Bike Purchase Analysis

Project Overview

The goal was to understand customer demographics, socioeconomic status, lifestyle, and purchasing behavior to develop targeted marketing strategies and improve customer engagement.

ETL Process

• Utilized Power Query for extracting, transforming, and loading data into Excel sheets.
• Overcame challenges related to data quality and integration by removing duplicate data, improving data inconsistency and partitioning some number values into groups for clear data visualization.

Data Analysis

• Conducted thorough analysis using Excel PivotTables.
• Utilized descriptive and inferential statistics to uncover patterns and trends within the data.
• Performed detailed analysis to understand customer profiles, preferences and purchasing behavior.

Data Visualization

• Created interactive dashboards in MS Excel to visualize key metrics and trends.
• Developed visualizations such as bar charts, column, pie and donut charts to effectively communicate insights.

Key Performance Indicators (KPIs)

• Identified crucial KPIs such as bike purchase rate, gender distribution, and average income.
• Tracked these KPIs to measure performance and guide decision-making.

Key Insights and Findings

• Discovered that 48% of customers purchased bikes, with higher purchase rates among homeowners.
• Found that the highest average income was in the 41-55 age group, guiding targeted marketing efforts.

Impact and Results

• Improved marketing strategies led to a slightly increase in conversion rates.
• Enhanced customer engagement and satisfaction through data-driven insights.

Conclusion

This project underscores the importance of data-driven decision-making and demonstrates my expertise in data analysis and business intelligence. I’m always eager to connect with like-minded professionals and explore new opportunities for collaboration.

💡 Interested in learning more or discussing potential collaborations? Feel free to reach out or comment below!