Sales data for all Islanders Board Of Industry & Service (IBIS) stores.

E-Commerce Sales Dataset

Analyzing and Maximizing Online Business Performance

By ANil [source]

About this dataset

This dataset provides an in-depth look at the profitability of e-commerce sales. It contains data on a variety of sales channels, including Shiprocket and INCREFF, as well as financial information on related expenses and profits. The columns contain data such as SKU codes, design numbers, stock levels, product categories, sizes and colors. In addition to this we have included the MRPs across multiple stores like Ajio MRP , Amazon MRP , Amazon FBA MRP , Flipkart MRP , Limeroad MRP Myntra MRP and PaytmMRP along with other key parameters like amount paid by customer for the purchase , rate per piece for every individual transaction Also we have added transactional parameters like Date of sale months category fulfilledby B2b Status Qty Currency Gross amt . This is a must-have dataset for anyone trying to uncover the profitability of e-commerce sales in today's marketplace

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How to use the dataset

This dataset provides a comprehensive overview of e-commerce sales data from different channels covering a variety of products. Using this dataset, retailers and digital marketers can measure the performance of their campaigns more accurately and efficiently.

The following steps help users make the most out of this dataset: - Analyze the general sales trends by examining info such as month, category, currency, stock level, and customer for each sale. This will give you an idea about how your e-commerce business is performing in each channel.
- Review the Shiprocket and INCREF data to compare and analyze profitability via different fulfilment methods. This comparison would enable you to make better decisions towards maximizing profit while minimizing costs associated with each method’s referral fees and fulfillment rates.
- Compare prices between various channels such as Amazon FBA MRP, Myntra MRP, Ajio MRP etc using the corresponding columns for each store (Amazon MRP etc). You can judge which stores are offering more profitable margins without compromising on quality by analyzing these pricing points in combination with other information related to product sales (TP1/TP2 - cost per piece).
- Look at customer specific data such as TP 1/TP 2 combination wise Gross Amount or Rate info in terms price per piece or total gross amount generated by any SKU dispersed over multiple customers with relevant dates associated to track individual item performance relative to others within its category over time periods shortlisted/filtered appropriately.. Have an eye on items commonly utilized against offers or promotional discounts offered hence crafting strategies towards inventory optimization leading up-selling operations.?
- Finally Use Overall ‘Stock’ details along all the P & L Data including Yearly Expenses_IIGF information record for takeaways which might be aimed towards essential cost cutting measures like switching amongst delivery options carefully chosen out of Shiprocket & INCREFF leadings away from manual inspections catering savings under support personnel outsourcing structures.?

By employing a comprehensive understanding on how our internal subsidiaries perform globally unless attached respective audits may provide us remarkably lower operational costs servicing confidence; costing far lesser than being incurred taking into account entire pallet shipments tracking sheets representing current level supply chains efficiencies achieved internally., then one may finally scale profits exponentially increases cut down unseen losses followed up introducing newer marketing campaigns necessarily tailored according playing around multiple goods based spectrums due powerful backing suitable transportation boundaries set carefully

Research Ideas

• Analysing the difference in profitability between sales made through Shiprocket and INCREFF. This data can be used to see where the biggest profit margins lie, and strategize accordingly.
• Examining the Complete Cost structure of a product with all its components and their contribution towards revenue or profitability, i.e., TP 1 & 2, MRP Old & Final MRP Old together with Platform based MRP - Amazon, Myntra and Paytm etc., Currency based Profit Margin etc.
• Building a predictive model using Machine Learning by leveraging historical data to predict future sales volume and profits for e-commerce products across multiple categories/devices/platforms such as Amazon, Flipkart, Myntra etc as well providing m...

Grocery Sales Database - Data Card

Overview

The Grocery Sales Database is a structured relational dataset designed for analyzing sales transactions, customer demographics, product details, employee records, and geographical information across multiple cities and countries. This dataset is ideal for data analysts, data scientists, and machine learning practitioners looking to explore sales trends, customer behaviors, and business insights.

Database Schema

The dataset consists of seven interconnected tables:

File Name	Description
categories.csv	Defines the categories of the products.
cities.csv	Contains city-level geographic data.
countries.csv	Stores country-related metadata.
customers.csv	Contains information about the customers who make purchases.
employees.csv	Stores details of employees handling sales transactions.
products.csv	Stores details about the products being sold.
sales.csv	Contains transactional data for each sale.

Table Descriptions

1. categories

Key	Column Name	Data Type	Description
PK	CategoryID	INT	Unique identifier for each product category.
	CategoryName	VARCHAR(45)	Name of the product category.

