Sales Data Description This dataset represents synthetic sales data generated for practice purposes only. It is not real-time or based on actual business operations, and should be used solely for educational or testing purposes. The dataset contains information that simulates sales transactions across different products, regions, and customers. Each row represents an individual sale event with various details associated with it.

Columns in the Dataset

1. Product_ID: Unique identifier for each product sold. Randomly generated for practice purposes.
2. Sale_Date: The date when the sale occurred. Randomly selected from the year 2023.
3. Sales_Rep: The sales representative responsible for the transaction. The dataset includes five random sales representatives (Alice, Bob, Charlie, David, Eve).
4. Region: The region where the sale took place. The possible regions are North, South, East, and West.
5. Sales_Amount: The total sales amount for the transaction, including discounts if any. Values range from 100 to 10,000 (in currency units).
6. Quantity_Sold: The number of units sold in that transaction, randomly generated between 1 and 50.
7. Product_Category: The category of the product sold. Categories include Electronics, Furniture, Clothing, and Food.
8. Unit_Cost: The cost per unit of the product sold, randomly generated between 50 and 5000 currency units.
9. Unit_Price: The selling price per unit of the product, calculated to be higher than the unit cost.
10. Customer_Type: Indicates whether the customer is a New or Returning customer.
11. Discount: The discount applied to the sale, randomly chosen between 0% and 30%.
12. Payment_Method: The method of payment used by the customer (e.g., Credit Card, Cash, Bank Transfer).
13. Sales_Channel: The channel through which the sale occurred. Either Online or Retail.
14. Region_and_Sales_Rep: A combined column that pairs the region and sales representative for easier tracking.

Disclaimer

Please note: This data was randomly generated and is intended solely for practice, learning, or testing. It does not reflect real-world sales, customers, or businesses, and should not be considered reliable for any real-time analysis or decision-making.

Books Sales and Ratings

Books Dataset: Analyzing Sales, Ratings, and Genres

By Josh Murrey [source]

About this dataset

The Books Dataset: Sales, Ratings, and Publication provides comprehensive information on various aspects of books, including their publishing year, author details, ratings given by readers, sales performance data, and genre classification. The dataset consists of several key columns that capture important attributes related to each book.

The Publishing Year column indicates the year in which each book was published. This information helps in understanding the chronological distribution of books in the dataset.

The Book Name column contains the titles of the books. Each book has a unique name that distinguishes it from others in the dataset.

The Author column specifies the name(s) of the author(s) responsible for creating each book. This information is crucial for understanding different authors' contributions and analyzing their impact on sales and ratings.

The language_code column represents a specific code assigned to indicate the language in which each book is written. This code serves as a reference point for language-based analysis within the dataset.

Each author's rating is captured in the Author_Rating column. This rating is based on their previous works and serves as an indicator of their reputation or acclaim among readers.

The average rating given by readers for each book is recorded in the Book_average_rating column. This value reflects how well-received a particular book is by its audience.

The number of ratings given to each book by readers can be found in the Book_ratings_count column. This metric helps gauge reader engagement and provides insights into popular or widely-discussed books within this dataset.

Books are classified into different genres or categories which are mentioned under the genre column. Genre classification allows for analyzing trends across specific literary genres or identifying patterns related to certain types of books.

Sales-related data includes both gross sales revenue (gross sales) generated by each book and publisher revenue (publisher revenue) earned from these sales transactions. These numeric values provide insights into financial performance aspects associated with the book market.

The sale price column denotes the specific price at which each book is sold. This information helps evaluate pricing strategies and their potential impact on sales figures.

Sales performance is further quantified through the sales rank column, which assigns a numerical rank to each book based on its sales performance. This ranking system aids in identifying high-performing books within the dataset.

Lastly, the units sold column captures the number of units of each book that have been sold. This data highlights popular books based on reader demand and serves as a crucial measure of commercial success within the dataset.

Overall, this expansive and comprehensive Books Dataset

How to use the dataset

Introduction:

• Getting Familiar with the Columns: The dataset contains multiple columns that provide different kinds of information:

• Book Name: The title of each book.

• Author: The name of the author who wrote the book.

• language_code: The code representing the language in which the book is written.

• Author_Rating: The rating assigned to the author based on their previous works.

