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

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

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...

This dataset contains a list of sales and movement data by item and department appended monthly. Update Frequency : Monthly

The link for the Excel project to download can be found on GitHub here. It includes the raw data, Pivot Tables, and an interactive dashboard with Pivot Charts and Slicers. The project also includes business questions and the formulas I used to answer. The image below is included for ease. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12904052%2F61e460b5f6a1fa73cfaaa33aa8107bd5%2FBusinessQuestions.png?generation=1686190703261971&alt=media" alt=""> The link for the Tableau adjusted dashboard can be found here.

A screenshot of the interactive Excel dashboard is also included below for ease. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12904052%2Fe581f1fce8afc732f7823904da9e4cce%2FScooter%20Dashboard%20Image.png?generation=1686190815608343&alt=media" alt="">

A structured dataset of over three months of sales transactions from three supermarket branches. It includes attributes such as invoice ID, branch, city, customer type, gender, product line, unit price, quantity, total, tax, payment method, and gross income. Designed for predictive analytics, sales forecasting, and customer behavior analysis.

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.

Explore our extensive Amazon Product Dataset, featuring detailed information on prices, ratings, sales volume, and more.

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.

pricing

High-quality, free real estate dataset from all around the United States, in CSV format. Over 10.000 records relevant to Real Estate investors, agents, and data scientists. We are working on complete datasets from a wide variety of countries. Don't hesitate to contact us for more information.

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!

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.

🔍 Total Sales: Achieved $456,000 in revenue across 1,000 transactions, with an average transaction value of $456.00.

👥 Customer Demographics:

Average Age: 41.39 years Gender Distribution: 51% male, 49% female Most active age groups: 31-40 & 41-50 years 🏷️ Product Performance:

Top Categories: Electronics and Clothing led the sales, each contributing $160,000, followed by Beauty products with $140,000. Quantity Sold: Clothing topped the charts with 894 units sold. 📈 Sales Trends: Identified key sales peaks, especially in May 2023, indicating the success of targeted promotional strategies.

Why This Matters:

Understanding these metrics allows for better-targeted marketing, efficient inventory management, and strategic planning to capitalize on peak sales periods. This project demonstrates the power of data-driven decision-making in retail!

💡 Takeaway: Power BI continues to be a game-changer in visualizing and interpreting complex data, helping businesses to not just see numbers but to translate them into actionable insights.

I’m always looking forward to new challenges and projects that push my skills further. If you're interested in diving into the details or discussing data insights, feel free to reach out!

PowerBI #DataAnalysis #RetailSales #DataVisualization #BusinessIntelligence #DataDriven

Music Sales by Format and Year

Sales data for music industry by format and year

By Charlie Hutcheson [source]

About this dataset

The Music Industry Sales by Format and Year dataset provides comprehensive information on the sales data for different music formats over a span of 40 years. The dataset aims to analyze and visualize the trends in music industry sales, specifically focusing on various formats and metrics used to measure these sales.

The dataset includes several key columns to facilitate data analysis, including Format which represents the different formats of music sales such as physical (CDs, vinyl) or digital (downloads, streaming). Additionally, the column Metric indicates the specific measure used to quantify the sales data, such as units sold or revenue generated. The column Year specifies the particular year in which the sales data was recorded.

To provide a more comprehensive understanding of each combination of format, metric, and year, additional columns are included. The Number of Records column denotes the total number of entries or records available for each unique combination. This information helps assess sample size reliability for further analysis. Moreover, there is an Actual Value column that presents precise numerical values representing the actual recorded sales figure corresponding to each format-metric-year combination.

This dataset is obtained from credible sources including RIAA's U.S Sales Database and was originally presented through a visualization by Visual Capitalist. It offers insights into historical trends in music industry sales patterns across different formats over four decades.

