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:

• Owler: Gain valuable business insights and competitive intelligence. -AngelList: Receive fresh startup data transformed into actionable insights. -CrunchBase: Access clean, parsed, and ready-to-use business data from private and public companies. -Craft.co: Make data-informed business decisions with Craft.co's company datasets. -Product Hunt: Harness the Product Hunt dataset, a leader in curating the best new products.

We provide fresh and ready-to-use company data, eliminating the need for complex scraping and parsing. Our data includes crucial details such as:

• Company name;
• Size;
• Founding date;
• Location;
• Industry;
• Revenue;
• Employee count;
• Competitors.

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:

• Fresh and accurate data collected and parsed by our expert web scraping team.
• Time and resource savings, allowing you to focus on data analysis and achieving your business goals.
• A customized approach tailored to your specific business needs.
• Legal compliance in line with GDPR and CCPA standards, thanks to our membership in the Ethical Web Data Collection Initiative.

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:

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

Description

The datasets demonstrate the malware economy and the value chain published in our paper, Malware Finances and Operations: a Data-Driven Study of the Value Chain for Infections and Compromised Access, at the 12th International Workshop on Cyber Crime (IWCC 2023), part of the ARES Conference, published by the International Conference Proceedings Series of the ACM ICPS.

Using the well-documented scripts, it is straightforward to reproduce our findings. It takes an estimated 1 hour of human time and 3 hours of computing time to duplicate our key findings from MalwareInfectionSet; around one hour with VictimAccessSet; and minutes to replicate the price calculations using AccountAccessSet. See the included README.md files and Python scripts.

We choose to represent each victim by a single JavaScript Object Notation (JSON) data file. Data sources provide sets of victim JSON data files from which we've extracted the essential information and omitted Personally Identifiable Information (PII). We collected, curated, and modelled three datasets, which we publish under the Creative Commons Attribution 4.0 International License.

1. MalwareInfectionSet
We discover (and, to the best of our knowledge, document scientifically for the first time) that malware networks appear to dump their data collections online. We collected these infostealer malware logs available for free. We utilise 245 malware log dumps from 2019 and 2020 originating from 14 malware networks. The dataset contains 1.8 million victim files, with a dataset size of 15 GB.

2. VictimAccessSet
We demonstrate how Infostealer malware networks sell access to infected victims. Genesis Market focuses on user-friendliness and continuous supply of compromised data. Marketplace listings include everything necessary to gain access to the victim's online accounts, including passwords and usernames, but also detailed collection of information which provides a clone of the victim's browser session. Indeed, Genesis Market simplifies the import of compromised victim authentication data into a web browser session. We measure the prices on Genesis Market and how compromised device prices are determined. We crawled the website between April 2019 and May 2022, collecting the web pages offering the resources for sale. The dataset contains 0.5 million victim files, with a dataset size of 3.5 GB.

3. AccountAccessSet
The Database marketplace operates inside the anonymous Tor network. Vendors offer their goods for sale, and customers can purchase them with Bitcoins. The marketplace sells online accounts, such as PayPal and Spotify, as well as private datasets, such as driver's licence photographs and tax forms. We then collect data from Database Market, where vendors sell online credentials, and investigate similarly. To build our dataset, we crawled the website between November 2021 and June 2022, collecting the web pages offering the credentials for sale. The dataset contains 33,896 victim files, with a dataset size of 400 MB.

Credits Authors

• Billy Bob Brumley (Tampere University, Tampere, Finland)
• Juha Nurmi (Tampere University, Tampere, Finland)
• Mikko Niemelä (Cyber Intelligence House, Singapore)

Funding

This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme under project numbers 804476 (SCARE) and 952622 (SPIRS).

Alternative links to download: AccountAccessSet, MalwareInfectionSet, and VictimAccessSet.

The dataset originates from the book "Practical Statistics for Data Scientists" by Peter Bruce, Andrew Bruce, and Peter Gedeck.

Context:

A company selling a high-value service wants to determine which of two web presentations is more effective at selling. Due to the high value and infrequent nature of the sales, as well as the lengthy sales cycle, it would take too long to accumulate enough sales data to identify the superior presentation. Therefore, the company uses a proxy variable to measure effectiveness.

A proxy variable stands in for the true variable of interest, which may be unavailable, too costly, or too time-consuming to measure directly. In this case, the proxy variable is the amount of time users spend on a detailed interior page that describes the service.

