Developed an interactive sales analytics dashboard in Microsoft Excel to transform raw sales data...

Developed an interactive sales analytics dashboard in Microsoft Excel to transform raw sales data...Developed an interactive sales analytics dashboard in Microsoft Excel to transform raw sales data...

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Narendra Choudhary

• Jul 18

Developed an interactive sales analytics dashboard in Microsoft Excel to transform raw sales data into meaningful business insights. Utilized Pivot Tables, Pivot Charts, advanced Excel formulas, slicers, and conditional formatting to analyze sales performance across products, regions, customers, and time periods. Identified key trends, top-performing categories, revenue growth opportunities, and profit metrics, enabling data-driven decision-making through a clear and user-friendly dashboard.

Skills and deliverables

Microsoft Excel

Data Analysis

Data Cleaning

Data Processing

Dashboard

Slicer

Pivot charts

Completed work

Data Science Data Visualization Microsoft Excel Python SQL Data Analysis

Orbix Studio

max

• Jul 15

Live football data has always existed. A dashboard that makes it feel this immediate hasn't.

Clutch Lens brings the entire match into one screen: live streaming, real-time scoreboard, possession and shot stats, dual team heatmaps, player leaderboard, and a tactic map that shows exactly how the game is being played, not just who's winning it.

France 0 VS Spain 2. Semi-finals. World Cup 2026. Every number that matters, right where it should be.

Dark. Live. Built for the football obsessed.

Designing a sports analytics platform that needs to feel this considered? Let's build it together.

Tools: Figma

#SportsUI #DashboardDesign #FIFA2026 #WorldCup2026 #UIDesign #DarkUI #ContraFreelance

worldcup2026 dashboard Figma

Anjuman Sharear

• Jul 15

Amazing design

Advik Rathee

• Jul 14

Built this one for an e-commerce customer behavior project - 18,000+ customers, segmented using RFM (Recency, Frequency, Monetary) analysis.

The interesting part wasn't the segmentation itself, it was what it revealed: nearly 22% of customers were "at risk" of churning, and most businesses wouldn't know that until those customers had already stopped buying.

This is the kind of insight that's usually sitting in your order data already, it just needs the right lens. Happy to take a look at your numbers if you're curious what's hiding in yours.

Tools used: Power BI, SQL, Python (Pandas, Scikit-learn for clustering)