• Book_average_rating: The average rating given to the book by readers.

• Book_ratings_count: The number of ratings given to the book by readers.

• genre: The genre or category to which the book belongs.

• gross sales: The total sales revenue generated by each book.

• publisher revenue: The revenue earned by publishers from selling each book.

• sale price: The price at which each copy of a book is sold.

• sales rank: A numeric value indicating a book's rank based on its sales performance in comparison to other books within its category (genre).

• units sold : Total number of copies sold for each specific title.

• Understanding Numeric and Textual Data: Numeric columns in this dataset include Publishing Year, Author_Rating, Book_average_rating, Book_ratings_count,gross sales,publisher revenue,sale price,sales rank and units sold; these provide quantitative insights that can be used for statistical analysis and comparisons.

Additionally,the columns 'Author','Book Name',and 'genre' contain textual data that provides descriptive elements such as authors' names and categorization genres.

• Exploring Relationships Between Data Points: By combining different co...

1.Introduction

Sales data collection is a crucial aspect of any manufacturing industry as it provides valuable insights about the performance of products, customer behaviour, and market trends. By gathering and analysing this data, manufacturers can make informed decisions about product development, pricing, and marketing strategies in Internet of Things (IoT) business environments like the dairy supply chain.

One of the most important benefits of the sales data collection process is that it allows manufacturers to identify their most successful products and target their efforts towards those areas. For example, if a manufacturer could notice that a particular product is selling well in a certain region, this information could be utilised to develop new products, optimise the supply chain or improve existing ones to meet the changing needs of customers.

This dataset includes information about 7 of MEVGAL’s products [1]. According to the above information the data published will help researchers to understand the dynamics of the dairy market and its consumption patterns, which is creating the fertile ground for synergies between academia and industry and eventually help the industry in making informed decisions regarding product development, pricing and market strategies in the IoT playground. The use of this dataset could also aim to understand the impact of various external factors on the dairy market such as the economic, environmental, and technological factors. It could help in understanding the current state of the dairy industry and identifying potential opportunities for growth and development.

2. Citation

Please cite the following papers when using this dataset:

1. I. Siniosoglou, K. Xouveroudis, V. Argyriou, T. Lagkas, S. K. Goudos, K. E. Psannis and P. Sarigiannidis, "Evaluating the Effect of Volatile Federated Timeseries on Modern DNNs: Attention over Long/Short Memory," in the 12th International Conference on Circuits and Systems Technologies (MOCAST 2023), April 2023, Accepted

3. Dataset Modalities

The dataset includes data regarding the daily sales of a series of dairy product codes offered by MEVGAL. In particular, the dataset includes information gathered by the logistics division and agencies within the industrial infrastructures overseeing the production of each product code. The products included in this dataset represent the daily sales and logistics of a variety of yogurt-based stock. Each of the different files include the logistics for that product on a daily basis for three years, from 2020 to 2022.

3.1 Data Collection

The process of building this dataset involves several steps to ensure that the data is accurate, comprehensive and relevant.

The first step is to determine the specific data that is needed to support the business objectives of the industry, i.e., in this publication’s case the daily sales data.

Once the data requirements have been identified, the next step is to implement an effective sales data collection method. In MEVGAL’s case this is conducted through direct communication and reports generated each day by representatives & selling points.

It is also important for MEVGAL to ensure that the data collection process conducted is in an ethical and compliant manner, adhering to data privacy laws and regulation. The industry also has a data management plan in place to ensure that the data is securely stored and protected from unauthorised access.

The published dataset is consisted of 13 features providing information about the date and the number of products that have been sold. Finally, the dataset was anonymised in consideration to the privacy requirement of the data owner (MEVGAL).