Content:

The dataset includes a total of 36 sessions across the two web presentations: 21 sessions for page A and 15 sessions for page B. The goal is to determine if users spend more time on page B compared to page A. If users spend more time on page B, it would suggest that page B is more effective at engaging potential customers, and therefore, does a better selling job.

The time is expressed in hundredths of seconds. For example, a value of 0.1 indicates 10 seconds, and a value of 2.53 indicates 253 seconds.

Access high-fidelity consumer data powered by our proprietary modeling technology that provides the most comprehensive consumer intelligence, accurate targeting, first-party data enrichment, and personalization at scale. Our deterministic dataset, anchored in the purchasing habits of over 140 million U.S. consumers, delivers superior targeting performance with proven 70% increase in ROAS.

Core Data Assets Transactional Data Foundation: Real purchasing behavior from over 140 million U.S. consumers with 8.5 billion behavioral signals across 250 million adults. Seven years of daily credit card and debit card purchase data aggregated from all major credit cards sourced from more than 300 national banks, capturing $2+ trillion in annual discretionary spending.

Consumer Demographics & Lifestyle: Comprehensive profiles including age, income, household composition, geographic distribution, education, employment, and lifestyle indicators. Our proprietary taxonomy organizes consumer spending across 8,000+ brands and 2,500+ merchants, from major retailers to emerging direct-to-consumer brands.

Behavioral Segmentation: 150+ custom consumer communities including demographic groups (Gen Z, Millennials, Gen X), lifestyle segments (Health & Fitness Enthusiasts, Tech Early Adopters, Luxury Shoppers), and behavioral categories (Deal Seekers, Brand Loyalists, Premium Service Users, Streaming Subscribers). Purchase Intelligence: Deep insights into consumer spending patterns across entertainment, fitness, fashion, technology, travel, dining, and retail categories. Our models identify cross-category purchasing behaviors, seasonal trends, and brand switching patterns to optimize targeting strategies. Advanced Modeling Technology

Our proprietary consumer intelligence engine combines deterministic transaction-based data with Smart Audience Engineering that transforms first-party signals from anonymized website traffic, behavioral indicators, and CRM enrichment into precision-modeled segments. Unlike traditional data providers who sell static lists, our AI-powered predictive modeling continuously learns and optimizes for unprecedented precision and superior conversion outcomes.

Performance Advantages: Audiences built on user-level transactional data deliver 70% increase in ROAS compared to traditional targeting methods. Weekly-optimized audiences with performance narratives eliminate wasted ad spend by 20-30%, while our deterministic AI models analyze hundreds of attributes and conversion-validated signals to identify prospects with genuine purchase intent, not just lookalike behaviors.

Selling prices of main crop potatoes

Access the Home Depot products dataset, a comprehensive collection of web-scraped data featuring home improvement products. Discover trending tools, hardware, appliances, décor, and gardening essentials to enhance your projects. From power tools and building materials to lighting, furniture, and outdoor living items, this dataset provides insights into top-rated products, best-selling brands, and emerging trends.

Download now to explore detailed product data for smarter decision-making in home improvement, DIY, and construction projects.

For a closer look at the product-level data we’ve extracted from Home Depot, including pricing, stock status, and detailed specifications, visit the Home Depot dataset page. You can explore sample records and submit a request for tailored extracts directly from there.

Individuals using the internet for selling goods or services

Track dealer health through topline sales, average sales prices, and inventory sell through for major online auto sellers. Web Auto Sellers data is sourced from vehicle sales and stocking information for US online used auto retailers.

The Office of Policy and Management maintains a listing of all real estate sales with a sales price of $2,000 or greater that occur between October 1 and September 30 of each year. For each sale record, the file includes: town, property address, date of sale, property type (residential, apartment, commercial, industrial or vacant land), sales price, and property assessment. Data are collected in accordance with Connecticut General Statutes, section 10-261a and 10-261b: https://www.cga.ct.gov/current/pub/chap_172.htm#sec_10-261a and https://www.cga.ct.gov/current/pub/chap_172.htm#sec_10-261b. Annual real estate sales are reported by grand list year (October 1 through September 30 each year). For instance, sales from 2018 GL are from 10/01/2018 through 9/30/2019. Some municipalities may not report data for certain years because when a municipality implements a revaluation, they are not required to submit sales data for the twelve months following implementation.

