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 (36 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 200 companies targeting children and families in the United States, only 25 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 13 percent of companies did not disclose whether they engaged in such practices.

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?

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

Online Retail Transaction Data

UK Online Retail Sales and Customer Transaction Data

By UCI [source]

About this dataset

Comprehensive Dataset on Online Retail Sales and Customer Data

Welcome to this comprehensive dataset offering a wide array of information related to online retail sales. This data set provides an in-depth look at transactions, product details, and customer information documented by an online retail company based in the UK. The scope of the data spans vastly, from granular details about each product sold to extensive customer data sets from different countries.

This transnational data set is a treasure trove of vital business insights as it meticulously catalogues all the transactions that happened during its span. It houses rich transactional records curated by a renowned non-store online retail company based in the UK known for selling unique all-occasion gifts. A considerable portion of its clientele includes wholesalers; ergo, this dataset can prove instrumental for companies looking for patterns or studying purchasing trends among such businesses.

The available attributes within this dataset offer valuable pieces of information:

• InvoiceNo: This attribute refers to invoice numbers that are six-digit integral numbers uniquely assigned to every transaction logged in this system. Transactions marked with 'c' at the beginning signify cancellations - adding yet another dimension for purchase pattern analysis.

• StockCode: Stock Code corresponds with specific items as they're represented within the inventory system via 5-digit integral numbers; these allow easy identification and distinction between products.

• Description: This refers to product names, giving users qualitative knowledge about what kind of items are being bought and sold frequently.

• Quantity: These figures ascertain the volume of each product per transaction – important figures that can help understand buying trends better.

• InvoiceDate: Invoice Dates detail when each transaction was generated down to precise timestamps – invaluable when conducting time-based trend analysis or segmentation studies.

• UnitPrice: Unit prices represent how much each unit retails at — crucial for revenue calculations or cost-related analyses.

Finally,

• Country: This locational attribute shows where each customer hails from, adding geographical segmentation to your data investigation toolkit.

This dataset was originally collated by Dr Daqing Chen, Director of the Public Analytics group based at the School of Engineering, London South Bank University. His research studies and business cases with this dataset have been published in various papers contributing to establishing a solid theoretical basis for direct, data and digital marketing strategies.

Access to such records can ensure enriching explorations or formulating insightful hypotheses about consumer behavior patterns among wholesalers. Whether it's managing inventory or studying transactional trends over time or spotting cancellation patterns - this dataset is apt for multiple forms of retail analysis

How to use the dataset

1. Sales Analysis:

Sales data forms the backbone of this dataset, and it allows users to delve into various aspects of sales performance. You can use the Quantity and UnitPrice fields to calculate metrics like revenue, and further combine it with InvoiceNo information to understand sales over individual transactions.

2. Product Analysis:

Each product in this dataset comes with its unique identifier (StockCode) and its name (Description). You could analyse which products are most popular based on Quantity sold or look at popularity per transaction by considering both Quantity and InvoiceNo.

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. Concatenating invoice numbers (which stand for separate transactions) per client will give insights about your clients as well.

4. Geographical Analysis:

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

Practical applications

Understand what products sell best where - It can help drive tailored marketing strategies. Anomalies detection – Identify unusual behaviors that might lead frau...