File	Period	Number of Samples (days)
product 1 2020.xlsx	01/01/2020–31/12/2020	363
product 1 2021.xlsx	01/01/2021–31/12/2021	364
product 1 2022.xlsx	01/01/2022–31/12/2022	365
product 2 2020.xlsx	01/01/2020–31/12/2020	363
product 2 2021.xlsx	01/01/2021–31/12/2021	364
product 2 2022.xlsx	01/01/2022–31/12/2022	365
product 3 2020.xlsx	01/01/2020–31/12/2020	363
product 3 2021.xlsx	01/01/2021–31/12/2021	364
product 3 2022.xlsx	01/01/2022–31/12/2022	365
product 4 2020.xlsx	01/01/2020–31/12/2020	363
product 4 2021.xlsx	01/01/2021–31/12/2021	364
product 4 2022.xlsx	01/01/2022–31/12/2022	364
product 5 2020.xlsx	01/01/2020–31/12/2020	363
product 5 2021.xlsx	01/01/2021–31/12/2021	364
product 5 2022.xlsx	01/01/2022–31/12/2022	365
product 6 2020.xlsx	01/01/2020–31/12/2020	362
product 6 2021.xlsx	01/01/2021–31/12/2021	364
product 6 2022.xlsx	01/01/2022–31/12/2022	365
product 7 2020.xlsx	01/01/2020–31/12/2020	362
product 7 2021.xlsx	01/01/2021–31/12/2021	364
product 7 2022.xlsx	01/01/2022–31/12/2022	365

3.2 Dataset Overview

The following table enumerates and explains the features included across all of the included files.

Feature	Description	Unit
Day	day of the month	-
Month	Month	-
Year	Year	-
daily_unit_sales	Daily sales - the amount of products, measured in units, that during that specific day were sold	units
previous_year_daily_unit_sales	Previous Year’s sales - the amount of products, measured in units, that during that specific day were sold the previous year	units
percentage_difference_daily_unit_sales	The percentage difference between the two above values	%
daily_unit_sales_kg	The amount of products, measured in kilograms, that during that specific day were sold	kg
previous_year_daily_unit_sales_kg	Previous Year’s sales - the amount of products, measured in kilograms, that during that specific day were sold, the previous year	kg
percentage_difference_daily_unit_sales_kg	The percentage difference between the two above values	kg
daily_unit_returns_kg	The percentage of the products that were shipped to selling points and were returned	%
previous_year_daily_unit_returns_kg	The percentage of the products that were shipped to

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.

Use our Best Buy products to collect ratings, prices, and descriptions about products from an e-commerce online web. You can purchase either the entire dataset or a customized subset, depending on your requirements. The Best Buy Products Dataset stands as a comprehensive resource for businesses, researchers, and analysts aiming to navigate the vast array of products offered by Best Buy, a leading retailer in consumer electronics and technology. Tailored to provide a deep understanding of Best Buy's e-commerce ecosystem, this dataset facilitates market analysis, pricing optimization, customer behavior comprehension, and competitor assessment. At its core, the dataset encompasses essential attributes such as product ID, title, descriptions, ratings, reviews, pricing details, and seller information. These fundamental data elements empower users to glean insights into product performance, customer sentiment, and seller credibility, thereby facilitating informed decision-making processes. Whether you're a retailer looking to enhance your product portfolio, a researcher investigating trends in consumer electronics, or an analyst seeking to refine e-commerce strategies, the Best Buy Products Dataset offers a valuable resource for uncovering opportunities and driving success in the ever-evolving landscape of retail.

Context

A 11.11 sale was going on Daraz within all categories. This dataset contains some product data from all categories such as health & beauty, men's & boys' fashion, groceries and others. Each product data carries information like product title, original price, discount, seller name and some more .

• The scraper program for data collection can be found at Daraz 11.11 Top Selling Product Data Analysis git repo.
• The Tableau visualization is available at 11.11 sale data analysis.

Dataset Containings

File Name: [All the .csv files within categorical_data folder]

• Category: Category of the product.
• SubCategory: Link to the relatable sub-category page.
• Title: The given name of the product given by seller.
• Original Price: Price before 11.11 sale.
• Discount Price: Price running on 11.11 sale.
• Discount: The discount(%) offered by the seller.
• Seller Name: The shop name selling the product.
• Number of Ratings: The total number of ratings given for the product.
• Positive Seller Ratings: Seller’s positive ratings percentage.
• Ship On Time: Seller’s on time shipping percentage.
• Chat Response Rate: Seller’s message reply percentage.
• Delivery Type: Whether the product is a “Free Delivery” product or “Standard Delivery” one?
• Flagship Store: Whether the shop is a flagship store or not? Yes/ No
• Cash On Delivery: Whether cash on delivery option is available or not?

