Sales Data Analysis
Ibiangake Nyong
Sales Data Analysis
This repository contains a comprehensive dataset of sales transactions, consisting of 9,994 rows and 13 columns. The data provides insights into various aspects of sales performance, including:
Key Features:
Sales Performance Metrics: Total sales, quantity sold, profit, discount, and payment methods (cash, POS, online transfer)
Customer Information: Customer name, segment (home office, corporate, consumer), city, state, region
Product Categories: Category (technology, office supplies, furniture) and sub-category (machines, copiers, binders, etc.)
Key Findings:
Based on the analysis, the following key findings emerged:
The top-performing sales region is the South, accounting for 35% of total sales.
The corporate segment is the largest customer segment, responsible for 40% of total sales.
Technology products are the best-selling category, with machines and copiers being the top sub-categories.
Online transfer is the most popular payment method, accounting for 30% of total sales, surpassing POS (25%) and cash (20%).
Key Metrics:
Count of Sales: Total number of sales transactions
Sum of Sales: Total revenue generated from sales
Unique Customers: Number of distinct customers who made purchases
Total Quantity: Total number of items sold
Total Profit: Total profit generated from sales
Total Discount: Total discount amount applied to sales
Sales by Payment Method: Breakdown of sales by cash, POS, and online transfer
Sales by Category and Sub-Category: Breakdown of sales by product category and sub-category
Data Analysis:
The dataset offers a wide range of opportunities for analysis, such as:
Identifying top-performing sales regions and customer segments
Analyzing sales trends by product category and sub-category
Evaluating the effectiveness of different payment methods
Investigating the relationship between discount rates and sales performance
Methodology
The analysis was conducted using a combination of data visualization and statistical techniques. The following steps were taken:
Data Cleaning: The dataset was cleaned and preprocessed to remove any missing or duplicate values.
Exploratory Data Analysis (EDA): Summary statistics and data visualizations were used to understand the distribution of variables and identify patterns in the data.
Descriptive Statistics: Means, medians, and standard deviations were calculated for key variables, such as sales, profit, and discount.
Pivot tables: pivot tables were used to summarize key metrics.
Data Visualization: Bar charts, pie chart, donut chart and cards were used to visualize the data and communicate findings.
Dashboard
A comprehensive dashboard was created to provide a clear and concise overview of the sales data. The dashboard includes key metrics with cards such as: - Count of sales - Sum of sales - Unique customers - Total quantity - Total profit - Total discount
Sales by Category: A pie chart showing the distribution of sales across different categories.
Order Returns: A donut chart showing the proportion of orders that were returned.
Payment Method Breakdown: Cards showing the total sales by cash ($427,604), POS ($466,937), and online transfer ($1,402,660).
Sales by Sub-Category: A bar chart showing the sales performance of different sub-categories.
Recommendations:
Based on the key findings, the following recommendations are made:
Develop a robust e-commerce platform: Invest in creating a user-friendly and secure online shopping experience to capitalize on the growing trend of online transfers.
Implement digital payment incentives: Offer discounts or rewards for customers who use online transfer payments to further encourage this behavior.
Enhance digital marketing efforts: Allocate more resources to digital marketing channels, such as social media, email marketing, and search engine optimization, to reach a wider online audience.
Explore mobile payment options: Consider integrating mobile payment methods, such as Apple Pay or Google Pay, to provide customers with more convenient payment options.
Focus on digital customer support: Develop a robust online customer support system to ensure that customers receive timely and effective assistance with their online purchases.
Sheet Structure:
The Google Sheet contains 3 sheets:
Cleaned Data: Raw sales data cleaned and formatted for analysis
Pivot Table: Summary of sales data by key metrics
Sales Dashboard: Visual representation of sales trends and insights
Dataset:
Like this project
Posted Feb 18, 2025
The data provides insights into various aspects of sales performance metrics such as total sales, quantity sold, profit, discount & payment methods (cash & POS)
