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.

Although a majority of internet users aged between 18 and 75 years in the United Kingdom (UK) are still skeptical when it comes to personal data being collected by companies, a small share (** percent) would be willing to share this data in return for financial compensation. These types of data mainly included purchase history and location data, while a slightly smaller percentage stated they were willing to sell their browsing history and online media consumption to companies.

According to an analysis conducted in 2023 of over *** companies targeting children and families in the United States, only ** percent of the businesses had a privacy-protective mindset and did not sell data. Under the California Privacy Rights Act amendment, companies are supposed to disclose if they sell users' personal data. Around ** percent of companies did not disclose whether they engaged in such practices.

The Data Monetization Market size was valued at USD 4.05 billion in 2023 and is projected to reach USD 20.19 billion by 2032, exhibiting a CAGR of 25.8 % during the forecasts period. The data monetization market refers to the actual steps of taking large amounts of unstructured data and transforming them into income-earning products or new business models. Businesses collect data, process and monetize them as information that they are able to sell them to other businesses or use it for the organization’s benefit such as running operations efficiently, making better decisions and making clients’ experiences better. Some of the uses include; selling the compiled consumer data to marketers, providing data services such as predeterminant analysis and letting out copyright consumer data to research firms. The concepts of its use are versatile and can be applied to retail sales, finance, health care, telecommunications, and others. Some important trends of data management are the use of big data and artificial intelligence and machine learning for analysis, burgeoning use of data markets, and legal changes related to data protection and data ownership. Since data is gaining more currency in the management of organizations, the organizations are now employing intelligent technologies and techniques to monetize on the data resources that are available to bring competitive advantage. Recent developments include: In February 2024, Gulp Data announced a partnership with Snowflake that enables organizations to explore, share, and unlock value from their data, providing data valuation, data-backed loans, and data monetization services. , In December 2023, Thales completed the acquisition of Imperva. By providing the most comprehensive solutions for the broadest range of application, data security, and identity use cases, Thales and Imperva will help customers address cybersecurity challenges that are increasing rapidly in frequency, severity, and complexity. , In September 2022, SAS declared SAS Viya on Azure as a powerful data analytics platform available on the Microsoft Azure marketplace. This new offering makes it easier than ever for businesses to gain insights from their data by combining the scalability and flexibility of Azure with the power of SAS Viya. , In March 2022, Domo, Inc. announced Data Apps, a new low-code data tool designed to make data-driven decisions and actions accessible to everyone in an organization. It makes Data Apps more accessible to a wider range of users than traditional BI tools, often specifically designed for executives, managers, and data analysts. , In January 2022, Revelate Data Monetization Corp. formerly known as TickSmith announced a $20 million Series, a funding investment to promote its innovative data-selling platform. Unlike any other product now available, its data web store is a B2B SaaS platform offering an e-commerce data shopping experience by offering all the tools required to prepare, manage, package, and monetize data. .

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 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

This dataset mainly provides the actual information of real estate transactions declared by the declared person nationwide (providing MANIFEST.CSV, schema-main.csv, schema-build.csv, schema-land.csv, schema-park)Released once on the 1st, 11th, and 21st of each month

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?

Develop Louisville Focuses on the full range of land development activities, including planning and design, vacant property initiatives, advanced planning, housing & community development programs, permits and licensing, land acquisition, public art and clean and green sustainable development partnerships.Data Dictionary:“LBA” is the abbreviation for the Louisville and Jefferson County LBA Authority, Inc."Parcel ID" is an identification code assigned to a piece of real estate by the Jefferson County Property Valuation Administration. The Parcel ID is used for record keeping and tax purposes.“IMPROV” stands for whether or not the real estate parcel had an “improvement” (i.e., a structure) situated on it at the time it was sold. “1” indicates that a structure existed when the parcel was sold and “0” indicates that the parcel was an empty, piece of land.“APPLICANT” is the individual(s) or active business entity that submitted an Application to Purchase the real estate parcel and whose application was presented to and approved by the LBA’s Board of Directors. The Board of Directors must approve each application before a transfer deed is officially recorded with the Office of the County Clerk of Jefferson County, Kentucky.“SALE DATE” is the date that the Applicant signed the transfer deed for the respective real estate parcel.“SALE AMOUNT” is the amount that the Applicant paid to purchase the respective real estate parcel.“SALE PROGRAM” is the LBA’s disposition program that the Applicant participated in to acquire the real estate parcel.The Office of Community Development defines each “Sale Program” as follows:Budget Rate (“Budget Rate Policy for New Construction Projects”) – Applicant submitted a proposed construction project for the empty, piece of land.Cut It Keep It - Applicant requested to maintain the empty piece of land situated on the same block as a real estate parcel owned by the Applicant. Applicant must retain ownership of the lot for three (3) years before the Applicant can sell it.Demo for Deed (“Last Look – Demo for Deed”) – Applicant requested to demolish the structure situated on the real estate parcel and retain the land for a future use.Flex Rate (“Flex Rate Policy for New Construction Projects”) – Applicant submitted a proposed construction project for the empty, piece of land but did not have proof of funding or a timeline as to when the project would be completed.Metro Redevelopment – The real estate parcel was part of a redevelopment project being considered by Metro Government.Minimum Pricing Policy – The pricing policy that was approved by the LBA’s Board of Directors and in effect as of the real estate parcel’s sale date.RFP (“Request for Proposals”) - Applicant requested to rehabilitate the structure in order to place it back into productive use within the neighborhood.Save the Structure (“Last Look – Save the Structure”) - Applicant requested to rehabilitate the structure in order to place it back into productive use within the neighborhood.Side Yard – The Applicant requested to acquire the LBA’s adjoining piece of land to make the Applicant’s occupied, real estate parcel larger and more valuable.SOI (“Solicitation of Interest”) – The LBA assembled two (2) or more real estate parcels and the Applicant submitted a redevelopment project for the subject parcels.For more information about each of the current disposition programs that the LBA offers, please refer to the following website pages:https://louisvilleky.gov/government/community-development/vacant-lot-sales-programshttps://louisvilleky.gov/government/community-development/vacant-structures-saleContact:Connie Suttonconnie.sutton@louisvilleky.gov

