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Superstore Sales Performance Dashboard
I built this Superstore Sales Performance Dashboard using Power BI, DAX, and Excel to analyze sales performance and turn raw data into actionable business insights.
📊 Key KPIs
Total Sales: $2.26M
Total Orders: 4.92K
Total Customers: 793
Average Delivery Days: 3.96
🔎 What the Dashboard Shows
Current Year vs Previous Year performance
Year-over-Year (YoY) analysis
Sales by Region and Category
Sales by State
Monthly sales trends
Segment, Category and Sub-Category analysis
Dynamic Top Category, Top Region and Best Month insights
Interactive Region, Segment and Category filters
🛠️ Tools Used
Excel — Data cleaning and preparation
Power BI — Data modelling, visualization and dashboard design
DAX — KPI measures, YoY calculations and dynamic insights
💡 Key Learning
This project strengthened my ability to work with DAX, Date Tables, relationships, filter context, dynamic measures and data storytelling.
More importantly, I learned that building a strong analytics project is not about getting everything right immediately. It is about building, troubleshooting, learning, and improving until the data tells a clear story.
This project is another step in my journey toward becoming a stronger Data Analyst.
#DataAnalytics #PowerBI #DAX #Excel
#DataVisualization #BusinessIntelligence #DataAnalyst #AnalyticsPortfolio
#LearningInPublic #DataStorytelling
Business AnalysisData VisualizationMicrosoft Power BIMicrosoft PowerPointMicrosoft ExcelData Analysis
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
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Clientwise — Mobile Analytics & Business Dashboard UI/UX
Exciting to share a fresh iteration for Clientwise — a modern mobile analytics platform designed for business owners, founders, and decision-makers who need instant clarity on their metrics on the go. ⚡️
🎯 Key Features & Highlights:
Data Visualization at a Glance: Real-time revenue tracking, order value breakdowns, and growth trends delivered in a high-contrast layout.
Adaptive Design System: Clean component architecture including custom date pickers, crisp micro-icons, and dual-mode theme support (Light & Dark).
User-Centric UX: Streamlined navigation allowing users to filter custom date ranges and monitor client insights seamlessly.
Designed in Figma with a strong focus on typography, modern contrast, and scalable UI components.
💬 How do you approach dual-theme accessibility in data-heavy mobile apps? Would love to hear your thoughts in the comments!
🚀 Available for new product design opportunities & project collaborations.
Great work!
Project Title
Data-Driven Coffee Sales Dashboard & Revenue Optimization
Project Description
Overview
This project features an interactive, comprehensive Sales Performance Dashboard designed to track and optimize global coffee revenue. It transforms raw transactional data into actionable commercial insights, empowering stakeholders to identify key growth drivers across multiple product dimensions.
Core Metrics Delivered
$44.18K Total Sales analyzed to establish clear revenue baselines.
957 Orders Processed to evaluate volume metrics and consumer demand.
$46.16 Average Order Value (AOV) monitored to optimize pricing strategies.
Key Insights & Analytics Features
Sales Over Time Trend Analysis: Multi-variable line chart mapping seasonal demand fluctuations across four distinct coffee beans (Arabica, Excelsa, Liberica, Robusta) from 2019 to 2022.
Geographic Performance Tracking: Bar chart isolating top-performing regional markets, identifying the United States as the primary revenue driver ($35K+).
Product & Packaging Optimization: Granular breakdown of revenue by coffee type alongside dynamic slicers for roast levels (Dark, Light, Medium) and package sizes (0.20kg to 2.50kg) to identify high-margin inventory.
My Role & Technical Impact
Data Modeling: Cleaned and structured transactional data to build a cohesive relational schema.
UI/UX Dashboard Design: Designed a clean, executive-ready interface with synchronized visual anchors and intuitive layout hierarchies for seamless navigation.
Insight Generation: Translated complex timelines into clear trend lines, giving decision-makers immediate clarity on product lifecycles and market demands.
My core stack is less "generate pretty pictures" and more "build the pipeline that does the thinking":
n8n — the glue. Great for orchestrating multi-step agent workflows and connecting APIs without writing a service from scratch. Weakness: gets messy fast on complex branching logic, and debugging a broken node chain eats time.
RAG (usually with a vector DB + LangChain-style retrieval) — lets me ground LLM output in actual client data instead of hallucinated generalities. Strength: dramatically improves accuracy for domain-specific asks. Weakness: chunking/retrieval quality is finicky, and a bad embedding setup silently tanks results without throwing an error.
Claude/GPT for reasoning + code — my default for anything needing judgment: analysis writeups, SQL/Python generation, proposal drafting. Strength: fast iteration. Weakness: still needs a human check on numbers — confident-sounding ≠ correct.
Power BI / Tableau — where the AI output actually becomes something a client trusts. AI can shortcut the analysis, but the dashboard is what gets signed off on.
The honest lesson: the "AI" part is maybe 30% of the workflow. The rest is validation, data plumbing, and making the output legible to a non-technical stakeholder.
Silent failures are what I watch closest. I treat an answer as untrusted unless I can show exactly which source text the model read, because fluent output can hide a bad match.