Welcome to My Power BI Project!

> Hey There!, I am Shubham Dalvi

「 I am a data engineer with a passion for big data , distributed computing and data visualization 」

About the Project

✨   This project leverages Power BI to deliver dynamic visualizations and actionable business intelligence.

❤️   It focuses on analyzing customer behavior, profit trends, and marketing ROI across different demographics and channels.

☎️   For inquiries, reach me at: [email protected]

Project Screenshots

Skills and Technologies Used

Project Overview

This project focuses on analyzing marketing channels, customer demographics, and age-wise trends to achieve specific goals such as improving customer retention, optimizing resource allocation, and enhancing overall profitability. Key insights help organizations optimize marketing strategies and increase profitability. For example, identifying that the 25-34 age group provides the highest subsequent order profits enabled a retail client to target this demographic with tailored promotions, resulting in a 15% increase in repeat purchases.

Key Dashboards and Features

1. Customer Demographics Analysis

• Insights: Analysis shows that the 25-34 age group contributes the highest subsequent order profit, with a 256.02% increase from first orders. Other key groups include 35-44 and <25.
• Visuals: Pie charts, bar graphs, and trend lines showing registrations and order behaviors.
• Significance: Insights assist in optimizing targeted campaigns for high-value demographics.

2. Marketing Channel Performance

• Metrics:
  • Direct marketing achieves the highest total profit (₹534,626) with a 252.73% profit increase from subsequent orders.
  • Affiliates yield a remarkable 264.65% increase in subsequent orders despite lower initial profit.
• Visuals: Tables comparing ROI across channels and line charts highlighting conversion rates.
• Significance: Provides insights to allocate resources to channels with the best returns, such as Paid Social with a 901.24% ROI.

3. Profit Trends

• Insights:
  • Total profit breakdown reveals Dublin as the most profitable city, contributing ₹1,015,309.66 with a 252.02% profit increase.
  • Significant increase in profits observed in Cork and Galway, with 273.01% and 234.42% respectively.
• Visuals: Heatmaps of profit distribution by region and trend lines of subsequent order profits.
• Significance: Helps prioritize expansion in high-performing regions like Dublin and Cork.

4. Value Segmentation

• Insights:
  • Premium customers contribute the highest profit percentage, but low-segment customers dominate the customer base.
  • Promotions yield the best ROI among high and medium segments.
• Visuals: Donut charts of profit distribution and bar graphs showing value segmentation trends.
• Significance: Guides effective promotions targeting medium and high-value segments to maximize profitability.

5. City-Wise Analysis

• Metrics:
  • Dublin leads with 69.19% of the total profit share, followed by Cork and Galway with smaller contributions.
  • Subsequent order trends are highest in Cork with a 66.22% conversion rate.
• Visuals: Geo maps showing profit distribution and bar graphs of city performance.
• Significance: Highlights location-based performance for regional marketing. For example, identifying that Dublin contributes the highest profit allows for focused investment in that city, such as enhancing customer service or expanding marketing efforts. Similarly, understanding Cork's high subsequent order conversion rate informs strategies like localized promotions to further capitalize on this trend.

Technologies Used

• Power BI: Interactive visualizations and dashboard creation.
• SQL: Querying and data transformation.
• Excel: Data preparation and manipulation.
• Python: Advanced data cleaning and preprocessing.
• DAX: Complex measures and KPIs for in-depth analysis.

Skills Demonstrated

• Data Visualization: Crafting compelling visuals for insights.
• Business Intelligence: Decision-support dashboards.
• Data Transformation: Preparing and transforming data efficiently.
• DAX Expressions: Custom calculations for advanced analytics.
• Performance Optimization: Enhancing dashboard usability and responsiveness.

Usage Instructions

1. Prerequisites:
   • Install Power BI Desktop.
   • Obtain the dataset and import it into Power BI.
2. Steps:
   • Load and transform the data in Power Query.
   • Build dashboards based on the visual templates provided.
   • Publish the report to Power BI Service.
3. Output:
   • Use slicers and filters to customize insights.

Contributing

Contributions are welcome! Feel free to fork the repository and create pull requests with improvements or additional features.

License

This project is licensed under the MIT License. See the LICENSE file for more details.