📊 MS Excel Project: Customer Purchase Analysis
In this project, I explored customer behavior and purchase trends using Microsoft Excel. The goal was to uncover actionable insights through techniques like data cleaning, applying formulas (such as nested IF statements), creating pivot tables, and designing a detailed interactive dashboard. This case study demonstrates how Excel can transform raw data into meaningful stories and strategic decisions. You can view the complete interactive dashboard and cleaned data down of this page.
📊 Dashboard Design
The final and most exciting outcome of this project is the interactive Excel Dashboard, which brings the analysis to life with clean visuals and dynamic filtering tools. From the very top section titled "MS Excel Project: Customer Purchase Analysis", you can explore down below that how this dashboard was built - from understanding raw data to cleaning, and creating pivot tables.
The dashboard includes interactive slicers for filtering by Marital Status, Region, Education, Gender, Age and Commute Distance and a variety of charts and summaries that provide quick insights at a glance. It's a practical representation of how Excel can be used for real-world business decision-making.
Dashboard view:
📥 Raw Dataset Overview
The raw dataset consists of multiple attributes including ID, Gender, Marital Status and so on. Each column represents essential demographic or behavioral data which helps in understanding customer patterns.
Using problem-solving techniques and a data-driven approach, I began to analyze and convert this raw data into a more readable and structured format. This not only improved clarity but also made it more suitable for building pivot tables and an interactive dashboard later in the process.
You can find the raw data preview in the screenshot below:
🧹Data Cleaning & 🧾Cleaned Data Preview
In this step, I performed basic yet essential cleaning actions to prepare the dataset for analysis. I started by removing duplicate records, then replaced short forms and inconsistent values with meaningful names for better clarity. Additionally, I created new rows or categories wherever necessary to improve grouping and readability.
Example of used Nested if statements
IF(L2>=31,"Middle Age",
IF(L2<31,"Adolescent","Invalid")))
Since this project mainly focuses on creating Pivot Tables and designing an interactive Dashboard, I kept the data cleaning minimal and only to the point necessary for accurate analysis.
The cleaned dataset is shown in the screenshot below:
📈 Pivot Table Analysis & Charts
Once the data was cleaned, I utilized Pivot Tables to uncover hidden patterns and relationships across various customer attributes. Using Excel's pivot functionality, I analyzed bike purchase trends across age groups, gender, income levels, marital status, and commute distances. This helped in identifying what kind of individuals are more likely to purchase bikes, enabling better-targeted business strategies.
Below are some visual examples of the insights generated through pivot tables and charts:
💡 Key Insights
Please note that this is a demo project built for academic and portfolio purposes and does not reflect real-world customer data. However, it covers all the essential steps involved in building a high-quality dashboard, from understanding the data to cleaning, analyzing, and visualizing it effectively.
Through this project, we explored how Excel can be used to uncover meaningful patterns in customer behavior and translate those findings into actionable insights.
✅ Conclusion
This project highlights my ability to turn raw data into insights using problem-solving, critical thinking, and data visualization skills. From data structuring to building interactive dashboards, each step showcases Excel's power in practical business analysis. While the dataset is sample-based, the approach and logic used reflect real-world scenarios.
Thank you for taking the time to go through my work!