U.S. Airsoft Market Analysis
📊 From almost no structured data to a complete Power BI market analysis I was approached by people who are considering launching an airsoft business and asked to analyze the U.S. airsoft market, with a particular focus on Washington State. And the biggest challenge became obvious almost immediately: 🔎 There was very little structured market data available. For the first time, I had to use Python not only for analysis, but also for data collection. I gathered information from Google, Facebook, company websites, and other online sources, using APIs where available. 🧩 Collecting the data was only the beginning. This turned out to be one of the most challenging parts of the project. I am not a web-scraping specialist, and the raw data was far from analysis-ready. Business names were inconsistent, categories overlapped, information was missing or outdated, and the same companies could appear differently across multiple sources. Honestly, this part exhausted me. 😅 I started building the Power BI report several times, only to discover another issue in the underlying data and go back to cleaning and validating it again. Eventually, I was able to build a dataset that was consistent enough for analysis. ⚙️ The overall pipeline looked like this: Data Collection → Data Cleaning → Business Verification → Data Enrichment → Pricing & Services Analysis → Customer Review Analysis → Power BI Data Model → Interactive Dashboard → Business Scenario 🇺🇸 U.S. Market The final dataset included 851 U.S. businesses, from which I identified 194 confirmed airsoft operators. I then focused on Washington State, where I verified 10 active game operators and analyzed their locations, pricing, services, ratings, and customer feedback. 📍 Washington Market The analysis showed: 10 verified game operators • $22.50 median published open-play price • 4.8 average Google rating • 1,096 Google reviews across the verified operators 💬 Customer Voice I also analyzed customer review text to understand not just how businesses were rated, but what customers were actually talking about. The analysis highlighted recurring issues around operations & communication, price & value, rental equipment, and consistency in safety & rule enforcement. 📈 From analysis to a business scenario Finally, I brought everything together in Power BI and built an interactive business scenario where assumptions such as daily attendance, operating days, admission price, rental usage, and private bookings can be changed to explore their impact on estimated monthly revenue. 💡 What this project became For me, this project became much more than a Power BI dashboard. It was an end-to-end data project: finding the data → building the dataset → validating it → analyzing it → turning it into something that could support a real business discussion. And, perhaps most importantly, it taught me how much work can happen before the first chart ever appears on a dashboard. 😅 #PowerBI #DataAnalytics #Python #DataVisualization #BusinessIntelligence #MarketResearch #DataAnalysis #PortfolioProject
European Summer Getaways 2026
This report explores European Summer Getaways 2026, helping commercial and portfolio teams identify the strongest destination opportunities, understand the drivers of destination attractiveness, and evaluate how holiday packages can be positioned for different traveller needs. Through a three-page analytical journey, the report moves from opportunity prioritisation to destination analysis and concludes with traveller-focused package positioning recommendations. Portfolio: Where Should We Focus? The first page identifies the strongest portfolio opportunities by balancing destination appeal with package availability and portfolio depth. The analysis moves from destination types to countries and cities, helping identify where commercial attention and promotion may deliver the greatest opportunity. Outcome: Prioritise the destinations and markets that deserve greater commercial focus. Drivers: What Drives Destination Attractiveness? The second page explores why some destinations perform better than others. Weather conditions, temperature and tourism pressure provide context around destination attractiveness, while the performance matrix highlights the trade-off between strong appeal and visitor pressure. Outcome: Understand the factors behind destination performance instead of relying on ranking alone. Travellers: Which