https://github.com/preciousclement/superstore-sales-analysis
This project involves analyzing sales data from a fictional superstore to uncover key business insights. The analysis includes exploring sales patterns, customer demographics, product performance, and regional trends. Using Excel and Tableau.
https://github.com/preciousclement/superstore-sales-analysis
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This project involves analyzing sales data from a fictional superstore to uncover key business insights. The analysis includes exploring sales patterns, customer demographics, product performance, and regional trends. Using Excel and Tableau.
- Host: GitHub
- URL: https://github.com/preciousclement/superstore-sales-analysis
- Owner: Preciousclement
- Created: 2024-08-27T14:56:01.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-10-21T13:13:34.000Z (8 months ago)
- Last Synced: 2024-10-21T20:57:05.355Z (8 months ago)
- Size: 1.23 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Superstore Sales Analysis
This project involves analyzing sales data from a fictional superstore to uncover key business insights.
## Table of Content
- [Project Overview](#project-overview)
- [Data Source](#data-source)
- [Tools Used](#tools-used)
- [Objectives](#objectives)
- [Steps Taken](#steps-taken)
- [Key Insight for 2023](#key-insight-for-2023)
- [Conclusion](#conclusion)
### Project Overview
The analysis of this project includes exploring sales patterns, previous year perfomance from current year, product performance, profits and regional trends .
Using Excel and Tableau, the project aims to provide actionable recommendations for optimizing inventory, improving sales strategies, and enhancing customer satisfaction.
### Data Source
The primary data set used for this analysis is from kaggle.com
The dataset consists of 9,995 rows with the folllowing attributes; Row ID, Order ID, Order Date, Ship Date, Ship Mode, Customer ID, Customer Name, Segment, Country, City, State, Postal Code, Region, Product ID, Category, Sub-Category, Product Name, Sales Quantity, Discount and Profit
### Tools Used
- Excel
- Tableau
### Objectives
1. Data Cleaning: Ensure data integrity by addressing inconsistencies, such as date formatting issues and missing values.
2. Exploratory Data Analysis (EDA): Identify trends and patterns within the data.
3. Visualization: Create visual representations of the insights to facilitate understanding and decision-making.
### Steps Taken
#### 1. Analyzed Requirements
- Understood the data
- The right requirements we collected, the right charts were choosen, as well as the colors
- Drew a mockup for guidance
#### 2. Building Data Source & Charts
- Data was cleaned with Excel
- Data was connected to Tableau and data model created
- Renamed fields and tables
- Crosschecked data types
#### 3. Created Calculated Fields:
- Current Year, Previous Year
- CY of Profit, Sales, Quantity, Orders and Customers
- PY of Profit, Sales, Quantity, Orders and Customers
- Min/Max of Profit, Sales, Quantity, orders and Customers
- % Difference of Profit, Sales, Quantity, orders and Customers
- Inputed a "select year" parameter
- Build charts accordingly
- Formatted the charts by cleaning up axis & headers. coloring and tooltip
```CALCULATED FIELDS CREATED
-- CY of Sales --
IF YEAR([Order Date]) = [Select Year] THEN [Sales]
END
-- CY Customer --
IF YEAR([Order Date]) = [Select Year] THEN [Customer ID]
END
**Formular was also used for CY profit, quantity and orders**
-- PY of Sales --
IF YEAR([Order Date]) = [Select Year] - 1 THEN [Sales]
END
-- PY Customers --
IF YEAR([Order Date]) = [Select Year] - 1 THEN [Customer ID]
END
**Formular was also used for PY profit, quantity and orders**
-- % Difference of Sales between years --
(SUM([CY Sales]) - SUM([PY Sales])) / SUM([PY Sales])
-- % Difference of Customers between years --
(COUNTD([CY Customers]) - COUNTD([PY Customers])) / COUNTD([PY Customers])
**Formular was also used for % Diff of profit, quantity and orders**
-- Min/Max of Sales --
IF SUM([CY Sales])= WINDOW_MAX(SUM([CY Sales]))
THEN SUM([CY Sales])
ELSEIF SUM([CY Sales])= WINDOW_MIN(SUM([CY Sales]))
THEN SUM([CY Sales])
END
-- Min/Mx of Customers --
IF COUNTD([CY Customers]) = WINDOW_MAX(COUNTD([CY Customers]))
THEN COUNTD([CY Customers])
ELSEIF COUNTD([CY Customers]) = WINDOW_MIN(COUNTD([CY Customers]))
THEN COUNTD([CY Customers])
END
**Formular was also used for Min/Max of profit, quantity and orders**
```
#### 3. Dashboard Setup
- Drew Mockups for containers
- Built the container stgructures
- Pull all the charts together
- Formatted the charts by distributing evenly, including legends and adding inner & outer paddings
- Included Icons, Filter and Dynamics
**Dashbord**
Published on Tableau Public(Dynamic): https://public.tableau.com/views/SuperstoreSales2_17260991531630/CustomersDashboard?:language=en-US&:sid=&:redirect=auth&:display_count=n&:origin=viz_share_link
### Key Insight for 2023
1. **Overall Sales Performance:**
- **Total Sales:** The total sales for 2023 reached **$733k**, which is a **20.4% increase** compared to the previous year, showcasing a positive growth trend.
- **Total Profit:** Profits for the year totaled **$93k**, reflecting a **14.2% increase** year-over-year.
- **Total Quantity Sold:** A total of **12k units** were sold in 2023.
2. **Sales & Profit Trend:**
- The **average sales per month** for 2023 were **$14k**, while the **average profit per month** was **$2k**. This steady trend reflects consistent performance throughout the year.
3. **Customer Growth & Sales per Customer:**
- In 2023, we saw a total of **693 customers**, which is an **8.6% increase** compared to the previous year.
- The **average sales per customer** was **$1,058**, representing a **20.4% increase** year-over-year, indicating stronger spending or larger orders from the customer base.
4. **Total Orders:**
- The total number of orders for 2023 was **1,687**, with a **28.3% increase** compared to 2022, highlighting strong purchasing behavior.
5. **Regional Sales Distribution:**
- The **West region** led in total sales, followed by the **East**, then **Central**, and lastly, the **South**. This breakdown points to stronger market penetration in the Western and Eastern regions.
6. **Top 10 Customers by Profit:**
- The top 10 customers in 2023 contributed significantly to profits, with the highest-earning customer generating **$6,781** in profit and **$14,023** in sales. This emphasizes the importance of high-value customers to the overall profit margins.
### Conclusion
2023 marked strong growth with a **20.4% sales increase** and **14.2% profit rise**.
Higher order volumes and customer engagement, especially from top clients, drove success.
The **West region** led in sales, while other regions also showed growth potential.
Moving forward, the focus should be on optimizing customer relationships and expanding regional efforts to sustain momentum.