2. cities

Key	Column Name	Data Type	Description
PK	CityID	INT	Unique identifier for each city.
	CityName	VARCHAR(45)	Name of the city.
	Zipcode	DECIMAL(5,0)	Population of the city.
FK	CountryID	INT	Reference to the corresponding country.

3. countries

Key	Column Name	Data Type	Description
PK	CountryID	INT	Unique identifier for each country.
	CountryName	VARCHAR(45)	Name of the country.
	CountryCode	VARCHAR(2)	Two-letter country code.

4. customers

Key	Column Name	Data Type	Description
PK	CustomerID	INT	Unique identifier for each customer.
	FirstName	VARCHAR(45)	First name of the customer.
	MiddleInitial	VARCHAR(1)	Middle initial of the customer.
	LastName	VARCHAR(45)	Last name of the customer.
FK	cityID	INT	City of the customer.
	Address	VARCHAR(90)	Residential address of the customer.

5. employees

Key	Column Name	Data Type	Description
PK	EmployeeID	INT	Unique identifier for each employee.
	FirstName	VARCHAR(45)	First name of the employee.
	MiddleInitial	VARCHAR(1)	Middle initial of the employee.
	LastName	VARCHAR(45)	Last name of the employee.
	BirthDate	DATE	Date of birth of the employee.
	Gender	VARCHAR(10)	Gender of the employee.
FK	CityID	INT	unique identifier for city
	HireDate	DATE	Date when the employee was hired.

6. products

Key	Column Name	Data Type	Description
PK	ProductID	INT	Unique identifier for each product.
	ProductName	VARCHAR(45)	Name of the product.
	Price	DECIMAL(4,0)	Price per unit of the product.
	CategoryID	INT	unique category identifier
	Class ...

Sales transactions from an SME (small and medium enterprise) in Chemical Products industry. Data holds sales date, customer, product, price, quantity, city and sales person information. Data Set can be useful for performance tracking and monitoring, customer segmentation, financial forecasting, anomaly detection etc. Columns and details: DATE: Date of sales in DD/MM/YYYY hh:mm format SKU: Stock Code of the product CUSTOMER: Customer Code CITY: City ID PRICE: Sales price of the product QUANTITY: Number of items in the transaction SALESPERSON : Responsible sales person

Company Datasets for valuable business insights!

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You can choose your preferred data delivery method, including various storage options, delivery frequency, and input/output formats.

Receive datasets in CSV, JSON, and other formats, with storage options like AWS S3 and Google Cloud Storage. Opt for one-time, monthly, quarterly, or bi-annual data delivery.

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• Delivering data sample: We provide a sample for your feedback on data quality and the entire delivery process.
• Continuous data delivery: We continuously collect public data and deliver custom datasets per the agreed frequency.

Unlock the power of data with Oxylabs' Company Datasets and supercharge your business insights today!

This dataset contains transaction data from a fictitious SaaS company selling sales and marketing software to other companies (B2B). In the dataset, each row represents a single transaction/order (9,994 transactions), and the columns include:

Here is the Original Dataset: https://ee-assets-prod-us-east-1.s3.amazonaws.com/modules/337d5d05acc64a6fa37bcba6b921071c/v1/SaaS-Sales.csv

Features

| # | Name of the attribute | Description | | -- | --------------------- | -------------------------------------------------------- | | 1 | Row ID | A unique identifier for each transaction. | | 2 | Order ID | A unique identifier for each order. | | 3 | Order Date | The date when the order was placed. | | 4 | Date Key | A numerical representation of the order date (YYYYMMDD). | | 5 | Contact Name | The name of the person who placed the order. | | 6 | Country | The country where the order was placed. | | 7 | City | The city where the order was placed. | | 8 | Region | The region where the order was placed. | | 9 | Subregion | The subregion where the order was placed. | | 10 | Customer | The name of the company that placed the order. | | 11 | Customer ID | A unique identifier for each customer. | | 13 | Industry | The industry the customer belongs to. | | 14 | Segment | The customer segment (SMB, Strategic, Enterprise, etc.). | | 15 | Product | The product was ordered. | | 16 | License | The license key for the product. | | 17 | Sales | The total sales amount for the transaction. | | 18 | Quantity | The total number of items in the transaction. | | 19 | Discount | The discount applied to the transaction. | | 20 | Profit | The profit from the transaction. |

Inspiration: The CRoss Industry Standard Process for Data Mining (CRISP-DM) CRISP-DM methodology

• [ ] Understanding the business
• [ ] Understanding the data
• [x] Preparing the data
• [ ] Modelling
• [ ] Evaluating
• [ ] Implementing the analysis.

taken from this Kaggle competition:

  Dataset Description

In this competition, you will predict sales for the thousands of product families sold at Favorita stores located in Ecuador. The training data includes dates, store and product information, whether that item was being promoted, as well as the sales numbers. Additional files include supplementary information that may be useful in building your models.