Dataset from Singapore Land Authority. For more information, visit https://data.gov.sg/datasets/d_7a043a1e4dee8041cff62b2cdd152b03/view

Dataset

This dataset was created by Rahh 2333

Released under Other (specified in description)

Contents

Dataset Card for [best-selling-video-games]

  Dataset Summary

[More Information Needed]

  Supported Tasks and Leaderboards

[More Information Needed]

  Languages

[More Information Needed]

  Dataset Structure





  Data Instances

[More Information Needed]

  Data Fields

[More Information Needed]

  Data Splits

[More Information Needed]

  Dataset Creation





  Curation Rationale

[More Information Needed]… See the full description on the dataset page: https://huggingface.co/datasets/arjunpatel/best-selling-video-games.

In today's digital landscape, data transparency and compliance are paramount. Organizations across industries are striving to maintain trust and adhere to regulations governing data privacy and security. To support these efforts, we present our comprehensive Ads.txt and App-Ads.txt dataset.

Key Benefits of Our Dataset:

• Coverage: Our dataset offers a comprehensive view of the Ads.txt and App-Ads.txt files, providing valuable information about publishers, advertisers, and the relationships between them. You gain a holistic understanding of the digital advertising ecosystem.
• Multiple Data Formats: We understand that flexibility is essential. Our dataset is available in multiple formats, including .CSV, .JSON, and more. Choose the format that best suits your data processing needs.
• Global Scope: Whether your business operates in a single country or spans multiple continents, our dataset is tailored to meet your needs. It provides data from various countries, allowing you to analyze regional trends and compliance.
  • Top-Quality Data: Quality matters. Our dataset is meticulously curated and continuously updated to deliver the most accurate and reliable information. Trust in the integrity of your data for critical decision-making.
  • Seamless Integration: We've designed our dataset to seamlessly integrate with your existing systems and workflows. No disruptions—just enhanced compliance and efficiency.

The Power of Ads.txt & App-Ads.txt: Ads.txt (Authorized Digital Sellers) and App-Ads.txt (Authorized Sellers for Apps) are industry standards developed by the Interactive Advertising Bureau (IAB) to increase transparency and combat ad fraud. These files specify which companies are authorized to sell digital advertising inventory on a publisher's website or app. Understanding and maintaining these files is essential for data compliance and the prevention of unauthorized ad sales.

How Can You Benefit? - Data Compliance: Ensure that your organization adheres to industry standards and regulations by monitoring Ads.txt and App-Ads.txt files effectively. - Ad Fraud Prevention: Identify unauthorized sellers and take action to prevent ad fraud, ultimately protecting your revenue and brand reputation. - Strategic Insights: Leverage the data in these files to gain insights into your competitors, partners, and the broader digital advertising landscape. - Enhanced Decision-Making: Make data-driven decisions with confidence, armed with accurate and up-to-date information about your advertising partners. - Global Reach: If your operations span the globe, our dataset provides insights into the Ads.txt and App-Ads.txt files of publishers worldwide.

Multiple Data Formats for Your Convenience: - CSV (Comma-Separated Values): A widely used format for easy data manipulation and analysis in spreadsheets and databases. - JSON (JavaScript Object Notation): Ideal for structured data and compatibility with web applications and APIs. - Other Formats: We understand that different organizations have different preferences and requirements. Please inquire about additional format options tailored to your needs.

Data That You Can Trust:

We take data quality seriously. Our team of experts curates and updates the dataset regularly to ensure that you receive the most accurate and reliable information available. Your confidence in the data is our top priority.

Seamless Integration:

Integrate our Ads.txt and App-Ads.txt dataset effortlessly into your existing systems and processes. Our goal is to enhance your compliance efforts without causing disruptions to your workflow.

In Conclusion:

Transparency and compliance are non-negotiable in today's data-driven world. Our Ads.txt and App-Ads.txt dataset empowers you with the knowledge and tools to navigate the complexities of the digital advertising ecosystem while ensuring data compliance and integrity. Whether you're a Data Protection Officer, a data compliance professional, or a business leader, our dataset is your trusted resource for maintaining data transparency and safeguarding your organization's reputation and revenue.