Data Monetization Market size was valued at USD 3.5 Billion in 2023 and is projected to reach USD 8.5 Billion by 2030, growing at a CAGR of 20.3% during the forecast period 2024-2030.Global Data Monetization Market DriversThe growth and development of the Data Monetization Market are attributed to certain main market drivers. These factors have a big impact on how integrated gas systems are demanded and adopted in different sectors. Several of the major market forces are as follows:Increasing Data Volume: As digital technologies have spread widely, the amount of data produced by organizations, people, and networked devices has increased exponentially. Organizations have the opportunity to monetize their data assets due to the volume of data.Advanced Analytics and Data Technologies: Organisations may now extract meaningful insights from their data thanks to developments in analytics techniques like machine learning and artificial intelligence. These insights can be made profitable in a number of ways, such by providing data-driven goods and services or specialized advertising.A Greater Attention to Data Monetization Strategies: Companies are aggressively looking for ways to monetize their data assets as they become more and more aware of their worth. This entails creating plans for how to market, package, and sell data to third parties or how to create value by streamlining decision-making procedures.Regulatory Environment: Organisations are being prompted to investigate compliant methods of monetizing their data assets by regulatory frameworks like the CCPA and GDPR, which have raised awareness regarding data protection and security. Businesses who are involved in data monetization operations must take compliance with these requirements into account.Data marketplaces are becoming more and more popular, offering venues for the purchase, sale, and exchange of data assets. By facilitating trades between users and data producers, these markets increase accessibility and liquidity within the ecosystem of data monetization.Industry Convergence and Partnerships: In order to take advantage of one another's data assets for mutual gain, industries are working together more and more and establishing partnerships. Collaborations across industries help businesses generate new revenue streams and develop creative data-driven solutions.Demand for Personalised Experiences: Customers are coming to expect more and more from companies in a variety of industries when it comes to personalized experiences. Through data monetization, businesses can use consumer information to create customized goods, services, and advertising campaigns that increase client happiness and loyalty.

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.

This dataset provides information about the number of properties, residents, and average property values for Sell Drive cross streets in Claysburg, PA.

The global data monetization market is projected to reach a value of USD 53030 million by 2033, expanding at a CAGR of 46.8% during the forecast period (2025-2033). The market is driven by the growing volume and variety of data, the increasing adoption of cloud computing and big data analytics, and the need for businesses to generate new revenue streams. Key trends in the data monetization market include the rise of data marketplaces, the development of new technologies for data monetization, and the increasing focus on data privacy and security. Data marketplaces provide a platform for businesses to buy and sell data, and they are expected to play a major role in the growth of the data monetization market. The development of new technologies for data monetization is also expected to drive growth, as these technologies make it easier for businesses to extract value from their data. Finally, the increasing focus on data privacy and security is expected to lead to the development of new regulations and standards, which will impact the way that businesses monetize their data.

United States CSI: Home Selling Conditions: Good Time to Sell data was reported at 76.000 % in May 2018. This records an increase from the previous number of 72.000 % for Apr 2018. United States CSI: Home Selling Conditions: Good Time to Sell data is updated monthly, averaging 53.000 % from Nov 1992 (Median) to May 2018, with 307 observations. The data reached an all-time high of 77.000 % in Mar 2018 and a record low of 3.000 % in Oct 2010. United States CSI: Home Selling Conditions: Good 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?

This dataset provides information about the number of properties, residents, and average property values for Sell Street cross streets in Hartford, WI.

Customs records of Cana are available for SELL OFF USA. Learn about its Importer, supply capabilities and the countries to which it supplies goods

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

Customs records of are available for SELL WELL TRADING LIMITED. Learn about its Importer, supply capabilities and the countries to which it supplies goods

This dataset provides information about the number of properties, residents, and average property values for La Sell Drive cross streets in Champaign, IL.

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

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.

Dataset Description

The dataset for this competition (both train and test) was generated from a deep learning model trained on the Health Insurance Cross Sell Prediction Data dataset. Feature distributions are close to, but not exactly the same, as the original. Feel free to use the original dataset as part of this competition, both to explore differences as well as to see whether incorporating the original in training improves model performance.

Variable Definition

• id: Unique ID for the customer
• Gender: Gender of the customer
• Age: Age of the customer
• Driving_License: 0 : Customer does not have DL, 1 : Customer already has DL
• Region_Code: Unique code for the region of the customer
• Previously_Insured: 1 : Customer already has Vehicle Insurance, 0 : Customer doesn't have Vehicle Insurance
• Vehicle_Age: Age of the Vehicle
• Vehicle_Damage: 1 : Customer got his/her vehicle damaged in the past. 0 : Customer didn't get his/her vehicle damaged in the past.
• Annual_Premium: The amount customer needs to pay as premium in the year
• Policy_Sales_Channel: Anonymized Code for the channel of outreaching to the customer i.e. Different Agents, Over Mail, Over Phone, In Person, etc.
• Vintage: Number of Days, Customer has been associated with the company
• Response: 1 : Customer is interested, 0 : Customer is not interested

Orignal Dataset ---> Reference

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...