This project highlights analysis solutions showing details of an E commerce transactions and understanding customers behaviour. Click here to get more details concerning the project and the dataset.
Problem 1: Identify outliers or anomalies in the dataset, such as unusually large orders or frequent returns, and what factors might explain them?
This problem was solved using Python. Click the link here to understand the process taken to fish out outliers in which there are several of them by the way. It was noticed that these outliers was mostly seen in United Kingdom, so this means that the data entry procedure is not efficient in the UK branch, as most of the errors came from there. Meanwhile, this might be as a result of much more sales and much more percentage of the dataset coming from the UK. So, one thing to be done as it regards this problem is to set more effective and automated of collecting the sales data in the UK.
Also, the outliers and other data validity issues was dealt with using Python libraries before importing into Power BI for further analysis.
The image above shows some important metrics about the business, ranging from total revenue made, the total number of product sold between 2010 and 2011, but the one to take note of the massive sales growth from 2010 to 2011. The business experienced about 1192% growth in revenue generation between 2010 and 2011.
And to deep dive into why this is so, we tend to understand the top products that sold the most and thus generated more profit. The image above gives a clear insight on that.
Problem 3: What is the month-over-month or year-over-year growth rate in revenue for each product category, and how does it compare to overall revenue growth?
Understanding such massive increase in revenue raised more concerns which led to understanding the months that performed well by analyzing the month over month revenue generated.
Problem 4: Create a geographical heat map showing the distribution of customer orders by country, and identify any regions with particularly high or low sales activity?
Understanding the region with the best sales performance is also important, and the geographical heat map shows that the United Kingdom generated more sales than most regions.
Problem 5: Identify any seasonal trends or patterns in customer purchasing behaviour over the years.
To show seasonal trend, a line chat was used and it shows that our business experience a top sales level mostly in November.