  File Descriptions and Data Field Information

train.csv… See the full description on the dataset page: https://huggingface.co/datasets/t4tiana/store-sales-time-series-forecasting.

SuperKart Sales Dataset

This dataset supports a sales prediction pipeline (Product × Store).

Source file: raw/SuperKart.csv Target: Product_Store_Sales_Total

Expected columns: Product_Id, Product_Weight, Product_Sugar_Content, Product_Allocated_Area, Product_Type, Product_MRP, Store_Id, Store_Establishment_Year, Store_Size, Store_Location_City_Type, Store_Type, Product_Store_Sales_Total

Use our constantly updated Walmart products dataset to get a complete snapshot of new products, categories, pricing, and consumer reviews. You may purchase the entire dataset or a customized subset, depending on your needs. Popular use cases: Identify product inventory gaps and increased demand for certain products, analyze consumer sentiment and define a pricing strategy by locating similar products and categories among your competitors. The dataset includes all major data points: product, SKU, GTIN, currency,timestamp, price,a nd more. Get your Walmart dataset today!

Dataset Description:

• The dataset represents retail transactional data. It contains information about customers, their purchases, products, and transaction details. The data includes various attributes such as customer ID, name, email, phone, address, city, state, zipcode, country, age, gender, income, customer segment, last purchase date, total purchases, amount spent, product category, product brand, product type, feedback, shipping method, payment method, and order status.

Key Points:

Customer Information:

• Includes customer details like ID, name, email, phone, address, city, state, zipcode, country, age, and gender. Customer segments are categorized into Premium, Regular, and New. ##Transaction Details:
• Transaction-specific data such as transaction ID, last purchase date, total purchases, amount spent, total purchase amount, feedback, shipping method, payment method, and order status. ##Product Information:
• Contains product-related details such as product category, brand, and type. Products are categorized into electronics, clothing, grocery, books, and home decor. ##Geographic Information:
• Contains location details including city, state, and country. Available for various countries including USA, UK, Canada, Australia, and Germany. ##Temporal Information:
• Last purchase date is provided along with separate columns for year, month, date, and time. Allows analysis based on temporal patterns and trends. ##Data Quality:
• Some rows contain null values, and others are duplicates, which may need to be handled during data preprocessing. Null values are randomly distributed across rows. Duplicate rows are available at different parts of the dataset. ##Potential Analysis:
• Customer segmentation analysis based on demographics, purchase behavior, and feedback. Sales trend analysis over time to identify peak seasons or trends. Product performance analysis to determine popular categories, brands, or types. Geographic analysis to understand regional preferences and trends. Payment and shipping method analysis to optimize services. Customer satisfaction analysis based on feedback and order status. ##Data Preprocessing:
• Handling null values and duplicates. Parsing and formatting temporal data. Encoding categorical variables. Scaling numerical variables if required. Splitting data into training and testing sets for modeling.

Analysis of ‘Big Mart Sales’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/akashdeepkuila/big-mart-sales on 12 November 2021.

--- Dataset description provided by original source is as follows ---

Context

The data scientists at Big Mart have collected 2013 sales data for 1559 products across 10 stores in different cities. Also, certain attributes of each product and store have been defined. The aim is to build a predictive model and predict the sales of each product at a particular outlet.

Using this model, Big Mart will try to understand the properties of products and outlets which play a key role in increasing sales.

Please note that the data may have missing values as some stores might not report all the data due to technical glitches. Hence, it will be required to treat them accordingly.

Content

The dataset provides the product details and the outlet information of the products purchased with their sales value split into a train set (8523) and a test (5681) set. Train file: CSV containing the item outlet information with sales value Test file: CSV containing item outlet combinations for which sales need to be forecasted

Variable Description

• ProductID : unique product ID
• Weight : weight of products
• FatContent : specifies whether the product is low on fat or not
• Visibility : percentage of total display area of all products in a store allocated to the particular product
• ProductType : the category to which the product belongs
• MRP : Maximum Retail Price (listed price) of the products
• OutletID : unique store ID
• EstablishmentYear : year of establishment of the outlets
• OutletSize : the size of the store in terms of ground area covered
• LocationType : the type of city in which the store is located
• OutletType : specifies whether the outlet is just a grocery store or some sort of supermarket
• OutletSales : (target variable) sales of the product in the particular store

Inspiration

Sales of a given product at a retail store can depend both on store attributes as well as product attributes. The dataset is ideal to explore and build a data science model to predict the future sales.

