Sales analysis in Microsoft Excel

The project's main objective appears to be to analyze the factors influencing bike purchases among customers. The specific goals include:

Income Analysis:

To determine the average income of customers who purchased a bike versus those who did not.

To understand how income varies between genders about bike purchases.

Commute Distance Analysis:

To analyze the relationship between commute distance and the likelihood of purchasing a bike.

To identify which commute distances have higher or lower rates of bike purchases.

Demographic Analysis:

To explore the distribution of bike purchases across different age groups.

To assess the impact of marital status, region, and education on bike purchase behaviour.

Interactive Data Exploration:

To provide an interactive dashboard that allows users to filter and analyze data based on different attributes.

To visualize key metrics and trends in bike sales data in an accessible and interactive format.

This analysis helps understand customer behaviour, identify target demographics, and make data-driven decisions to increase bike sales.

This project gave me significant insights into data analysis and visualization using Excel. I learned how to organize and clean a dataset effectively, ensuring accuracy and consistency. Creating pivot tables and charts allowed me to uncover patterns and trends, such as the correlation between income levels and bike purchases and the impact of commute distance on purchasing decisions. Developing an interactive dashboard enhanced my ability to present data in a user-friendly manner, making it easier to filter and explore key metrics. This project also improved my understanding of how demographic factors like age, gender, and education influence consumer behaviour, providing valuable skills for future data-driven decision-making and market analysis endeavours.

In conclusion, the bike sales analysis project provided valuable insights into the factors influencing bike purchases, revealing that higher income levels and specific commute distances positively impact the likelihood of purchasing a bike, while demographic factors such as age, gender, and education also play significant roles in consumer behaviour. From this project, I learned how to effectively organize and clean data, create insightful pivot tables and charts, and develop an interactive dashboard for user-friendly data exploration. These skills allowed me to uncover key patterns and trends, enhancing my ability to make data-driven decisions and providing a solid foundation for future market analysis endeavours.