Get Started Today:

Don't miss out on the opportunity to unlock the power of data transparency and compliance. Contact us today to learn more about our Ads.txt and App-Ads.txt dataset, available in multiple formats and tailored to your specific needs. Join the ranks of organizations worldwide that trust our dataset for a compliant and transparent future.

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.

Context

E-commerce has become a new channel to support businesses development. Through e-commerce, businesses can get access and establish a wider market presence by providing cheaper and more efficient distribution channels for their products or services. E-commerce has also changed the way people shop and consume products and services. Many people are turning to their computers or smart devices to order goods, which can easily be delivered to their homes.

Content

This is a sales transaction data set of UK-based e-commerce (online retail) for one year. This London-based shop has been selling gifts and homewares for adults and children through the website since 2007. Their customers come from all over the world and usually make direct purchases for themselves. There are also small businesses that buy in bulk and sell to other customers through retail outlet channels.

The data set contains 500K rows and 8 columns. The following is the description of each column. 1. TransactionNo (categorical): a six-digit unique number that defines each transaction. The letter “C” in the code indicates a cancellation. 2. Date (numeric): the date when each transaction was generated. 3. ProductNo (categorical): a five or six-digit unique character used to identify a specific product. 4. Product (categorical): product/item name. 5. Price (numeric): the price of each product per unit in pound sterling (£). 6. Quantity (numeric): the quantity of each product per transaction. Negative values related to cancelled transactions. 7. CustomerNo (categorical): a five-digit unique number that defines each customer. 8. Country (categorical): name of the country where the customer resides.

There is a small percentage of order cancellation in the data set. Most of these cancellations were due to out-of-stock conditions on some products. Under this situation, customers tend to cancel an order as they want all products delivered all at once.

Inspiration

Information is a main asset of businesses nowadays. The success of a business in a competitive environment depends on its ability to acquire, store, and utilize information. Data is one of the main sources of information. Therefore, data analysis is an important activity for acquiring new and useful information. Analyze this dataset and try to answer the following questions. 1. How was the sales trend over the months? 2. What are the most frequently purchased products? 3. How many products does the customer purchase in each transaction? 4. What are the most profitable segment customers? 5. Based on your findings, what strategy could you recommend to the business to gain more profit?

Photo by CardMapr on Unsplash

Unlock the full potential of LinkedIn data with our extensive dataset that combines profiles, company information, and job listings into one powerful resource for business decision-making, strategic hiring, competitive analysis, and market trend insights. This all-encompassing dataset is ideal for professionals, recruiters, analysts, and marketers aiming to enhance their strategies and operations across various business functions. Dataset Features

Profiles: Dive into detailed public profiles featuring names, titles, positions, experience, education, skills, and more. Utilize this data for talent sourcing, lead generation, and investment signaling, with a refresh rate ensuring up to 30 million records per month. Companies: Access comprehensive company data including ID, country, industry, size, number of followers, website details, subsidiaries, and posts. Tailored subsets by industry or region provide invaluable insights for CRM enrichment, competitive intelligence, and understanding the startup ecosystem, updated monthly with up to 40 million records. Job Listings: Explore current job opportunities detailed with job titles, company names, locations, and employment specifics such as seniority levels and employment functions. This dataset includes direct application links and real-time application numbers, serving as a crucial tool for job seekers and analysts looking to understand industry trends and the job market dynamics.

Customizable Subsets for Specific Needs Our LinkedIn dataset offers the flexibility to tailor the dataset according to your specific business requirements. Whether you need comprehensive insights across all data points or are focused on specific segments like job listings, company profiles, or individual professional details, we can customize the dataset to match your needs. This modular approach ensures that you get only the data that is most relevant to your objectives, maximizing efficiency and relevance in your strategic applications. Popular Use Cases

Strategic Hiring and Recruiting: Track talent movement, identify growth opportunities, and enhance your recruiting efforts with targeted data. Market Analysis and Competitive Intelligence: Gain a competitive edge by analyzing company growth, industry trends, and strategic opportunities. Lead Generation and CRM Enrichment: Enrich your database with up-to-date company and professional data for targeted marketing and sales strategies. Job Market Insights and Trends: Leverage detailed job listings for a nuanced understanding of employment trends and opportunities, facilitating effective job matching and market analysis. AI-Driven Predictive Analytics: Utilize AI algorithms to analyze large datasets for predicting industry shifts, optimizing business operations, and enhancing decision-making processes based on actionable data insights.