--- Original source retains full ownership of the source dataset ---

Myntra is a major Indian fashion e-commerce company. The crawl Feeds team extracted more than 30K+ records for research and analysis purposes. Last extracted on 25th July 2021.

Contact crawl feeds team to customize dataset as per your needs like format changes, data frequency, and adding or removing fields.

1) Data Introduction • The Agro sales 2024-2025 Dataset is a compilation of agricultural-related sales data from 2024 to 2025, and the JM sales.csv file includes monthly and quarterly agricultural sales performance of certain regions or companies.

2) Data Utilization (1) Agro sales 2024-2025 Dataset has characteristics that: • This dataset contains a column-by-column list of key transaction information related to agricultural sales, including sales date, item, quantity, unit price, and total sales. • It reflects the actual sales flow of agricultural sites and is structured to enable time-series (annual, monthly, and quarterly) analysis. (2) Agro sales 2024-2025 Dataset can be used to: • Sales Trend Analysis: By analyzing agricultural sales and sales fluctuations by period, you can identify seasonality, growth trends, popular items, and more. • Demand forecasting and inventory management: It can be used to develop demand forecasting models based on machine learning or to support decision-making for efficient inventory management.

Utilize our Costco products dataset to gain a comprehensive view of new products, categories, pricing, and customer feedback. You can acquire the full dataset or tailor it to fit specific requirements.

Popular use cases include identifying inventory shortages and pinpointing high-demand items, analyzing customer sentiment, and crafting pricing strategies by comparing similar products and categories with your competitors.

The Costco dataset may include these data points: category, product name, description, images, features, price, specifications, review count, review score, review texts, and more. Subsets are available by categories and specific data points.

Context

This dataset contains lot of historical sales data. It was extracted from a Brazilian top retailer and has many SKUs and many stores. The data was transformed to protect the identity of the retailer.

Content

[TBD]

Acknowledgements

This data would not be available without the full collaboration from our customers who understand that sharing their core and strategical information has more advantages than possible hazards. They also support our continuos development of innovative ML systems across their value chain.

Inspiration

Every retail business in the world faces a fundamental question: how much inventory should I carry? In one hand to mush inventory means working capital costs, operational costs and a complex operation. On the other hand lack of inventory leads to lost sales, unhappy customers and a damaged brand.

Current inventory management models have many solutions to place the correct order, but they are all based in a single unknown factor: the demand for the next periods.

This is why short-term forecasting is so important in retail and consumer goods industry.

We encourage you to seek for the best demand forecasting model for the next 2-3 weeks. This valuable insight can help many supply chain practitioners to correctly manage their inventory levels.

HitHorizons Manufacturing Company Data API gives access to aggregated firmographic data on 4,289,762 manufacturing companies from the whole of Europe and beyond.

Company registration data: company name national identifier and its type registered address: street, postal code, city, state / province, country business activity: SIC code, local activity code with classification system year of establishment company type location type

Sales and number of employees data: sales in EUR, USD and local currency (with local currency code) total number of employees sales and number of employees accuracy local number of employees (in case of multiple branches) companies’ sales and number of employees market position compared to other companies in a country / industry / region

Industry data: size of the whole industry size of all companies operating within a particular SIC code benchmarking within a particular country or industry regional benchmarking (EU 27, state / province)

Contact details: company website company email domain (without person’s name)

Invoicing details available for selected countries: company name company address company VAT number

Objectives:

• I leveraged advanced data visualization techniques to extract valuable insights from a comprehensive dataset. By visualizing sales patterns, customer behavior, and product trends, I identified key growth opportunities and provided actionable recommendations to optimize business strategies and enhance overall performance. you can find the GitHub repo here Link to GitHub Repository.