Holiday Is Right for Which Traveller? The final page shifts from destination performance to package value and positioning. The analysis compares traveller spending, accommodation pricing, travel convenience and city-level value to understand how attractive destinations can translate into commercially relevant holiday packages. Outcome: Support package positioning around value, convenience and different traveller needs. Final Takeaway One of the biggest challenges with such a rich dataset was deciding what not to show. Rather than trying to analyse every available field, I focused each page on a clear business question and built the report around a simple journey: Opportunity → Understanding → Action A rich dataset does not need a crowded report. Every visual should earn its place by helping answer the business question. Data Source: FP20 Analytics ZoomCharts Challenge 40 | Summer 2026 Holiday DatasetThe Finance Dashboard
The Finance Dashboard | Power BI Project 📊 Excited to share my latest Finance Dashboard built using Microsoft Power BI! This project focuses on Financial Data Analytics, Business Intelligence, and Interactive Data Visualization. Key Features: - Total Amount, Tax, Fees & Final Amount KPIs - Country-wise Financial Analysis - Company-wise Amount Analysis - Transaction Status Monitoring - Tax Analysis by Country & Company - Year & Account Type Filters - Interactive Dashboard Design This dashboard helps transform raw financial data into meaningful insights for better business reporting and data-driven decision-making. Tools: Microsoft Power BI | Data Analytics | Business Intelligence | Financial Reporting I am passionate about Data Analytics, Power BI, and turning data into actionable business insights. Your feedback is welcome! #PowerBI #PowerBIDashboard #DataAnalytics #BusinessIntelligence #FinancialAnalytics #DataVisualization #FinanceDashboard #PowerBIDeveloper #DataAnalyst #MicrosoftPowerBI
The Finance Dashboard | Power BI Financial Analytics & Business Intelligence Report
The Finance Dashboard is a comprehensive Power BI financial analytics solution built using a cleaned and transformed finance dataset containing 50,000+ records. Key Features: ✅ Data Cleaning & Transformation using Power Query ✅ Financial KPI Monitoring ✅ Revenue, Tax, Fees & Final Amount Analysis ✅ Country-wise Financial Performance ✅ Company-wise Revenue Tracking ✅ Status Analysis (Completed, Pending, Failed) ✅ Interactive Filters & Slicers ✅ Executive-Level Business Intelligence Reporting Tools Used: • Microsoft Power BI • Power Query • DAX Measures • Data Modeling • Data Visualization • Financial Analytics This dashboard enables decision-makers to monitor financial performance, identify trends, compare countries and companies, and make data-driven business decisions through interactive visualizations. Power BI Dashboard, Finance Dashboard, Financial Analytics, Business Intelligence Dashboard, Power BI Project, Data Visualization, Financial Reporting, DAX, Power Query, Executive Dashboard, KPI Dashboard, Finance Data Analysis, Revenue Analysis, Tax Analysis, Corporate Reporting, Interactive Dashboard, Business Analytics, Data Cleaning, Data Transformation, Power BI Portfolio Project eyJrIjoiYTQ0MzIzOWQtNzM5Zi00NjBiLWFhNjItMWM1NGI1ODEwODljIiwidCI6IjI0MWNlN2VlLTVjYmUtNDczNi1hYWM0LWZkOWZmM2NjMWRkMSIsImMiOjl9
Global Electronics Retailers Analysis Dashboard
🌍 Global Electronics Retail Performance Intelligence System | Power BI Developed a strategic retail analytics dashboard to analyze multi-region electronics sales, profitability, customer contribution, and store performance, transforming fragmented transactional data into executive-level performance intelligence. 📊 Key Business Insights & Quantified Impact 💰 Revenue & Profit Concentration Risk Top 20% of products contributed ~65–70% of total revenue, revealing heavy SKU dependency. Bottom 30% of SKUs generated less than 8–10% revenue while occupying inventory capacity. Identified margin variation of up to 18–25% across product categories, highlighting pricing optimization opportunities. 👉 Business Implication: Even a 5% margin improvement in top-performing categories could significantly increase overall profitability. 🏬 Store Performance Gap Analysis Best-performing region generated ~2.3x higher revenue than the lowest-performing region. Identified 15–20% performance gap between top and bottom quartile stores. Certain stores showed high sales but below-average profit margin (margin leakage indicator). 