This dataset contains a list of sales and movement data by item and department appended monthly. Update Frequency : Monthly
By ANil [source]
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
For more datasets, click here.
- 🚨 Your notebook can be here! 🚨!
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
- 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...
https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
🔍 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!
https://www.aiceltech.com/termshttps://www.aiceltech.com/terms
Korean Companies’ Duty Free Sales Data provides important information to analyze consumer behavior of international travelers. This data includes sales volume, product categories, purchase timing, and location. Collected from duty-free sales data, it helps investors predict sales, gauge brand popularity, conduct competitor analysis, and understand international consumption trends, aiding in the valuation of Korean companies in the duty-free sector.
Autos include all passenger cars, including station wagons. The U.S. Bureau of Economic Analysis releases auto and truck sales data, which are used in the preparation of estimates of personal consumption expenditures.
Company Datasets for valuable business insights!
Discover new business prospects, identify investment opportunities, track competitor performance, and streamline your sales efforts with comprehensive Company Datasets.
These datasets are sourced from top industry providers, ensuring you have access to high-quality information:
We provide fresh and ready-to-use company data, eliminating the need for complex scraping and parsing. Our data includes crucial details such as:
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.
With Oxylabs Datasets, you can count on:
Pricing Options:
Standard Datasets: choose from various ready-to-use datasets with standardized data schemas, priced from $1,000/month.
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:
Unlock the power of data with Oxylabs' Company Datasets and supercharge your business insights today!
Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/
License information was derived automatically
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.
[TBD]
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.
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.
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="">
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
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.
The dataset below contains monthly sales data for trucks from a specific company throughout the year .
The dataset can be used to develop an ARIMA/SARIMA forecasting model for predicting the future sales
Do explore pinned 📌 notebook under code section for quick📊 reference
Consider an upvote ^ if you find the dataset useful
Dataset Card for "sales-conversations"
This dataset was created for the purpose of training a sales agent chatbot that can convince people. The initial idea came from: textbooks is all you need https://arxiv.org/abs/2306.11644 gpt-3.5-turbo was used for the generation
Structure
The conversations have a customer and a salesman which appear always in changing order. customer, salesman, customer, salesman, etc. The customer always starts the conversation Who ends the… See the full description on the dataset page: https://huggingface.co/datasets/goendalf666/sales-conversations.
Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
License information was derived automatically
This dataset was generated for use with Nile's Sales Assistant example: https://github.com/niledatabase/niledatabase/tree/main/examples/ai/sales_insight It includes:
Simulated sales conversations for 5 different fictional companies. Chunked and embedded version of these conversations (embeddings use OpenAI's text-embedding-3-small model).
The chunks and embeddings can be directly loaded to a vector databases and searched using vector similarity methods. The example's ./ingest directory… See the full description on the dataset page: https://huggingface.co/datasets/gwenshap/sales-transcripts.
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
Explore our extensive Amazon Product Dataset, featuring detailed information on prices, ratings, sales volume, and more.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
pricing
Open Government Licence - Canada 2.0https://open.canada.ca/en/open-government-licence-canada
License information was derived automatically
This table contains 3 series, with data for years 2016 - 2017 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada); Sales (3 items: Retail trade; Electronic shopping and mail-order houses; Retail E-commerce sales).
This dataset is generated for the purpose of analyzing furniture sales data using multiple regression techniques. It contains 2,500 rows with 15 columns, including 7 numerical columns and 7 categorical columns, along with a target variable (revenue) which represents the total revenue generated from furniture sales. The dataset captures various aspects of furniture sales, such as pricing, cost, sales volume, discount percentage, inventory levels, delivery time, and different categorical attributes like furniture type, material, color, and store location.
Guys please upload your notebook of this dataset so that others can also learn from your work
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the population of Sale City by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Sale City across both sexes and to determine which sex constitutes the majority.
Key observations
There is a majority of female population, with 58.09% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Scope of gender :
Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.
Variables / Data Columns
Good to know
Margin of Error
Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.
Custom data
If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.
Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.
This dataset is a part of the main dataset for Sale City Population by Race & Ethnicity. You can refer the same here
By Charlie Hutcheson [source]
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
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...
ExactOne delivers unparalleled consumer transaction insights to help investors and corporate clients uncover market opportunities, analyze trends, and drive better decisions.
Dataset Highlights - Source: Debit and credit card transactions from 600K+ active users and 2M accounts connected via Open Banking. Scale: Covers 250M+ annual transactions, mapped to 1,800+ merchants and 330+ tickers. Historical Depth: Over 6 years of transaction data. Flexibility: Analyse transactions by merchant/ticker, category/industry, or timeframe (daily, weekly, monthly, or quarterly).
ExactOne data offers visibility into key consumer industries, including: Airlines - Regional / Budget Airlines - Cargo Airlines - Full Service Autos - OEMs Communication Services - Cable & Satellite Communication Services - Integrated Telecommunications Communication Services - Wireless Telecom Consumer - Services Consumer - Health & Fitness Consumer Staples - Household Supplies Energy - Utilities Energy - Integrated Oil & Gas Financial Services - Insurance Grocers - Traditional Hotels - C-corp Industrial - Misc Industrial - Tools And Hardware Internet - E-commerce Internet - B2B Services Internet - Ride Hailing & Delivery Leisure - Online Gambling Media - Digital Subscription Real Estate - Brokerage Restaurants - Quick Service Restaurants - Fast Casual Restaurants - Pubs Restaurants - Specialty Retail - Softlines Retail - Mass Merchants Retail - European Luxury Retail - Specialty Retail - Sports & Athletics Retail - Footwear Retail - Dept Stores Retail - Luxury Retail - Convenience Stores Retail - Hardlines Technology - Enterprise Software Technology - Electronics & Appliances Technology - Computer Hardware Utilities - Water Utilities
Use Cases
For Private Equity & Venture Capital Firms: - Deal Sourcing: Identify high-growth opportunities. - Due Diligence: Leverage transaction data to evaluate investment potential. - Portfolio Monitoring: Track performance post-investment with real-time data.
For Consumer Insights & Strategy Teams: - Market Dynamics: Compare sales trends, average transaction size, and customer loyalty. - Competitive Analysis: Benchmark market share and identify emerging competitors. - E-commerce vs. Brick & Mortar Trends: Assess channel performance and strategic opportunities. - Demographic & Geographic Insights: Uncover growth drivers by demo and geo segments.
For Investor Relations Teams: - Shareholder Insights: Monitor brand performance relative to competitors. - Real-Time Intelligence: Analyse sales and market dynamics for public and private companies. - M&A Opportunities: Evaluate market share and growth potential for strategic investments.
Key Benefits of ExactOne - Understand Market Share: Benchmark against competitors and uncover emerging players. - Analyse Customer Loyalty: Evaluate repeat purchase behavior and retention rates. - Track Growth Trends: Identify key drivers of sales by geography, demographic, and channel. - Granular Insights: Drill into transaction-level data or aggregated summaries for in-depth analysis.
With ExactOne, investors and corporate leaders gain actionable, real-time insights into consumer behaviour and market dynamics, enabling smarter decisions and sustained growth.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Retail Sales in the United States increased 0.60 percent in August of 2025 over the previous month. This dataset provides - U.S. December Retail Sales Increased More Than Forecast - actual values, historical data, forecast, chart, statistics, economic calendar and news.
This dataset contains a list of sales and movement data by item and department appended monthly. Update Frequency : Monthly