https://github.com/mandeep04sharma/kpmg-data-analysis-using-excel

End-to-end customer and sales data analysis project using Excel. Includes data cleaning, segmentation, transaction trends, and customer lifetime value.
https://github.com/mandeep04sharma/kpmg-data-analysis-using-excel

charts data-modeling formulas pivot-tables power-query

Last synced: 5 months ago
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End-to-end customer and sales data analysis project using Excel. Includes data cleaning, segmentation, transaction trends, and customer lifetime value.

• Host: GitHub
• URL: https://github.com/mandeep04sharma/kpmg-data-analysis-using-excel
• Owner: Mandeep04Sharma
• Created: 2025-04-14T09:15:01.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2025-04-14T09:32:08.000Z (about 1 year ago)
• Last Synced: 2025-06-01T08:08:29.842Z (about 1 year ago)
• Topics: charts, data-modeling, formulas, pivot-tables, power-query
• Homepage:
• Size: 5.41 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# 📊 KPMG Excel Data Analysis Project

This Excel-based project analyzes customer and transaction data from a fictional KPMG dataset. The goal was to derive insights on customer behavior, lifetime value, sales trends, and segmentation using only Microsoft Excel.

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# 🎥 Project Video Walkthrough

📽️ [Click here to watch the full video presentation](https://drive.google.com/drive/folders/196lAkVsefX8Jv-HEPpgAneSxgjbziKpV)

## 🛠 Tools & Techniques

- Microsoft Excel (Pivot Tables, Charts, Conditional Formatting)

- Data Cleaning

- Customer Segmentation

- Transaction Trend Analysis

- Customer Lifetime Value (CLV) Calculation

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## 📈 Key Tasks Completed

1. **Data Cleaning**

   - Fixed gender and default anomalies

   - Removed duplicates and standardized formats

2. **Customer Segmentation**

   - Segmented customers by wealth, gender, and job industry

   - Found insights on tenure and spending behavior

3. **Sales & Product Analysis**

   - Created monthly sales trend charts

   - Identified top brands and product lines

4. **Customer Lifetime Value (CLV)**

   - Calculated CLV using Excel formulas:

     ```

     CLV = (Average Purchase Value × Purchase Frequency) × Customer Lifespan

     ```

5. **New Customer Insights**

   - Analyzed new customer demographics and estimated revenue

6. **Executive Summary**

   - Final dashboard and strategic recommendations provided

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## 📁 File in this Repository

- **KPMG-Data-Analysis-using-Excel.xlsx**  

  Contains all sheets: data cleaning, segmentation, analysis, charts, and final insights.

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## 🧠 Sample Insights

- NSW and QLD have the highest concentration of high-value customers.

- Professional segments in the “Mass Customer” wealth segment contribute the most revenue.

- Seasonal sales spikes observed in Q3, with specific brands dominating.

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## 📌 Future Improvements

- Convert this Excel project into a Power BI Dashboard.

- Use Python/Pandas to automate CLV calculations.

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## 📧 Contact

Feel free to connect:

- LinkedIn: https://www.linkedin.com/in/mandeep-sharma04/

- Email: Mandeep04sharma@gmale.com