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This project explores the correlation between sales and customer ratings using Power BI. The goal is to understand whether higher customer ratings drive more sales and how businesses can optimize their strategies using data-driven insights to identify key sales patterns, peak demand periods, and underperforming product lines.
👉 Click to download the Dashboard Download Power BI Dashboard
✅ Correlation Analysis → Measures the impact of customer ratings on sales.
✅ Time-Based Sales Trends → Identifies peak sales hours and high-demand days.
✅ Interactive Power BI Dashboard → Filters & slicers for deep analysis.
✅ Heatmap Visualization → Highlights low-rated, high-selling product lines.
✅ Data-Driven Business Recommendations → Practical strategies for improving sales.
👉 Click to download the Blugate Dataset Download dataset.csv
The dataset includes:
📌 Date & Time of Sales
📌 Total Sales Amount
📌 Customer Ratings (1-10 Scale)
📌 Product Line & Categories
📌 Store Locations (City-Based Analysis)
📈 Correlation Coefficient Calculation → Determines the strength of the relationship between ratings & sales.
📊 Trendline & Time-Based Analysis → Identifies peak sales hours & seasonal demand.
🔥 Heatmap Visualization → Highlights low-rated but high-selling products.
🎯 Slicer-Enabled Filtering → Allows analysis by city, product line, and rating groups.
🔹 Power BI → Data modeling, visualization, and dashboard creation.
🔹 Power Query → Data transformation & cleaning.
🔹 DAX (Data Analysis Expressions) → Custom calculations & correlation metrics.
1️⃣ Download the Dataset: Clone this repository CLONE REPOSITORY and access the dataset/.pbix file.
2️⃣ Open Power BI File: Load the .pbix file in Power BI Desktop.
3️⃣ Explore the Dashboard: Use filters and slicers to analyze trends.
4️⃣ Modify & Customize: Adjust DAX formulas or add new metrics as needed.
📊 Weak Positive Correlation (0.05) → Ratings have a minimal direct impact on sales.
🔥 Peak Sales in Evenings & Weekends → Sales are highest between 5 PM - 8 PM and on Saturdays.
1️⃣ Improve Product Quality & After-Sales Support for low-rated, high-selling items.
2️⃣ Use Promotional Strategies to drive sales instead of relying on ratings.
3️⃣ Optimize Inventory & Staffing based on peak sales hours and high-demand days.
4️⃣ Adjust Pricing & Discounts to align with demand trends.
**I am Oluseyi Adeyemo, Data & Cybersecurity Analysis. If you think you can add/correct/edit and enhance this project you are most welcome 🙏
You can ⭐ Star if useful and 🍴 Fork if useful! Starring and Forking is free for you, but it tells me and other people that it was helpful and you like this project.
Go here if you aren’t here already and click → ⭐ Star and 🍴 Fork button in the top right corner. You will be asked to create a GitHub account if you don't already have one.
This project is open-source under the MIT License.
For any inquiries or suggestions, feel free to reach out via:
📧 Email: [email protected]
📌 LinkedIn: https://www.linkedin.com/in/oluseyi-adeyemo/