👉 Business Implication: Standardizing best-performing store practices could improve low-performing outlets by an estimated 10–15%. 📈 Growth & Trend Analysis Year-over-Year growth varied significantly across regions (ranging from -5% decline to +22% growth). Seasonal peaks showed up to 35% higher sales during high-demand months. Profit growth lagged revenue growth in some regions, signaling cost structure inefficiencies. 👉 Business Implication: Improved demand planning and cost control during peak seasons could protect margins while scaling revenue. 👥 Customer Intelligence Top 25% of customers contributed nearly 60% of total revenue. Revenue concentration risk identified in specific customer segments. Repeat purchasing patterns indicated strong upselling potential in high-value segments. 👉 Business Implication: Targeted retention strategies for top customers could prevent significant revenue volatility. 🧠 Analytical Depth Demonstrated Built optimized star-schema data model Developed 25+ advanced DAX measures including: YoY growth % Dynamic ranking Contribution % Profit margin variance Implemented drill-through and executive navigation structure Reduced report loading time via model optimization 🎯 Why This Project Signals High-Level Value This is not a visualization project. It demonstrates ability to: Quantify revenue risk and opportunity Identify operational inefficiencies Detect margin leakage Translate raw retail data into strategic financial decisions If implemented in a real retail environment, insights from this dashboard could potentially drive: 5–10% margin improvement 10–15% uplift in underperforming stores Better inventory allocation decisions Reduced revenue concentration risk eyJrIjoiMzNjNDVjNTYtYTQ3Yy00YzJjLTkyNjEtNzcwNDUyOWNhNWQzIiwidCI6IjE5OTFlNDljLWFlYTItNDhhNS1iMWVmLTIxMjRkYjVhYWQwMCIsImMiOjN9🎅 Santa's Christmas Eve Journey in Power BI with Icon Map
🎅Here's a bit of fun for the holiday season ! 🎅 We loaded the JSON version of the data from NORAD's Santa Tracker site into Power BI and using the Icon Map Slicer visual, mapped Santa's sleigh as it travels around the world. We used the play axis visual to the animate the journey. The new card visual shows the number of presents delivered and the current UTC time. The new Image visual shows an image from the location at that time. To go along with it, we used AI to generate a song which is played by a bespoke custom visual we built just for this purpose. We've created a blog post showing how it was put together and included the .pbix file with this post. Here's the video of the report Don't forget to try out the Icon Map Slicer visual that we used for this report. Happy Holidays everyone! eyJrIjoiYWYxODU5NWYtMDAzNC00ZjNiLTljYTctNDY1OTAxNTM0NzFkIiwidCI6IjgzYjFlODU4LWJiNGEtNDFkZC1hYjc2LTg1ZTkxODZjZjA3NCJ9Power BI Advent Calendar with movies
✨ Little side-project joy: I wanted to see how far I could push HTML inside Power BI… so I built a Christmas Advent Calendar dashboard 🎅📊 I first mocked it up in Lovable, then used ChatGPT to translate the React UI into DAX-generated HTML. The result? A grid of 25 days, each opening to reveal Christmas classics ... movies most of us end up watching this time of year anyway 😄 My original plan was to use clickable pop-ups like in the web prototype, but that would’ve required ~50 bookmarks. Instead, I got creative and used one hidden tooltip page + a transparent day-slicer to trigger each movie card. And I used my Timer custom visual too 😅 Cleaner, faster, still festive. It was fun to mix design, code, and Power BI into something completely unnecessary, but very Christmassy. 🎄🤭 Because data can be magical too... eyJrIjoiMWYyYWM3NWYtN2IzYS00YWYxLTg4MzUtYTE1NGQ1MjMzODg0IiwidCI6IjI2OGUxZmZhLTMwMmItNDRmNS1iODlkLTYzMjcyMjhiMjhhMyIsImMiOjF9
Workout Wednesday 2025 Week 50 | Power BI: First Snowfall Scatterplot