In order to enhance this dataset visual representation and further explore its potential insights accurately, it would be necessary to perform an exploratory analysis assessing: seasonal patterns within each format; changes in market share across multiple years; growth rates comparison between physical and digital formats; etc. These analyses can help identify emerging trends in consumer preferences along with underlying factors driving shifts in market dynamics. Additionally,the presentation media (such as charts or graphs) could benefit from improvements such as clearer labeling, more detailed annotations,captions that allow viewers to easily interpret visualized information,and arrangement providing a logical flow conducive to understanding the data

How to use the dataset

Dataset Overview

The dataset consists of the following columns:

• Format: The format of the music sales, such as physical (CDs, vinyl) or digital (downloads, streaming).
• Metric: The metric used to measure the sales, such as units sold or revenue generated.
• Year: The year in which the sales data was recorded.
• Number of Records: The number of records or entries for each combination of format, metric and year.
• Value (Actual): The actual value of the sales for each combination of format, metric and year.

Key Considerations

Before diving into analyzing this dataset, here are some key points to consider:

• Categorical Variables: Both Format and Metric columns contain categorical variables that represent different aspects related to music industry sales.
• Numeric Variables: Year, Number of Records, and Value (Actual) are numeric variables providing chronological information about record counts and actual sale values.

Interpreting Insights

To make meaningful interpretations using this data set:

Analyzing Different Formats:

• You can compare different formats' popularity over time based on units sold/revenue generated.
• Explore how digital formats have influenced physical format sales over time.
• Understand which formats have experienced growth or decline in specific years.

Evaluating Different Metrics:

• Analyze revenue trends compared to unit count trends for different formats each year.
• Identify metrics showing exceptional growth/decline compared across differing years/formats.

Understanding Sales Trends:

• Examine the relationship between the number of records and actual sales value each year.
• Identify periods where significant changes in music industry sales occurred.
• Observe trends and fluctuations based on different formats/metrics.

Visualizing Data

To enhance your analysis, create visualizations using this dataset:

• Time Series Analysis: Create line plots to visualize the trend in music sales for different formats over time.
• Comparative Analysis: Generate bar charts or grouped bar plots...

This dataset contains information about India's Sales of Motor Vehicles for2007-2019.Data from Ministry of Road Transport and Highways.

Sample data for exercises in Further Adventures in Data Cleaning.

The Shopee Products Dataset is a comprehensive resource that empowers businesses, researchers, and analysts to gain a holistic view of the Shopee e-commerce ecosystem. Whether your goal is to conduct market analysis, optimize pricing strategies, understand customer behavior, or evaluate competitors, this dataset offers the essential information you need to make informed decisions and succeed in the dynamic world of Shopee. At its core, this dataset provides key attributes such as product ID, title, ratings, reviews, pricing details, and seller information, among others. These fundamental data elements offer insights into product performance, customer sentiment, and seller credibility.

Sales Data contains information about the sale of freehold and leasehold properties within Western Australia. This dataset is derived from; information from Transfer of Land documents registered at Landgate subject to the Transfer of Land Act 1943 for each of the last 3 sales of the property, and known property attribute information at the time of last sale, that was gathered subject to the Valuation of Land Act 1977. This dataset reflects information about the last three sales of property dating back to 1988. As the information is gathered from 2 different sources of stored data, that have been captured to service the requirements of independent Legislation, data contained in this dataset is subject to anomalies and may not necessarily meet the intended purpose of the user. © Western Australian Land Information Authority. Use of Landgate data is subject to Personal Use License terms and conditions unless otherwise authorised under approved License terms and conditions.

Enterprise-Value-To-Sales-Ratio Time Series for MongoDB. MongoDB, Inc., together with its subsidiaries, provides general purpose database platform worldwide. The company provides MongoDB Atlas, a hosted multi-cloud database-as-a-service solution; MongoDB Enterprise Advanced, a commercial database server for enterprise customers to run in the cloud, on-premises, or in a hybrid environment; and Community Server, a free-to-download version of its database, which includes the functionality that developers need to get started with MongoDB. It offers professional services comprising consulting and training. The company was formerly known as 10gen, Inc. and changed its name to MongoDB, Inc. in August 2013. MongoDB, Inc. was incorporated in 2007 and is headquartered in New York, New York.

Dataset

This dataset was created by Sam Olkin

Contents