E-Commerce Sales Analysis

Table of Contents

• Project Overview
• Data Sources
• Recommendations

Project Overview

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This data analysis project aims to provide insights into the sales performance of an e-commerce company over the past year. By analyzing various aspects of the sales data, we seek to identify trends, make data-driven recommendations, and gain a deeper understanding of the company's performance.

Data Sources

Sales Data: The primary dataset used for this analysis is the "sales_data.csv" file, containing detailed information about each sale made by the company.

Tools

• Excel - Data Cleaning
  • Download here
• SQL Server - Data Analysis
• PowerBI - Creating reports

Data Cleaning/Preparation

In the initial data preparation phase, we performed the following tasks:

1. Data loading and inspection.
2. Handling missing values.
3. Data cleaning and formatting.

Exploratory Data Analysis

EDA involved exploring the sales data to answer key questions, such as:

• What is the overall sales trend?
• Which products are top sellers?
• What are the peak sales periods?

Data Analysis

Include some interesting code/features worked with

SELECT * FROM table1
WHERE cond = 2;

Results/Findings

The analysis results are summarized as follows:

1. The company's sales have been steadily increasing over the past year, with a noticeable peak during the holiday season.
2. Product Category A is the best-performing category in terms of sales and revenue.
3. Customer segments with high lifetime value (LTV) should be targeted for marketing efforts.

Recommendations

Based on the analysis, we recommend the following actions:

• Invest in marketing and promotions during peak sales seasons to maximize revenue.
• Focus on expanding and promoting products in Category A.
• Implement a customer segmentation strategy to target high-LTV customers effectively.

Limitations

I had to remove all zero values from budget and revenue columns because they would have affected the accuracy of my conclusions from the analysis. There are still a few outliers even after the omissions but even then we can still see that there is a positive correlation between both budget and number of votes with revenue.

References

1. SQL for Businesses by werty.
2. Stack Overflow

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