The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Back to feedPost
Retail Sales Analysis by Season and Category
I analyzed a retail sales dataset to explore how total sales vary across seasons (Fall, Winter, Spring, Summer) within three product categories: Beauty, Clothing, and Electronics. Using Python (pandas and matplotlib), I aggregated total sales amounts and visualized the results in a grouped bar chart to compare seasonal performance side by side.
Key insight: Winter consistently drove the highest sales across all three categories, while Spring showed the weakest performance in Clothing and Electronics — suggesting a potential opportunity for targeted spring promotions.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Related posts
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
Amazing design
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)
Built a 4-part Power BI dashboard suite analyzing $80M+ in revenue for supply chain & e-commerce data — designed for 4 different stakeholders.
📊 Executive KPI Dashboard (VP of Supply Chain) — Revenue, profit & a 71% revenue drop identified in 2018
🚚 Delivery Performance (Procurement Team) — 54.8% late delivery rate flagged, broken down by shipping class
📦 Product & Warehouse Analysis (Warehouse Ops) — 33K SKUs across 9 warehouses, revenue vs. margin comparison
💳 Customer & Payment Insights (Finance Team) — Sentiment, review scores & payment method breakdown
Tools: Tableau, SQL, Excel, Python
Trending
Claude
Claude has entered the design space. How are you using Claude Design?
Contra University
Learn from expert creatives how to earn more using next-gen AI tools.
creativeaiflow
Creative AI workflows are evolving. What tools do you use, and what are their strengths and weaknesses?
freelancerlife
Freelancer life is wins, pivots, and everything in between. What’s yours right now?