File Name: subategories.csv

• Category: Category Name
• SubCategory Name: The subcategory name.
• SubCategory Link: Url of the subccategory page

File Name: Top_Selling_Product_Data.csv

• Category: Category of the product.
• SubCategory: Subcategory of the product within the category.
• Title: The given name of the product given by seller.
• Original Price: Price before 11.11 sale.
• Discount Price: Price running on 11.11 sale.
• Discount: The discount(%) offered by the seller.
• Seller Name: The shop name selling the product.
• Number of Ratings: The total number of ratings given for the product.
• Positive Seller Ratings: Seller’s positive ratings percentage.
• Ship On Time: Seller’s on time shipping percentage.
• Chat Response Rate: Seller’s message reply percentage.
• Delivery Type: Whether the product is a “Free Delivery” product or “Standard Delivery” one?
• Flagship Store: Whether the shop is a flagship store or not? Yes/ No
• No. of products to be sold: Total products to be sold in discount price to reach a break-even point equal to the sell of 50 products in original price. (Hypothetical Situation)
• Sell percentage to increase: Sell percentage to increase than the normal sale to reach the break-even point. (Hypothetical Situation)

Acknowledgements

The website daraz was used to scrape the dataset. If you use the data research purpose, don't forget add a citation.

Inspiration

This dataset can be used for traditional machine learning based project and also natural language processing workings.

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.

Personal Location Data Market

Data From: "There’s a Multibillion-Dollar Market for Your Phone’s Location Data"

Original source. Author: The Markup

About this dataset

There’s a multibillion-dollar market for your phone’s location data. We surveyed 100 companies to find out who they are, what they do with your data, and whether they follow best practices.

Your phone’s location is constantly being tracked and collected by hundreds of companies, many of which are unknown to you. This data is valuable—and it’s being bought and sold in a thriving industry with little regulation.

The Markup surveyed 100 companies that collect or sell location data to get a better understanding of this industry and what it means for your privacy. We asked these companies about their policies and practices around collecting, using, and selling location data. We also reviewed their public statements and website disclosures related to privacy.

What we found was an industry that lacks transparency and accountability, with few companies following best practices around protecting the privacy of their users’ data. In many cases, these companies are collecting more data than they need, retaining it for longer than necessary, sharing it with third parties without user consent, or failing to secure it properly—putting users at risk of identity theft, fraud, or other harms.

If you care about your privacy, you should know who has access to your location data—and what they’re doing with it. This dataset contains information on the 100 companies we surveyed so that you can make informed choices about which ones to trust with your personal data

How to use the dataset

This dataset contains information on companies that collect and sell location data. The data includes the company name, website, logo, narrative, company response, privacy email, privacy policy, and whether or not the company is a California-licensed data broker

Research Ideas

• To study how location data is collected and sold
• To understand the business model of location data companies
• To learn about the privacy policies of these companies

Acknowledgements

This dataset was compiled and analyzed by The Markup. The Markup is a nonprofit newsroom that investigates how powerful institutions impact our lives

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: location-data-companies.csv | Column name | Description | |:-------------------|:--------------------------------------------------------------------| | name | The name of the company. (String) | | website | The company's website. (String) | | logo | The company's logo. (String) | | narrative | A description of the company. (String) | | privacy_email | The company's privacy email address. (String) | | privacy_policy | The company's privacy policy. (String) | | CA_broker | Whether the company is a California-licensed data broker. (Boolean) |

(i)The count and turnover (£'000s) of VAT and/or PAYE based enterprises in the UK in UK SIC Class 4751 -retail sale of textiles and Class 4782 - retail sale via stalls and markets of textiles, clothing and footwear. (ii) A group of legal units under common ownership is called an Enterprise Group. An Enterprise is the smallest combination of legal units (generally based on VAT and/or PAYE records) which has a certain degree of autonomy within an Enterprise Group. An individual site (for example a factory or shop) in an enterprise is called a local unit. Turnover provided to the ONS for the majority of traders is based on VAT returns for a 12 month period. For 2020 this relates to a 12 month period covering the financial year 2018/2019. Turnover data should be used with caution as they are derived mainly from administrative sources which ONS is unable to validate. (iii) UK (iv) Information on the methods used by ONS https://www.ons.gov.uk/methodology

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.

