedrojc) https://lnkd.in/dtjZWEnH ✔ You might not need a dashboard (by Irina Wagner, PhD)  https://lnkd.in/daJ-wmaE ✔ Practical rules for better dashboard design (by Taras Bakusevych) https://lnkd.in/djS5Z8ye "Storytelling with Data" by Cole Nussbaumer Knaflic: A must-read for anyone interested in presenting data effectively. Tableau Public: Explore thousands of dashboards for inspiration and learning. https://lnkd.in/g4EXXQtP Dribbble's Data Visualization: For design inspiration and the latest trends in dashboard aesthetics. 🔨 Tools: ✔ Hope UI Admin Dashboard Kit for Figma (by Iqonic Design)  https://lnkd.in/dN45ygit ✔ SaaS B2B Dashboard template for Figma https://lnkd.in/dfnqhHmm 

In today’s data-driven world, the ability to quickly understand and act on data is more critical than ever. One of the most powerful tools to achieve this is data visualization, especially when using Excel. By transforming raw data into visual representations, we can not only identify trends and patterns but also communicate insights in a more digestible format. 𝐿𝑒𝑡’𝑠 𝑑𝑖𝑣𝑒 𝑖𝑛𝑡𝑜 ℎ𝑜𝑤 𝑦𝑜𝑢 𝑐𝑎𝑛 𝑙𝑒𝑣𝑒𝑟𝑎𝑔𝑒 𝐸𝑥𝑐𝑒𝑙’𝑠 𝑓𝑒𝑎𝑡𝑢𝑟𝑒𝑠 𝑡𝑜 𝑒𝑛ℎ𝑎𝑛𝑐𝑒 𝑦𝑜𝑢𝑟 𝑑𝑎𝑡𝑎 𝑎𝑛𝑎𝑙𝑦𝑠𝑖𝑠 𝑎𝑛𝑑 𝑑𝑒𝑐𝑖𝑠𝑖𝑜𝑛-𝑚𝑎𝑘𝑖𝑛𝑔 𝑝𝑟𝑜𝑐𝑒𝑠𝑠𝑒𝑠: 📈 Charts and Graphs: Visualizing data with charts and graphs helps highlight important trends and patterns at a glance. Whether it’s a bar chart, line graph, or pie chart, these visuals are perfect for simplifying complex data and making it easier to interpret. ℹ️ Conditional Formatting: Want to quickly spot outliers or key data points? Conditional formatting is your go-to tool. By applying color scales, data bars, or icon sets, you can instantly identify critical information without having to sift through every row of data. 📊 Pivot Charts: Pivot charts allow you to create dynamic visual summaries of your data, giving you the flexibility to explore different perspectives on the fly. With the ability to adjust and manipulate the data, you can uncover insights that might have been overlooked in static tables. 🌟 Sparklines: These mini-charts inside a cell are perfect for showcasing trends within a single row of data. Use sparklines to get a snapshot of trends without taking up too much space on your sheet. 〰️ Dashboard Integration: A dashboard consolidates multiple visualizations into one interactive view, making it easier to track key metrics and make informed decisions. With Excel, you can integrate different charts and graphs into a dashboard that provides a holistic view of your data. Data visualization isn’t just about creating pretty pictures—it’s about making data more accessible, understandable, and actionable. Whether you’re tracking business performance or analyzing trends, these tools can turn raw numbers into strategic insights that drive decisions. How do you currently use data visualization to inform your decision-making process, and which Excel feature do you find most effective? Share your thoughts in the comments below! #DataVisualization #ExcelTips #ExcelDashboards #DataInsights #DataDrivenDecisionMaking

💡Line, bar, and pie chart design: tips & tricks Effective data viz makes it easy to communicate complex information without requiring people to read and interpret a lot of text. People tend to be familiar with common chart types, such as line and bar charts, so using one of these types in your app can make it more likely that people will already know how to read your chart. 🍎 General recommendations ✔ Ensure chart placement and size aligns with the visual hierarchy of the page. When using multiple charts at the same level of importance, they should have consistent sizing. ✔ Design charts to adapt to different screen sizes and resolutions without losing clarity or detail. ✔ Use colorblind-friendly palettes and ensure charts are legible in both dark and light mode. 🍏 Line chart Line charts connect a series of data points using a line, and are commonly used to show data trends over time. ✔ Simplify the lines: Use distinct, contrasting colors for each line. Avoid using more than four to six lines to prevent clutter. ✔ Axis labels and gridlines: Use subtle gridlines and clearly label the axes. ✔ Data points: Highlight data points with small markers (circles, squares) to make it easier to read exact values. ✔ Interactivity: Consider adding tooltips that show exact data values when users hover over specific points on the line. ✔ Avoid using just color to communicate meaning in your data visualization. Incorporate other visual indicators such as shapes, line texture, patterns, or direct labels to help users make sense of the data. How to create line chart in Figma: https://lnkd.in/edPVhsih 🍏 Bar chart Bar charts are used to compare categorical or discrete data. ✔ Baseline number: Don't set the baseline to any number other than zero. Doing so misrepresents the data. ✔ Color: Use a single color for all bars unless comparing different categories; in that case, use distinct colors for each category. ✔ Labels: Place value labels directly on top of or inside the bars to make the data easily accessible without needing to reference the axis. ✔ Stacked vs. grouped bars: Use stacked bars for comparing parts of a whole and grouped bars for comparing multiple categories across the same scale. 🍏 Pie chart Pie chart helps users visualize portions of a whole at a glance. ✔ Segment clarity: Limit the number of slices to 5; more slices make the chart difficult to read. Consider grouping smaller slices into an "Others" category. ✔ Pie order: show data in descending order, starting at the 12 o'clock point and moving clockwise. ✔ Minimalist design: Keep the design clean by avoiding 3D effects, shadows, or excessive borders, which can distort perception. How to create bar chart in Figma: https://lnkd.in/eAaV3KBs #dataviz #datavisualization #ui #uidesign #productdesign #userinterface #uxdesign