We Never Sell Your Personally Identifiable Information Without Your Permission!
Prospect Wallet does “sell” personal information, but only with specific consent, under the CCPA’s broad definition of “sell,” which encompasses even the ordinary flow of data in the digital analytics and advertising ecosystem. Prospect Wallet, like most businesses that run websites and applications, employs online analytics to track how people interact with them

The awareness among worldwide consumers about companies selling their personal data to third parties has grown in recent years. As of July 2022, three in four consumers in selected countries worldwide said they knew that companies sell personal information. In comparison, in 2020, this share was a little over 60 percent.

This dataset is about books. It has 3 rows and is filtered where the book is Buying and selling a house. It features 7 columns including author, publication date, language, and book publisher.

This fact sheet will help business owners create promotional information about their products or services in the format of a one-page “sell sheet.” Business owners will learn how to define their target audience, convey the key benefits they are selling and create an effective sell sheet.

Sell Products

## Overview

Sell Products is a dataset for object detection tasks - it contains Item annotations for 8,988 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This dataset is about books. It has 6 rows and is filtered where the book is Sell your way to success. It features 7 columns including author, publication date, language, and book publisher.

Goals :

1. Sales Analysis:

Sales data forms the backbone of this dataset, and it allows users to delve into various aspects of sales performance.

2. Product Analysis:

Each product in this dataset comes with its unique identifier (StockCode) and its name (Description).

3. Customer Segmentation:

If you associated specific business logic onto the transactions (such as calculating total amounts), then you could use standard machine learning methods or even RFM (Recency, Frequency, Monetary) segmentation techniques combining it with 'CustomerID' for your customer base to understand customer behavior better.

4. Geographical Analysis:

The Country column enables analysts to study purchase patterns across different geographical locations.

5. Sales Performance Dashboard:

To track the sales performance of the online retail company, a sales performance dashboard can be created. This dashboard can include key metrics such as total sales, sales by product category, sales by customer segment, and sales by geographical location. By visualizing the sales data in an interactive dashboard, it becomes easier to identify trends, patterns, and areas for improvement.

Research Ideas ****:

1. Inventory Management: By analyzing the quantity and frequency of product sales, retailers can effectively manage their stock and predict future demand. This would help ensure that popular items are always available while less popular items aren't overstocked.
2. Customer Segmentation: Data from different countries can be used to understand buying habits across different geographical locations. This will allow the retail company to tailor its marketing strategy for each specific region or country, leading to more effective advertising campaigns.
3. Sales Trend Analysis: With data spanning almost a year, temporal patterns in purchasing behavior can be identified, including seasonality and other trends (like an increase in sales during holidays). Techniques like time-series analysis could provide insights into peak shopping times or days of the week when sales are typically high.
4. Predictive Analysis for Cross-Selling & Upselling: Based on a customer's previous purchase history, predictive algorithms can be utilized to suggest related products that might interest the customer, enhancing upsell and cross-sell opportunities.
5. Detecting Fraud: Analysing sale returns (marked with 'c' in InvoiceNo) across customers or regions could help pinpoint fraudulent activities or operational issues leading to those returns
6. RFM Analysis: By using the RFM (Recency, Frequency, Monetary) segmentation technique, the online retail company can gain insights into customer behavior and tailor their marketing strategies accordingly.

**************Steps :**************

1. Data manipulation and cleaning from raw data using SQL language Google Big Query
2. Data filtering, grouping, and slicing
3. Data Visualization using Tableau
4. Data visualization analysis and result

This statistic shows results of the UPS & Statista expert survey "European eCommerce Monitor 2020". Some ** percent of respondents stated that their company sells through an own website.

Templates to help you compile relevant documentation when closing, selling or leaving your business. Documents may be adapted to match your business needs and branding.

This dataset is about books. It has 1 row and is filtered where the book is Loving Monday : succeeding in business without selling your soul. It features 7 columns including author, publication date, language, and book publisher.

