This project is a Bike Sales Analysis Dashboard built using Excel. It leverages pivot tables, charts, and slicers to visualize and analyze data related to bike purchases based on various factors, including commute distance, age brackets, gender, and income levels.
The dashboard provides insights into customer behavior and preferences in the bike sales industry. Key highlights include:
- Customer Commute Analysis: Understand how commute distance influences bike purchases.
- Demographics Breakdown: Explore age groups, gender, and other factors affecting buying decisions.
- Income Analysis: Correlate average income levels with bike purchase trends.
- Use slicers for gender, marital status, education, and region to customize the view of the data interactively.
- Line Charts: For tracking commute trends.
- Pie Charts: For visualizing age group distribution.
- Bar Charts: To compare income levels of customers who purchased bikes vs. those who did not.
- Target Audience: Middle-aged customers are the primary buyers, making up 86% of the purchases.
- Economic Factors: Income plays a significant role in purchase decisions, with higher earners more likely to buy.
- Commute Impact: Commute distances closer to 0–1 miles show higher purchase trends.
bike_buyers (RAW): Contains the raw dataset used for the analysis.working_sheet: Intermediate data preparation and calculations.pivot_table: A consolidated summary using pivot tables.dashboard: The final interactive dashboard showcasing insights.
- Open the Excel file.
- Navigate to the Dashboard tab.
- Use the slicers to filter the data and update the visuals dynamically.
- Switch to the pivot_table tab to view the underlying summarized data.
- Marketing Strategies: Focus on middle-aged customers for targeted promotions.
- Product Development: Tailor products to commuters traveling short distances (0–1 miles).
- Regional Sales: Use the region slicer to explore specific geographic trends and opportunities.