Whether you are mapping out competitive landscapes, sourcing new talent, or analyzing job market trends, our LinkedIn dataset provides the tools you need to succeed. Customize your access to fit specific needs, ensuring that you have the most relevant and timely data at your fingertips.

Analysis of ‘E-Shop Clothing Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/adityawisnugrahas/eshop-clothing-dataset on 28 January 2022.

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

Data description “e-shop clothing 2008”

Variables:

1. YEAR (2008)

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1. MONTH -> from April (4) to August (8)

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1. DAY -> day number of the month

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1. ORDER -> sequence of clicks during one session

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1. COUNTRY -> variable indicating the country of origin of the IP address with the following categories:

1-Australia 2-Austria 3-Belgium 4-British Virgin Islands 5-Cayman Islands 6-Christmas Island 7-Croatia 8-Cyprus 9-Czech Republic 10-Denmark 11-Estonia 12-unidentified 13-Faroe Islands 14-Finland 15-France 16-Germany 17-Greece 18-Hungary 19-Iceland 20-India 21-Ireland 22-Italy 23-Latvia 24-Lithuania 25-Luxembourg 26-Mexico 27-Netherlands 28-Norway 29-Poland 30-Portugal 31-Romania 32-Russia 33-San Marino 34-Slovakia 35-Slovenia 36-Spain 37-Sweden 38-Switzerland 39-Ukraine 40-United Arab Emirates 41-United Kingdom 42-USA 43-biz (.biz) 44-com (.com) 45-int (.int) 46-net (.net) 47-org (*.org)

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1. SESSION ID -> variable indicating session id (short record)

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1. PAGE 1 (MAIN CATEGORY) -> concerns the main product category: 1-trousers 2-skirts 3-blouses 4-sale

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1. PAGE 2 (CLOTHING MODEL) -> contains information about the code for each product (217 products)

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1. COLOUR -> colour of product

1-beige 2-black 3-blue 4-brown 5-burgundy 6-gray 7-green 8-navy blue 9-of many colors 10-olive 11-pink 12-red 13-violet 14-white

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1. LOCATION -> photo location on the page, the screen has been divided into six parts:

1-top left 2-top in the middle 3-top right 4-bottom left 5-bottom in the middle 6-bottom right

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1. MODEL PHOTOGRAPHY -> variable with two categories:

1-en face 2-profile

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1. PRICE -> price in US dollars

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1. PRICE 2 -> variable informing whether the price of a particular product is higher than the average price for the entire product category

1-yes 2-no

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1. PAGE -> page number within the e-store website (from 1 to 5)

++++++++++++++++++++++++++++++++++++++++++++++++++++++++

I want to know how to solve this data regarding any problem (clustering, regression, classification, EDA)

Source: https://archive.ics.uci.edu/ml/datasets/clickstream+data+for+online+shopping

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

The Detroit Land Bank Authority's (DLBA) Own It Now (OIN) program offers vacant homes for sale with a minimum offer price of $1,000. Homes are available on the DLBA website for sale 24 hours a day, 7 days a week and may be offered on at any time. Homes are sold as is and buyers are required to rehab the houses and ensure that they are occupied. The dataset provides information on the properties that have been successfully sold by the DLBA through the OIN program.The Detroit Land Bank Authority (DLBA) works directly with individual buyers, as well as Community Partner organizations and developers to achieve their mission to return the city's blighted and vacant properties to productive use. They utilize a variety of sales programs to make homeownership and land purchases accessible to Detroiters. One of these programs is Own it Now (OIN).Each row in the dataset represents an OIN home and includes data about the sale status, closing date for sold homes, sale price, and location information such as: address; parcel ID; and neighborhood.For more information about the DLBA's sales programs please visit their website.Click here for the Analytics Hub visualization of this dataset.

Selling prices of piglets

This dataset contains information about Saudi Arabia's Total Electricity Sale for 1990-2019. Data is from Electricity and Cogeneration Regulatory Authority. The Electricity and Cogeneration Regulatory Authority (ECRA) website has changed to Water & Electricity Regulatory Authority (WERA).

This dataset was extracted from ECRA’s website and has been discontinued, to access WERA’s datasets please find the following linkFollow datasource.kapsarc.org for timely data to advance energy economics research.