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

Sell Products 2

## Overview

Sell Products 2 is a dataset for object detection tasks - it contains Item annotations for 4,145 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The global Internet Business Buy and Sell market size was valued at USD 24,869 million in 2025 and is projected to reach USD 77,950 million by 2033, exhibiting a CAGR of 15.3% during the forecast period (2025-2033). The growth of the market can be attributed to the increasing popularity of online business transactions and the growing number of startups and small businesses entering the market. Online business buying and selling platforms provide a convenient and efficient way for businesses to connect with buyers and sellers, which has led to its widespread adoption by both individuals and organizations. The internet business buy and sell market is expected to witness significant growth in the coming years due to several factors. One of the key drivers of growth is the increasing adoption of e-commerce by businesses of all sizes. As more businesses move their operations online, the demand for platforms that facilitate the buying and selling of businesses is likely to increase. Additionally, the growing popularity of online marketplaces and the increasing number of startups and small businesses entering the market are also expected to contribute to the growth of the internet business buy and sell market.

EU Countries with the Highest Share of Enterprises B2C Selling via a Website or Apps, 2016 Discover more data with ReportLinker!

This dataset provides a comprehensive overview of online sales transactions across different product categories. Each row represents a single transaction with detailed information such as the order ID, date, category, product name, quantity sold, unit price, total price, region, and payment method.

Columns:

• Order ID: Unique identifier for each sales order.
• Date:Date of the sales transaction.
• Category:Broad category of the product sold (e.g., Electronics, Home Appliances, Clothing, Books, Beauty Products, Sports).
• Product Name:Specific name or model of the product sold.
• Quantity:Number of units of the product sold in the transaction.
• Unit Price:Price of one unit of the product.
• Total Price: Total revenue generated from the sales transaction (Quantity * Unit Price).
• Region:Geographic region where the transaction occurred (e.g., North America, Europe, Asia).
• Payment Method: Method used for payment (e.g., Credit Card, PayPal, Debit Card).

Insights:

• 1. Analyze sales trends over time to identify seasonal patterns or growth opportunities.
• 2. Explore the popularity of different product categories across regions.
• 3. Investigate the impact of payment methods on sales volume or revenue.
• 4. Identify top-selling products within each category to optimize inventory and marketing strategies.
• 5. Evaluate the performance of specific products or categories in different regions to tailor marketing campaigns accordingly.

Direct-selling companies retail a range of products from one person to another away from a fixed retail location. The COVID-19 outbreak caused a substantial shift in the industry, as mass layoffs propelled industry participation levels, resulting in heightened performance. However, intense competition from big-box retailers and e-commerce has pressured the industry, as competitors can offer a wider selection of substitute products at lower prices and in a convenient one-stop location. Direct sellers have embraced innovative sales strategies and digital platforms to maintain growth. Direct selling revenue is expected to climb at a CAGR of 5.0% to $75.2 billion through the end of 2025, including growth of 2.3% in 2025 alone. Profit will also improve as rising per capita disposable income levels improve spending on high-priced goods. Direct-selling companies have relatively low start-up costs and some unemployed or underemployed Americans establish direct-selling businesses as a means of income. As the unemployment rate fluctuated but ultimately climbed in recent years, more enterprises entered the industry. As demand and direct sellers' revenue rose, more businesses entered the industry to use it as a flexible, low-commitment way to earn supplemental income. The health and wellness segment has boomed, with consumers seeking natural and sustainable products. This shift has fueled sales of nutritional supplements and skincare products. Direct sellers have harnessed social media to reach wider audiences, creating personal connections that resonate with consumers. Positive economic trends, like rising consumer confidence and spending, will contribute to rising revenue for direct-selling companies in the coming years. However, rising incomes and consumer spending will also lead many consumers to shop at substitute industries, like mass retailers and online competitors. As e-commerce continues to expand, direct sellers will further integrate digital tools and platforms to enhance customer engagement and streamline sales processes. Artificial intelligence and data analytics will enable companies to fine-tune marketing strategies, personalize shopping experiences and optimize inventory management. Sustainability will continue to be a critical focus, with consumers demanding greater transparency and environmentally friendly practices. Regulatory scrutiny remains a wildcard, as the industry must navigate potential challenges to ensure ethical practices and the protection of both consumers and sellers. Revenue is expected to expand at a CAGR of 3.0% to $87.0 billion through the end of 2030.

Internet Business Buy And Sell Market Outlook

The global market size for Internet Business Buy And Sell was valued at approximately USD 6.3 billion in 2023 and is expected to reach around USD 12.1 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.5%. This growth is spurred by factors such as the increasing digitization of businesses, enhanced access to capital for online ventures, and the escalating need for diversification in investment portfolios.