Access our extensive Facebook datasets that provide detailed information on public posts, pages, and user engagement. Gain insights into post performance, audience interactions, page details, and content trends with our ethically sourced data. Free samples are available for evaluation. Over 940M records available Price starts at $250/100K records Data formats are available in JSON, NDJSON, CSV, XLSX and Parquet. 100% ethical and compliant data collection Included datapoints:

Post ID Post Content & URL Date Posted Hashtags Number of Comments Number of Shares Likes & Reaction Counts (by type) Video View Count Page Name & Category Page Followers & Likes Page Verification Status Page Website & Contact Info Is Sponsored Post Attachments (Images/Videos) External Link Data And much more

About the Dataset:

Amazon is one of the biggest online retailers in the UK. With this dataset, you can get an in-depth idea of what products sell best, which SEO titles generate the most sales, the best price range for a product in a given category, and much more.

It took a lot of time and energy to prepare this original dataset, so don't forget to hit the upvote button! 😊💝

Similar Datasets:

Amazon Canada Products

Amazon India Products

Amazon USA Products

Interesting Task Ideas:

1. Uncover trending product categories and their sales performance.
2. Analyze customer ratings to find top-rated products.
3. Train a product title generator that can generate sales-worthy product titles based on products with the most sales.
4. Gain insight into the best price for any given product based on sales data and competition.
5. Identify which niches are the easiest to make sales in.
6. Gain insights into the general spending habits of online shoppers.
7. Use as a seed dataset for practicing database management and performance optimization.
8. Train (or learn to train) an AI-based search model to recommend Amazon products.

Checkout my other datasets

USA Unemployment Rates by Demographics & Race

USA Hispanic-White Wage Gap Dataset

Median and Avg Hourly Wages in the USA

Health Insurance Coverage in the USA

Black-White Wage Gap in the USA Dataset

Photo by Canonicalized

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

This child item describes R code used to determine whether public-supply water systems buy water, sell water, both buy and sell water, or are neutral (meaning the system has only local water supplies) using water source information from a proprietary dataset from the U.S. Environmental Protection Agency. This information was needed to better understand public-supply water use and where water buying and selling were likely to occur. Buying or selling of water may result in per capita rates that are not representative of the population within the water service area. This dataset is part of a larger data release using machine learning to predict public supply water use for 12-digit hydrologic units from 2000-2020. Output from this code was used as an input feature variable in the public supply water use machine learning model. This page includes the following files: ID_WSA_04062022_Buyers_Sellers_DR.R - an R script used to determine whether a public-supply water service area buys water, sells water, or is neutral BuySell_readme.txt - a README text file describing the script

Success.ai’s Company Data Solutions provide businesses with powerful, enterprise-ready B2B company datasets, enabling you to unlock insights on over 28 million verified company profiles. Our solution is ideal for organizations seeking accurate and detailed B2B contact data, whether you’re targeting large enterprises, mid-sized businesses, or small business contact data.

Success.ai offers B2B marketing data across industries and geographies, tailored to fit your specific business needs. With our white-glove service, you’ll receive curated, ready-to-use company datasets without the hassle of managing data platforms yourself. Whether you’re looking for UK B2B data or global datasets, Success.ai ensures a seamless experience with the most accurate and up-to-date information in the market.

Why Choose Success.ai’s Company Data Solution? At Success.ai, we prioritize quality and relevancy. Every company profile is AI-validated for a 99% accuracy rate and manually reviewed to ensure you're accessing actionable and GDPR-compliant data. Our price match guarantee ensures you receive the best deal on the market, while our white-glove service provides personalized assistance in sourcing and delivering the data you need.

Why Choose Success.ai?

• Best Price Guarantee: We offer industry-leading pricing and beat any competitor.
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• Comprehensive Data: Over 15 data points, including company size, industry, funding, and technologies used.
• Accurate & Verified: AI-validated with a 99% accuracy rate, ensuring high-quality data.
• Real-Time Updates: Stay ahead with continuously updated company information.
• Ethically Sourced Data: Our B2B data is compliant with global privacy laws, ensuring responsible use.
• Dedicated Service: Receive personalized, curated data without the hassle of managing platforms.
• Tailored Solutions: Custom datasets are built to fit your unique business needs and industries.

