busayo-omoniyi/retail-sales-performance-analysis — explained in plain English
Analysis updated 2026-05-18
Study a worked example of SQL plus Power BI retail sales analysis.
Reuse the dashboard structure as a template for a similar business case study.
Learn how to turn raw transaction data into business recommendations.
| busayo-omoniyi/retail-sales-performance-analysis | 00kaku/gallery-slider-block | 04amanrajj/netwatch | |
|---|---|---|---|
| Stars | 0 | — | 0 |
| Language | — | JavaScript | Rust |
| Last pushed | — | 2021-05-19 | — |
| Maintenance | — | Dormant | — |
| Setup difficulty | easy | easy | moderate |
| Complexity | 1/5 | 2/5 | 3/5 |
| Audience | data | general | ops devops |
Figures from each repo's GitHub metadata at analysis time.
This is a case study with a PDF report and SQL files, not runnable software.
This repository is a data analytics case study, not a piece of software you install or run. It walks through analyzing a retail sales dataset called Global Superstore, which contains transaction records covering sales, profit, discounts, customers, products, regions, and order dates. The goal was to answer real business questions a company's management might ask: how sales are trending over time, which regions and customer groups bring in the most revenue, which product categories are most profitable, how discounts affect profit, who the most valuable customers are, and which products need attention. The work was done using SQL for the data analysis itself and Microsoft Power BI for building interactive dashboards, with Microsoft Excel also listed as a tool. The process followed a clear path: starting from the raw dataset, cleaning the data, running SQL queries to analyze it, building dashboards in Power BI, then turning the findings into written insights and recommendations. Four dashboard pages are described: an executive overview with key metrics and trends, a profitability dashboard that breaks down profit by category and region and flags products losing money, a customer dashboard highlighting top spenders and comparing customer segments, and a time analysis dashboard showing monthly and quarterly sales patterns. The README lists several findings, including that sales grew steadily from 2011 to 2014, the Central region generated the most revenue, the Consumer segment drove the most sales, Technology was the most profitable category, heavier discounts tended to reduce profitability, a small number of customers accounted for a large share of revenue, and some high-value products consistently lost money. Based on these findings, the author suggests preparing for peak sales periods in advance, adjusting discount policies to protect margins, reviewing underperforming markets and products, and focusing on retaining high-value customers. The repository itself contains a written business case study document and the SQL query files used in the analysis, useful as a portfolio example for anyone learning data analytics.
A SQL and Power BI case study analyzing a retail sales dataset for profitability, regional performance, and customer trends.
Setup difficulty is rated easy, with roughly 30min to a first successful run.
Mainly data.
This repo across BitVibe Labs
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