Electronic commerce and technology, enterprises that sell over the Internet, North American Industry Classification System (NAICS), for Canada from 2000 to 2007. (Terminated)

This dataset contains data on all Real Property parcels that have sold since 2013 in Allegheny County, PA.

Before doing any market analysis on property sales, check the sales validation codes. Many property "sales" are not considered a valid representation of the true market value of the property. For example, when multiple lots are together on one deed with one price they are generally coded as invalid ("H") because the sale price for each parcel ID number indicates the total price paid for a group of parcels, not just for one parcel. See the Sales Validation Codes Dictionary for a complete explanation of valid and invalid sale codes.

Sales Transactions Disclaimer: Sales information is provided from the Allegheny County Department of Administrative Services, Real Estate Division. Content and validation codes are subject to change. Please review the Data Dictionary for details on included fields before each use. Property owners are not required by law to record a deed at the time of sale. Consequently the assessment system may not contain a complete sales history for every property and every sale. You may do a deed search at http://www.alleghenycounty.us/re/index.aspx directly for the most updated information. Note: Ordinance 3478-07 prohibits public access to search assessment records by owner name. It was signed by the Chief Executive in 2007.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Graph and download economic data for Median Sales Price of Houses Sold for the United States (MSPUS) from Q1 1963 to Q2 2025 about sales, median, housing, and USA.

This dataset provides information about the number of properties, residents, and average property values for Sell Road cross streets in Banks, OR.

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.

According to Cognitive Market Research, the global Data Exchange Platform Services Market size was USD XX million in 2024 and will expand at a compound annual growth rate (CAGR) of XX% from 2024 to 2033.

North America held largest share of XX% in the year 2024 
Europe held share of XX% in the year 2024 
Asia-Pacific held significant share of XX% in the year 2024 
South America held significant share of XX% in the year 2024
Middle East and Africa held significant share of XX% in the year 2024 

Market Dynamics of the Data Exchange Platform Service Market:

Key Drivers for the Data Exchange Platform Service Market

Businesses Are Increasingly Requiring Third-Party Data to Analyse Consumer Purchase Behavior and the Market which las led to the growth of the market 

The market is experiencing an increase in demand for third-party data, which is being met by data exchange platform services. This data ranges from traffic and financial data to climatic, geographic, and streaming sensor data. In order to enhance their statistical and machine learning models, data scientists and researchers are always searching for new sources of data. Third-party data, including as demographic, psychographic, and social media information, is needed by market researchers in a variety of domains to enhance analysis, predictions, and plans and to build 360-degree perspectives of their clientele. Furthermore, big companies are already requesting clickstream data in order to, among other things, personalize user experiences and develop engaging suggestion engines. For instance, in January 2020, IBM Corporation and Yara International worked together to create an open data sharing platform that can help with field and farm data collaboration, allowing more food to be produced globally while leaving a reduced environmental impact. It is anticipated that demand for data exchange platform services will continue to grow during the forecast period due to intensifying competition and platform service providers' rush to create premium features. In order to enable data consumers to quickly survey, purchase, upload, and query such data sets, businesses are increasingly working to simplify the process for data providers to package, distribute, sell, protect, and manage data assets. Unquestionably, an uncontested data exchange platform fosters development for all parties involved—data operators, suppliers, and customers—and is easier to market and use. Throughout the forecast period, all of these factors will be propelling the worldwide data exchange platform services market.