Our database spans 195 countries and covers 28 million public and private company profiles, with detailed insights into each company’s structure, size, funding history, and key technologies. We provide B2B company data for businesses of all sizes, from small business contact data to large corporations, with extensive coverage in regions such as North America, Europe, Asia-Pacific, and Latin America.

Comprehensive Data Points: Success.ai delivers in-depth information on each company, with over 15 data points, including:

Company Name: Get the full legal name of the company. LinkedIn URL: Direct link to the company's LinkedIn profile. Company Domain: Website URL for more detailed research. Company Description: Overview of the company’s services and products. Company Location: Geographic location down to the city, state, and country. Company Industry: The sector or industry the company operates in. Employee Count: Number of employees to help identify company size. Technologies Used: Insights into key technologies employed by the company, valuable for tech-based outreach. Funding Information: Track total funding and the most recent funding dates for investment opportunities. Maximize Your Sales Potential: With Success.ai’s B2B contact data and company datasets, sales teams can build tailored lists of target accounts, identify decision-makers, and access real-time company intelligence. Our curated datasets ensure you’re always focused on high-value leads—those who are most likely to convert into clients. Whether you’re conducting account-based marketing (ABM), expanding your sales pipeline, or looking to improve your lead generation strategies, Success.ai offers the resources you need to scale your business efficiently.

Tailored for Your Industry: Success.ai serves multiple industries, including technology, healthcare, finance, manufacturing, and more. Our B2B marketing data solutions are particularly valuable for businesses looking to reach professionals in key sectors. You’ll also have access to small business contact data, perfect for reaching new markets or uncovering high-growth startups.

From UK B2B data to contacts across Europe and Asia, our datasets provide global coverage to expand your business reach and identify new markets. With continuous data updates, Success.ai ensures you’re always working with the freshest information.

Key Use Cases:

• Targeted Lead Generation: Build accurate lead lists by filtering data by company size, industry, or location. Target decision-makers in key industries to streamline your B2B sales outreach.
• Account-Based Marketing (ABM): Use B2B company data to personalize marketing campaigns, focusing on high-value accounts and improving conversion rates.
• Investment Research: Track company growth, funding rounds, and employee trends to identify investment opportunities or potential M&A targets.
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According to Cognitive Market Research, the global Data Broker Services market size is USD 268154.2 million in 2024 and will expand at a compound annual growth rate (CAGR) of 8.00% from 2024 to 2031.

North America held the major market of more than 40% of the global revenue with a market size of USD 107261.68 million in 2024 and will grow at a compound annual growth rate (CAGR) of 6.2% from 2024 to 2031.
Europe accounted for a share of over 30% of the global market size of USD 80446.26 million.
Asia Pacific held the market of around 23% of the global revenue with a market size of USD 61675.47 million in 2024 and will grow at a compound annual growth rate (CAGR) of 10.0% from 2024 to 2031.
Latin America market of more than 5% of the global revenue with a market size of USD 13407.71 million in 2024 and will grow at a compound annual growth rate (CAGR) of 7.4% from 2024 to 2031.
Middle East and Africa held the major market ofaround 2% of the global revenue with a market size of USD 5363.08 million in 2024 and will grow at a compound annual growth rate (CAGR) of 7.7% from 2024 to 2031.
The Subscription Paid held the highest Data Broker Services market revenue share in 2024.

Market Dynamics of Data Broker Services Market

Key Drivers of Data Broker Services Market

Increasing Demand for Personalized Marketing Solutions to boost the demand globally

The Data Broker Services Market is being driven by the increasing demand for personalized marketing solutions. Companies across various industries are leveraging data broker services to access valuable consumer insights and enhance their marketing strategies. Data brokers offer a wide range of data sets, including demographic, behavioral, and transactional data, which can be used to create targeted marketing campaigns. By utilizing data broker services, companies can tailor their marketing messages to specific consumer segments, leading to higher engagement and conversion rates. This trend is expected to continue driving the growth of the Data Broker Services Market as businesses increasingly prioritize personalized marketing approaches to remain competitive in the digital age.

