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:

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.)

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%).

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

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

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.

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.

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.

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