For this project, I analyzed a bike company's data to answer the question: What can we learn about the customers who are purchasing bikes?
Data was cleaned using tools including: removing duplicates, find and replace, and formatting. I created a column using a conditional formula to summarize the 'Age' column for ease of analysis
Four pivot tables were created to help identify trends in the data, including: relationship between income and bikes purchased, commute distance and bikes purchased, and age and bikes purchased.
Pivot tables were formatted into a dashboard so stakeholders could easily view the information in one place. Slicers were added for an interactive element.
From my analysis, we learn the following information about bike purchasers:
- The majority of bike purchasers were in the 31-55 year age bracket
- The average income of female bike purchasers was $55,000
- The average income of male bike purchasers was $60,000
- People with a 2-5 mile commute were more likely to purchase a bike
- People with a 5+ mile commute were more likely not to purchase a bike