Data Description:

there are exactly 6 table and 1 is a fact table and the rest of them are dimension tables: Fact Table:

payment_key:
  Description: An identifier representing the payment transaction associated with the fact.
  Use Case: This key links to a payment dimension table, providing details about the payment method and related information.

customer_key:
  Description: An identifier representing the customer associated with the fact.
  Use Case: This key links to a customer dimension table, providing details about the customer, such as name, address, and other customer-specific information.

time_key:
  Description: An identifier representing the time dimension associated with the fact.
  Use Case: This key links to a time dimension table, providing details about the time of the transaction, such as date, day of the week, and month.

item_key:
  Description: An identifier representing the item or product associated with the fact.
  Use Case: This key links to an item dimension table, providing details about the product, such as category, sub-category, and product name.

store_key:
  Description: An identifier representing the store or location associated with the fact.
  Use Case: This key links to a store dimension table, providing details about the store, such as location, store name, and other store-specific information.

quantity:
  Description: The quantity of items sold or involved in the transaction.
  Use Case: Represents the amount or number of items associated with the transaction.

unit:
  Description: The unit or measurement associated with the quantity (e.g., pieces, kilograms).
  Use Case: Specifies the unit of measurement for the quantity.

unit_price:
  Description: The price per unit of the item.
  Use Case: Represents the cost or price associated with each unit of the item.

total_price:
  Description: The total price of the transaction, calculated as the product of quantity and unit price.
  Use Case: Represents the overall cost or revenue generated by the transaction.

Customer Table: customer_key:

Description: An identifier representing a unique customer.
Use Case: Serves as the primary key to link with the fact table, allowing for easy and efficient retrieval of customer-specific information.

name:

Description: The name of the customer.
Use Case: Captures the personal or business name of the customer for identification and reference purposes.

contact_no:

Description: The contact number associated with the customer.
Use Case: Stores the phone number or contact details for communication or outreach purposes.

nid:

Description: The National ID (NID) or a unique identification number for the customer.

Item Table: item_key:

Description: An identifier representing a unique item or product.
Use Case: Serves as the primary key to link with the fact table, enabling retrieval of detailed information about specific items in transactions.

item_name:

Description: The name or title of the item.
Use Case: Captures the descriptive name of the item, providing a recognizable label for the product.

desc:

Description: A description of the item.
Use Case: Contains additional details about the item, such as features, specifications, or any relevant information.

unit_price:

Description: The price per unit of the item.
Use Case: Represents the cost or price associated with each unit of the item.

man_country:

Description: The country where the item is manufactured.
Use Case: Captures the origin or manufacturing location of the item.

supplier:

Description: The supplier or vendor providing the item.
Use Case: Stores the name or identifier of the supplier, facilitating tracking of item sources.

unit:

Description: The unit of measurement associated with the item (e.g., pieces, kilograms).

Store Table: store_key:

Description: An identifier representing a unique store or location.
Use Case: Serves as the primary key to link with the fact table, allowing for easy retrieval of information about transactions associated with specific stores.

division:

Description: The administrative division or region where the store is located.
Use Case: Captures the broader geographical area in which...

Introducing E-Commerce Product Datasets!

Unlock the full potential of your product strategy with E-Commerce Product Datasets. Gain invaluable insights to optimize your product offerings and pricing, analyze top-selling strategies, and assess customer sentiment.

Our E-Commerce Datasets Source:

1. Amazon: Access accurate product data from Amazon, including categories, pricing, reviews, and more.

2. Walmart: Receive comprehensive product information from Walmart, covering pricing, sellers, ratings, availability, and more.

E-Commerce Product Datasets provide structured and actionable data, empowering you to understand customer needs and enhance product strategies. We deliver fresh and precise public e-commerce data, including product names, brands, prices, number of sellers, review counts, ratings, and availability.

You have the flexibility to tailor data delivery to your specific needs:

• Receive datasets in various formats, including JSON and CSV.
• Choose delivery via SFTP or directly to your cloud storage (e.g., AWS S3, Google Cloud Storage).
• Select from one-time, monthly, quarterly, or bi-annual data delivery frequencies.

Why Choose Oxylabs E-Commerce Datasets:

1. Fresh and accurate data: Access clean and structured public e-commerce data collected by our leading web scraping professionals.

2. Time and resource savings: Let our experts handle data extraction at an affordable cost, allowing you to focus on your core business objectives.

3. Customizable solutions: Share your unique business needs, and our team will craft customized dataset solutions tailored to your requirements.

4. Legal compliance: Partner with a trusted leader in ethical data collection, endorsed by Fortune 500 companies and fully compliant with GDPR and CCPA regulations.

Pricing Options:

Custom Datasets: Tailor datasets from any public web domain to your unique business needs. Contact our sales team for custom pricing.