The primary growth factor driving the Internet Business Buy And Sell market is the surge in digital transformation across various sectors. Businesses, regardless of their size, are increasingly recognizing the importance of having an online presence. This shift is not just confined to established enterprises but is also impacting small and medium-sized businesses, which are becoming lucrative acquisition targets. The continuous growth of e-commerce, Software as a Service (SaaS) platforms, and digital content websites is further propelling the market. The ease of scaling these businesses, combined with the potential for high returns on investment, makes them attractive to buyers.

Another critical factor contributing to the market's growth is the increased access to capital. With the rise of venture capital firms, private equity, and crowdfunding platforms, there is more funding available than ever before for acquiring and scaling online businesses. This influx of capital has lowered the barriers to entry, enabling more individuals and companies to participate in the buying and selling of digital enterprises. The financial backing also aids in the growth and development of acquired businesses, making them more profitable and thus more attractive to potential buyers.

The diversification of investment portfolios is another pivotal factor driving market growth. Investors are increasingly looking to diversify their portfolios beyond traditional assets like stocks and real estate. Online businesses offer a unique opportunity for diversification due to their potential for high returns and relatively low entry costs. This trend is particularly noticeable among investment firms and high-net-worth individuals who are seeking to capitalize on the growing digital economy. The availability of various types of online businesses, such as e-commerce, SaaS, and affiliate websites, provides ample opportunities for investors to diversify their holdings.

Regionally, North America holds a significant share of the Internet Business Buy And Sell market, primarily due to the high concentration of digital enterprises and advanced technological infrastructure. However, Asia Pacific is expected to exhibit the highest growth rate during the forecast period. This growth is attributed to the rapid digitalization, increasing internet penetration, and a burgeoning middle class with disposable income. European markets are also showing steady growth, driven by favorable regulatory frameworks and increasing entrepreneurial activities.

Business Type Analysis

The Internet Business Buy And Sell market is segmented by business type into E-commerce, SaaS, Content Websites, Affiliate Websites, and Others. E-commerce businesses have been one of the most sought-after categories due to their high scalability and profitability. These businesses typically involve online stores selling physical or digital products directly to consumers. The consumer shift towards online shopping, accelerated by the COVID-19 pandemic, has made e-commerce businesses highly attractive for acquisition. The diversity of products and niches within e-commerce adds another layer of appeal, allowing buyers to find opportunities that align with their expertise and interests.

SaaS businesses represent another lucrative segment. These companies provide software solutions over the internet, eliminating the need for physical distribution and offering a recurring revenue model. The scalability of SaaS businesses, combined with their subscription-based revenue models, makes them highly attractive to buyers. The ongoing demand for software solutions across various industries ensures a steady stream of potential clients, making SaaS businesses a stable and profitable investment.

Content websites, which include blogs, news portals, and informational sites, also form a significant part of the market. These websites generate revenue through advertising, sponsored content, and affiliate marketing. The increasing consumption of online content, driven by the proliferation of mobile devices and high-speed internet, has boosted the value of c

A survey conducted online in the United Kingdom (UK) in 2020 revealed that over ** percent of 18 to 24 year olds would be willing to share their personal data with companies in return for payment. Conversely, only slightly more than ** percent of those over 65 years of age said they would do the same. As a whole, ** percent of UK respondents were against the idea of sharing personal data for financial compensation.

This dataset is about book subjects. It has 3 rows and is filtered where the books is Buying and selling a home : includes buying a home in France. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

United States CSI: Home Selling Conditions: Bad Time to Sell data was reported at 21.000 % in May 2018. This records a decrease from the previous number of 25.000 % for Apr 2018. United States CSI: Home Selling Conditions: Bad Time to Sell data is updated monthly, averaging 41.000 % from Nov 1992 (Median) to May 2018, with 307 observations. The data reached an all-time high of 96.000 % in Mar 2009 and a record low of 17.000 % in May 1999. United States CSI: Home Selling Conditions: Bad Time to Sell data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H036: Consumer Sentiment Index: Home Buying and Selling Conditions. The question was: Generally speaking, do you think now is a good time or a bad time to sell a house?