Sunrise Coffee Chain Analytics with Python, SQL and Power BISunrise Coffee Chain Analytics with Python, SQL and Power BI
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Mosaddek Hosen's avatar
Mosaddek Hosen Sajeeb
• 1d
One business problem. Three analytical routes. One data-driven story. ☕📊
I turned raw business data into actionable decisions—not just another Power BI dashboard.
For the Onyx Data October 2026 Challenge, I worked on the Sunrise Coffee Chain analytics project, analyzing store performance, profitability, customer loyalty, revenue mix, and trading patterns across 60 UK stores (2023–2024).
🔹 My Analytical Approach
1️⃣ Python — Data Preparation Cleaned and prepared the source datasets to establish a reliable foundation for analysis.
2️⃣ SQL — Business Analysis Investigated store-level profitability, cost pressure, customer loyalty, revenue contribution, and trading patterns.
3️⃣ Power BI — Interactive Dashboards Built a 3-page interactive report featuring advanced DAX measures, KPI cards, YoY comparisons, profitability analysis, and business-focused visualizations.
📊 Key Findings
• £89.7M Revenue | £16.9M Net Profit The business recorded an aggregate net profit margin of 18.8% across the analysis period.
• Store Location Matters Transport-Hub stores achieved a 25.1% aggregate net profit margin, compared with 18.6% for High-Street stores.
• An Area Requiring Attention South London Corner recorded a £50.5K net loss over 24 months.
• Product Revenue Contribution Espresso contributed 45.7% of daily revenue, while food contributed 26.1%.
• Trading Patterns Average daily revenue was approximately 24% lower on weekends and 33% lower on UK bank holidays than on regular trading days.
💡 My Biggest Takeaway Revenue alone does not tell the whole story. Understanding profitability, cost pressure, product contribution, and trading patterns helps businesses identify where to improve performance and make better decisions.
🔗 Explore the Complete Project GitHub: https://github.com/SajeebTheAnalyst/sunrise-coffee-chain-analytics
If you were advising the management team, which finding would you investigate first—and why?
#DataAnalytics #SQL #PowerBI #Python #DAX #BusinessIntelligence #DataVisualization #OnyxData
Completed work
Data ScienceData VisualizationData AnalysisMicrosoft Power BIPostgreSQLPython
Simone's avatar
Simone Santia
pro
• 19h
I would investigate South London Corner first. Break the 24-month loss into store-month revenue, direct costs and allocated costs, then compare it with similar stores over matched trading periods. Reconcile at store-month grain before joining product or loyalty tables so a...
Mosaddek Hosen's avatar
Mosaddek Hosen Sajeeb
• 9h
Thank you for the detailed insight, Simone! That's a great point about separating monthly recurring losses from one-off allocation effects. To answer your question, the loss showed consistent dips during specific operational cycles rather than a single massive spike, but...
Simone's avatar
Simone Santia
pro
• 9h
Thanks, Mosaddek — that recurring pattern gives the next investigation a useful focus. I’d compare those cycles with the same calendar periods at similar stores, keeping contribution before allocated costs separate from reported net profit. Transaction count, average basket,...
Mosaddek Hosen's avatar
Mosaddek Hosen Sajeeb
• 3h
That is an exceptionally structured framework, Simone! Comparing contribution margins before allocated costs with net profit is definitely the right diagnostic lens to separate overhead/allocation rules from actual operational inefficiencies. Tracking labor cost per trading hour...
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