Growing Focus on Data Monetization to Propel Market Growth

Another key driver of the Data Broker Services Market is the growing focus on data monetization. Organizations are realizing the value of their data assets and are looking for ways to monetize them. Data broker services enable companies to sell their data to third parties, such as marketers, researchers, and other businesses, generating additional revenue streams. This trend is particularly prevalent in industries with large amounts of consumer data, such as retail, finance, and healthcare. By monetizing their data, companies can unlock new revenue opportunities and offset the costs associated with data collection and management. As the demand for data-driven insights continues to grow, the Data Broker Services Market is expected to expand, driven by the increasing number of organizations looking to capitalize on their data assets.

Restraint Factors Of Data Broker Services Market

Regulatory Challenges and Data Privacy Concerns to Limit the Sales

One of the key restraints in the Data Broker Services Market is the increasing regulatory challenges and data privacy concerns. With the implementation of regulations such as the GDPR in Europe and the CCPA in California, data brokers are facing stricter requirements for data collection, processing, and sharing. Compliance with these regulations requires significant resources and can limit the ability of data brokers to collect and monetize data. Additionally, concerns about data privacy and security among consumers are leading to greater scrutiny of data broker practices, further complicating the operating environment for these companies. As regulatory pressures continue to increase, data brokers may face challenges in expanding their operations and maintaining profitability.

Opportunity for the Data Broker Services Market

The Data Broker Service Market is poised to benefit significantly from the integration of blockchain technology.

By leveraging blockchain's decentralized and immutable nature, data brokers can ensure tamper-proof data exchange, enable secure data sharing, and provide auditable trails. This can increase trust and confidence in data exchange, driving growth in the data broker...

According to Connecticut law, no manufacturer, wholesaler, or out-of-state shipper may ship, transport or deliver within Connecticut, or sell or offer for sale, any alcoholic liquor unless the following information is registered with, and approved by, the Connecticut Department of Consumer Protection: The name of the brand, trade name or other distinctive characteristics by which the alcoholic liquors are bought and sold, the name and address of the manufacturer, and the name and address of each wholesaler permittee who is authorized by the manufacturer or his authorized representative to sell such alcoholic liquors. Brand registration is valid for three (3) years. The registration and subsequent renewal fees are payable by the manufacturer or his authorized representative when such liquors are manufactured in the United States and by the importer or his authorized representative when such liquors are imported into the country. No manufacturer, wholesaler, or out-of-state shipper may discriminate in price discounts between one permittee and another on sales or purchases of alcoholic liquors bearing the same brand or trade name and of like age, size and quality, nor shall he allow in any form any discount, rebate, free goods, allowance or other inducement for the purpose of making sales or purchases.

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This data table shows the percentage of tobacco retailer stores that sell electronic smoking devices (including e-cigarettes, other vapor devices or e-liquids) in 2013 and 2016 by county. Data for three city health departments (Berkeley, Long Beach and Pasadena) were analyzed separately, results for Alameda County include the city of Berkeley and results for Los Angeles County include the cities of Pasadena and Long Beach. Results were suppressed for items with a small sample size (n =< 5) and for results considered unreliable (coefficient of variation greater than or equal to 0.5). Cities or counties that conducted a census of tobacco retailers will not have a confidence interval due to the survey methodology.

The Healthy Stores for a Healthy Community (HSHC) marketing survey measured the availability of a range of unhealthy and healthy products, as well as marketing practices for tobacco, alcohol, food and beverage items, and condoms. The California Tobacco Control Program (CTCP) invited partners in the Nutrition Education and Obesity Prevention Branch at the California Department of Public Health (CDPH), the Substance Use Disorders Program at the California Department of Health Care Services (DHCS), and the Sexually Transmitted Diseases Control Branch at CDPH to join the campaign and look at the retail environment from a more comprehensive perspective, as there were many local and state efforts examining one or more of these health issues in community stores. This collaboration is part of the state’s continued effort to address the burden of chronic disease and to better understand the role that stores could play in making communities healthier. In 2013, the 61 local lead agencies (LLAs) completed the HSHC survey in a total of 7,393 randomly selected stores that sell tobacco throughout the state of California. In 2016, the LLAs completed a follow-up survey in 7,152 randomly selected stores that sell tobacco statewide.