Experience a seamless journey with Oxylabs:

• Understanding your data needs: We work closely to understand your business nature and daily operations, defining your unique data requirements.
• Developing a customized solution: Our experts create a custom framework to extract public data using our in-house web scraping infrastructure.
• Delivering data sample: We provide a sample for your feedback on data quality and the entire delivery process.
• Continuous data delivery: We continuously collect public data and deliver custom datasets per the agreed frequency.

Unlock the potential of your e-commerce strategy with E-Commerce Product Datasets!

US B2B Contact Database | 200M+ Verified Records | 95% Accuracy | API/CSV/JSON Elevate your sales and marketing efforts with America's most comprehensive B2B contact data, featuring over 200M+ verified records of decision-makers, from CEOs to managers, across all industries. Powered by AI and refreshed bi-weekly, this dataset ensures you have access to the freshest, most accurate contact details available for effective outreach and engagement.

Key Features & Stats:

200M+ Decision-Makers: Includes C-level executives, VPs, Directors, and Managers.

95% Accuracy: Email & Phone numbers verified for maximum deliverability.

Bi-Weekly Updates: Never waste time on outdated leads with our frequent data refreshes.

50+ Data Points: Comprehensive firmographic, technographic, and contact details.

Core Fields:

Direct Work Emails & Personal Emails for effective outreach.

Mobile Phone Numbers for cold calls and SMS campaigns.

Full Name, Job Title, Seniority for better personalization.

Company Insights: Size, Revenue, Funding data, Industry, and Tech Stack for a complete profile.

Location: HQ and regional offices to target local, national, or international markets.

Top Use Cases:

Cold Email & Calling Campaigns: Target the right people with accurate contact data.

CRM & Marketing Automation Enrichment: Enhance your CRM with enriched data for better lead management.

ABM & Sales Intelligence: Target the right decision-makers and personalize your approach.

Recruiting & Talent Mapping: Access CEO and senior leadership data for executive search.

Instant Delivery Options:

JSON – Bulk downloads via S3 for easy integration.

REST API – Real-time integration for seamless workflow automation.

CRM Sync – Direct integration with your CRM for streamlined lead management.

Enterprise-Grade Quality:

SOC 2 Compliant: Ensuring the highest standards of security and data privacy.

GDPR/CCPA Ready: Fully compliant with global data protection regulations.

Triple-Verification Process: Ensuring the accuracy and deliverability of every record.

Suppression List Management: Eliminate irrelevant or non-opt-in contacts from your outreach.

US Business Contacts | B2B Email Database | Sales Leads | CRM Enrichment | Verified Phone Numbers | ABM Data | CEO Contact Data | US B2B Leads | US prospects data

Superstore Sales Dataset

Overview: The Superstore Sales dataset provides comprehensive sales data for multiple products sold by a retail superstore. This dataset is suitable for exploring various aspects of sales analysis, including trend analysis, product performance, geographical segmentation, and consumer behavior.

Key Features:

Sales Data: Includes information on sales transactions, such as order date, order ID, quantity, and sales value. Product Information: Provides details about the products sold, including product category, subcategory, and product ID.

Geographical Segmentation: Contains information on the geographical location of sales, including country, state, and city.

Profit Analysis: Includes data on profits generated from sales transactions, enabling profitability analysis at various levels of granularity.

Consumer Segmentation: Provides insights into consumer behavior and preferences through segmentation variables such as customer ID, segment, and region.

Potential Use Cases: Time Series Analysis: Explore sales trends over time to identify seasonality, trends, and patterns.

Product Performance Analysis: Analyze the performance of different product categories and subcategories to identify top-selling products and opportunities for growth.

Geographic Analysis: Understand regional variations in sales performance and consumer behavior to optimize marketing strategies and inventory management.

Customer Segmentation: Segment customers based on purchasing behavior and demographics to tailor marketing campaigns and improve customer retention.

Dataset Information:

Source: https://www.kaggle.com/datasets/laibaanwer/superstore-sales-dataset Format: CSV (Comma-Separated Values) Size: 2.17 MB Columns: order_id, order_date, ship_date, ship_mode, customer_name, segment, state, country, market, region product_id, category, sub_category, product_name, sales, quantity, discount, profit, shipping_cost , order_priority, year

License: The dataset is available under the Apache 2.0. Please refer to the dataset source for licensing details.

Acknowledgments: We acknowledge the contributors and creators of the Superstore Sales dataset for making it publicly available for analysis and research purposes.

Find the URL of My GitHub repository where the project is hosted. Superstore Sales Data Dashboard Project