Restraints for the Data Exchange Platform Service Market

High initial costs for Data Exchange Platform Services may hamper the growth of the market 

Initial installation costs for demand planning solution programs might be high. They also incur additional expenditures associated with upkeep. Furthermore, organizations may be compelled to boost their expenditures for staff training on how to use the systems, in addition to spending on information technology (IT) infrastructure within the company. These challenges may impede Data Exchange Platform Services market growth throughout the projection period, particularly for small and medium-sized businesses. Without internal knowledge or technical resources, the costs for gear purchases, implementation fees, and software licensing can be prohibitive. Furthermore, continuing maintenance, such as repairs, training expenses, and IT assistance, may put further strain on already limited funds Market Overview of the Data Exchange Platform Services Market

Data Exchange Platform Services are often valuable for marketers, developers, website owners, and UI/UX professionals. It collects mouse motions such as scrolling, highlighting, typing, keypresses, heatmaps, and funnels, which assist to improve the efficiency of an application or website and obtain greater conversion rates. A replay solution delivers intangible facts for users who encounter difficult challenges when visiting a website. It helps to identify issues, eradicate them, and provide a smoother online experience. Furthermore, it aids in inspecting possible consumer behavior, better investigating customer wants, and adjusting web design layouts. A session replay tool lets the customer support staff fix difficulties in real-time using heatmap analysis, which reveals...

Snapshot img

Direct Selling Market Size 2025-2029

The direct selling market size is forecast to increase by USD 73.2 million at a CAGR of 5.3% between 2024 and 2029.

The market is experiencing significant growth, driven primarily by the increasing use of social media as a sales channel. The social media platforms have become essential tools for direct selling companies to reach and engage with customers, leading to increased sales and market expansion. Another key trend in the market is the rising demand for personalized customer experiences. Direct selling companies are responding to this trend by leveraging technology to offer customized product recommendations and tailored customer interactions, enhancing the overall shopping and social commerce experience. However, the market also faces challenges that require careful navigation.
Regulatory scrutiny and compliance are becoming increasingly important issues for direct selling companies. The governments around the world are increasing their focus on regulating the direct selling industry, with stricter rules regarding product safety, labeling, and marketing practices. Companies must invest in compliance efforts to avoid potential legal issues and maintain their reputation. These challenges, while significant, also present opportunities for companies that can effectively navigate the regulatory landscape and provide high-quality, safe products and services to customers.

What will be the Size of the Direct Selling Market during the forecast period?

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The market continues to evolve, driven by shifting consumer preferences and advances in technology. Independent consultants leverage customer service and residual income to build thriving businesses in various sectors, including personal care, home products, health and wellness, and financial services. Sales promotion and lead generation are key strategies, with trade shows and social media marketing essential for expanding customer bases. Team building and party plans facilitate growth through a multi-level marketing structure, offering flexible schedules and professional development opportunities. Customer retention remains a priority, with consumer loyalty fostered through exceptional customer relationship management and product quality.
Regulatory frameworks ensure business ethics and legal compliance. Data analytics and digital marketing tools, including mobile apps, provide valuable insights and competitive advantages. Brands continue to launch innovative products, from essential oils to weight management solutions, meeting diverse consumer needs and enhancing brand awareness. The industry association supports members with training and development, product launches, and industry news. Overall, the market's continuous dynamism offers opportunities for growth and innovation across numerous sectors.

How is this Direct Selling Industry segmented?

The direct selling industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Single-level marketing
  Multi-level marketing


Product

  Health and wellness
  Cosmetics and personal care
  Household goods and durables
  Others


Sales Channel

  Person-to-Person
  Online Sales
  Party Plan


End-User

  Individual Consumers
  Businesses


Geography

  North America

    US


  Europe

    France
    Germany
    UK


  APAC

    Australia
    China
    Indonesia
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Type Insights

The single-level marketing segment is estimated to witness significant growth during the forecast period.

Direct selling is a dynamic and evolving market where independent consultants directly connect with customers to sell a range of products and services. This model, which includes personal care, home products, cosmetics , health and wellness, financial services, and more, prioritizes customer service and relationship management. Flexible schedules enable consultants to balance their work and personal lives, making it an attractive option for many. Direct sales events such as trade shows and parties provide opportunities for lead generation and brand awareness. Business ethics are crucial in this industry, with a focus on transparency and legal compliance. Team building and training and development are essential for consultant success, fostering a collaborative and supportive environment.

Compensation plans offer residual income, ensuring consultants earn commissions on their sales volume. Sales promotions and digital marketing, including social media and mobile apps, help boost sales and customer retention. Data analytics plays a significant role in understanding consumer preferences and optimizing marketing strategi

Real estate sales cases require real price registration information, including location (de-identification), area, total price, and other information. - Shuangxi District

Comprehensive market data and analytics for I SELL MY ALL POKEMON CARD COLLECTION including pricing distribution, seller metrics, and market trends.

Context

This dataset was collected in the middle east from car owners who want to sell their cars.

Content

This data represents cars that were put for sale in 2020. Make, Model and Submodel are IDs of Maker, Model and Submodel for each particular car. There are many rows with the same Make_id, Model_id and submodel_id or Model_id and submodel_id combinations. They correspond to different advertisements of the same car type. These 3 IDs can be used as categorical variables.