Challenge The first snowfall in Denver was unseasonably late this year. So I got curious about the data and decided to make a visualization. While this one does communicate data, it's taking a bit of artistic license to achieve that winter feeling. This is your challenge as well: create a first snowfall scatterplot with some wintery visual effects. You can decide if you are making a more traditional chart or data art. But please keep accessibility and readability in mind! I made the example using the free HTML Content custom visual. Requirements Retrieve snowfall data from a reputable source. You can use the Denver data as shown in the example, or you can find data for a location of your choice (just try to make sure it goes back at least 40 years so your scatterplot isn’t too sparse). Create a measure that shows the amount of snowfall in the first month of each season. Create a scatterplot using your Power BI visual of choice (custom or core). Show the season/year on one axis and the month of first snowfall on the other axis. Plot points for the month in each season where the first snow of the season occurred. Make the size of the points correspond to the amount of snowfall in that month. Create a tooltip that appears when you hover over each data point that displays the month, season, and snow amount for that point. Add design elements related to winter or snow. In the example, the data points have the shape of a snowflake, there is a snowy background image, and the colors are related to snow and winter. And the snowflake data points are animated, falling into place when the report first opens and glowing while the visual remains in view. Dataset Data for this challenge can be found at https://www.weather.gov/bou/seasonalsnowfall. This data is supplied by the US National Weather Service. If you would prefer to use data from a different source, that is fine – just make sure you can complete the requirements for the challenge. Share After you finish your workout, share on Bluesky or LinkedIn using the hashtags #WOW2025 and #PowerBI, and tag @MMarie, @shan_gsd, @KerryKolosko (on BlueSky). Cross-posted at https://workout-wednesday.com/pbi-2025-w50. eyJrIjoiYjYwYjQ3NjItOWZjZi00OTQwLWIyZTMtMzlmYjVkNDdmYWEyIiwidCI6Ijg3N2MzNGI5LWRjNDMtNGMwYS04N2MzLTgwZDcwN2FiYzY3MSIsImMiOjN9Winter Wonderland in Power BI (No bookmarks)
Having fun with Power BI after work! Last week, a coworker said that it would be nice to have a bit of snow in a Power BI report. So, of course, I had to try it. And it worked. I even added pine trees and a switch between night and day. All inside Power BI with native visuals. - The GIFs are loaded through the image visual (we have some new functionality there!). I put the URLs in a table and connected it to my toggle button. I did the same for my logo. - The toggle button is a button slicer. No bookmarks. - The trees are a line chart with the line hidden, markers turned on and error bars added. - The color change comes from a table with HEX codes that is linked to the same toggle button. I loved working on this! This is exactly why Power BI is so much fun for me. Creating something like this in two hours after work still feels like magic. For almost everything I used here, I've created videos and tutorials before. This one is simply the combination of all those tricks. You can find my YouTube channel here: https://www.youtube.com/@porcubi Video about dynamic color change: https://www.youtube.com/watch?v=sZtHutgNgO4&t How to create a lollypop visual (replace the circle with a triangle): Lollypop Visual is Power BI - Line Chart with Error Bars eyJrIjoiZmI5NTM0ZDYtY2FkZi00MTZmLTlmODktYjA4ODI1ZGJlY2NkIiwidCI6ImRiNGRmYTQ0LTJhY2YtNGJlZC05ZWQwLTllMjkwYzJlYWRkYyIsImMiOjl9
Santa's Little Helper, a Christmas Snake Game
It’s December, and I wanted to create something fun in Power BI while also learning something new. So I decided to build a playable Christmas Snake game inside Power BI using a custom visual. This little project is a playful way to explore what’s possible in Power BI and to spark a bit of holiday creativity along the way In the game, you’re helping Santa! Ten gifts are missing just before Christmas, and your mission is to help Santa collect all 10 different gifts as quickly as possible. I got the inspiration from Phil Seamark, using vibe coding to create a custom visual in Power BI: https://dax.tips/2025/09/29/create-custom-visuals-in-power-bi-with-github-copilot/ I created the custom visual in VS Code using Claude Sonnet 4.5 in Agent mode. I spent some time getting the technical foundation in place, but once that was done, things really took off. In about 10 minutes, I had the first very basic, playable version up and running. After that, I spent another 5 hours refining the game and getting all the details right. Have fun playing the game! or Vibe Code your own game 🙂 eyJrIjoiZTFiYzMxZTEtY2Y3Ny00ODgwLWJhYmYtNDE3ZThlNjcwOGU5IiwidCI6IjcxOTFkMGE5LWJkNjYtNDU0OC04MDk4LWQ2NDZmMGRiYTUzMiIsImMiOjl9
