This data analysis project aims to provide insights on the factors contributing to the sales performance of bikes in the store using Excel. By analyzing various aspect of the Bike sales dataset, we seek to identify trends, make data-driven recommendations, gain deeper understanding of the store's performance.
Sales Data: The primary dataset used for this analysis is the "bike_sales_data.xlsx" file, containing detailed information about each sale made by the store.
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Microsoft Excel
In the initial data preparation phase, we performed the following tasks:
- Data loading and inspection.
- Handling missing values.
- Data cleaning and formatting.
EDA involved exploring the sales data to answer key questions such as:
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What is the average income per purchase?
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What are the customers commute distance?
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Which age bracket purchase more?
Performed data analysis using pivot tables
The analysis results are summarised as follows:
- Customers with high average income purchase more bikes.
- Short distance commuters purchase more bikes compare to long distance commuters.
- Customers in the middle age bracket purchase more bikes hereby generating more sales.
Based on the analysis done, the following actions are recommended:
- Invest in marketing and promotions during peak sales seasons.
- Implement a marketing strategy to focus more on customers with bachelors degree in the 3 regions to maximize revenue.
I had to replace all the 10+ miles with more than 10 miles in the Commute distance column because they would have affected the accuracy of my conclusions from the analysis. Also had to add the Age bracket column to further group the ages so as to have a clear insight on the dataset.
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