Sales Analysis

Introduction

This project analyzes sales data to answer interesting business questions and provide insights to the management team. The data is from a company's sales database and includes information on customers, products, orders, and transactions.

The project includes exploratory data analysis, data cleaning and preparation, data visualization, and market basket analysis using a priori algorithm and other techniques.

Objectives

The main objectives of this project are:

• Identify the top-selling products by category and region
• Determine the best sales month (seasonality)
• Analyze best sales hours along the day
• Perform market basket analysis to identify product associations and cross-selling opportunities.

Data Sources

The data for this project is an online sales data. One file for every month from 2019

• customers: information on customers (complete address detail including city and zip code)
• products: information on products, such as name, and price
• orders: information on orders, such as order number, date, and status
• transactions: information on individual transactions, such as product ID, quantity, and price

Methodology

The project uses the following methodology:

1. Exploratory Data Analysis (EDA): examine the data to understand its structure, distribution, and relationships
2. Data Cleaning and Preparation: transform the data to remove missing or erroneous values, create new variables, and aggregate the data
3. Data Visualization: create charts, graphs, and plots to visualize the data and identify patterns and trends
4. Market Basket Analysis: use machine learning and other techniques to analyze product associations and cross-selling opportunities

Results

The analysis of the sales data has revealed the following insights:

• The top-selling products are charging cables, wired headphones and between more expensive products we can mention 27 in Monitor and iphone
• The most profitable customers are in San Francisco
• Sales trends show a peak in Octuber, November and above all December, with a decline in january-february and june-july. The best selling hours are at 12 and sunset.
• Market basket analysis has identified several product associations, such as ('iPhone', 'Lightning Charging Cable'), ('Google Phone', 'USB-C Charging Cable'). This opens opportunity for offering the associate product and even evaluate adding a discount to it.

Conclusion

This project has demonstrated the value of sales analysis in providing insights and recommendations to the management team. By analyzing sales data, the company can make informed decisions on product offerings, marketing strategies, and customer targeting.