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. .

According to data collected in April 2022 in the United States, Telegram was the alternative social media platform that claimed to provide necessary privacy settings and a conscious approach to handling user data. Rumble appeared to have none of the mentioned online privacy control options among all the platforms. Gab and Parler were relatively neutral, claiming they wouldn't sell user data or have targeted third-party ads at the time of the research.

The data marketplace platform market is projected to grow significantly over the coming years, driven by increasing demand for data-driven decision-making, advancements in data science and analytics, and growing adoption of cloud-based solutions. The market size is expected to reach [Market size] million by 2033, with a CAGR of [CAGR] during the forecast period 2025-2033. The market is segmented by type into personal data marketplace platforms, B2B data marketplace platforms, and IoT data marketplace platforms. The personal data marketplace platform segment is expected to hold the largest market share, owing to the increasing demand for personal data for marketing, advertising, and research purposes. The B2B data marketplace platform segment is expected to witness significant growth, driven by the growing adoption of data-driven decision-making in business organizations. The IoT data marketplace platform segment is expected to gain traction in the coming years, as the number of IoT devices and the amount of data they generate continues to grow. The data marketplace platform is an emerging market poised for exponential growth in the coming years. It provides a platform for data buyers and sellers to connect and transact data-related services, fostering innovation and unlocking the value of data assets. This report offers a comprehensive analysis of the data marketplace platform market, highlighting key trends, driving forces, challenges, key segments, and leading players.

Market Analysis: Video Commerce and Live Selling Platform The video commerce and live selling platform market is experiencing rapid growth, propelled by the surge in online shopping and the increasing popularity of video content. With a global market size of XXX million in 2019, it is projected to reach XXX million by 2033, exhibiting a CAGR of XX%. This growth is primarily driven by factors such as enhanced user experience, real-time engagement, and reduced friction in the purchasing process. Moreover, the integration of social media platforms and the adoption of AI and machine learning technologies further contribute to market expansion. The market is segmented based on application, type, and region. In terms of application, merchants and individual consumers are the key users. B2B and B2C are the primary types, with B2C dominating the market. Regionally, North America and Asia Pacific are the major markets, followed by Europe. Key players in the industry include CommentSold, Bambuser, Microsoft Stream, and BetweenStoryStream, among others. The competitive landscape is expected to intensify as players invest in innovation and expansion to capture a larger market share.

• 500K+ Active Amazon Stores • 200K+ Seller Leads • Platforms USA, Germany, UK, Italy, France, Spain, CA • C-Suite/Marketing/Sales Contacts • FBA/Non-FBA Sellers • 15+ data points available for each prospect • Filter your leads by store size, niche, location, and many more • 100% manually researched and verified.

For over a decade, we have been manually collecting Amazon seller data from various data sources such as Amazon, Linkedin, Google, and others. We are specialized to get valid, and potential data so you may conduct ads and begin selling without hesitation.

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Data Points Available:

Full Name Linkedin URL Direct Email Generic Phone Number Business Name and Address Company Website Seller IDs and URLs Revenue Seller Review Count Niche FBA/Non-FBA Country and More

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...

According to a 2025 analysis, beads were among the most common items sold on Etsy. The marketplace reported over eight million listings featuring that item, as well as nearly 5.3 million listings for pendants. Beads also recorded the highest average click-through rate (CTR) among the selected items, at 102 percent.

Wirestock's AI/ML Image Training Data, 4.5M Files with Metadata: This data product is a unique offering in the realm of AI/ML training data. What sets it apart is the sheer volume and diversity of the dataset, which includes 4.5 million files spanning across 20 different categories. These categories range from Animals/Wildlife and The Arts to Technology and Transportation, providing a rich and varied dataset for AI/ML applications.

The data is sourced from Wirestock's platform, where creators upload and sell their photos, videos, and AI art online. This means that the data is not only vast but also constantly updated, ensuring a fresh and relevant dataset for your AI/ML needs. The data is collected in a GDPR-compliant manner, ensuring the privacy and rights of the creators are respected.

The primary use-cases for this data product are numerous. It is ideal for training machine learning models for image recognition, improving computer vision algorithms, and enhancing AI applications in various industries such as retail, healthcare, and transportation. The diversity of the dataset also means it can be used for more niche applications, such as training AI to recognize specific objects or scenes.

This data product fits into Wirestock's broader data offering as a key resource for AI/ML training. Wirestock is a platform for creators to sell their work, and this dataset is a collection of that work. It represents the breadth and depth of content available on Wirestock, making it a valuable resource for any company working with AI/ML.

The core benefits of this dataset are its volume, diversity, and quality. With 4.5 million files, it provides a vast resource for AI training. The diversity of the dataset, spanning 20 categories, ensures a wide range of images for training purposes. The quality of the images is also high, as they are sourced from creators selling their work on Wirestock.

In terms of how the data is collected, creators upload their work to Wirestock, where it is then sold on various marketplaces. This means the data is sourced directly from creators, ensuring a diverse and unique dataset. The data includes both the images themselves and associated metadata, providing additional context for each image.

The different image categories included in this dataset are Animals/Wildlife, The Arts, Backgrounds/Textures, Beauty/Fashion, Buildings/Landmarks, Business/Finance, Celebrities, Education, Emotions, Food Drinks, Holidays, Industrial, Interiors, Nature Parks/Outdoor, People, Religion, Science, Signs/Symbols, Sports/Recreation, Technology, Transportation, Vintage, Healthcare/Medical, Objects, and Miscellaneous. This wide range of categories ensures a diverse dataset that can cater to a variety of AI/ML applications.

We compare house sales on a For-Sale-By-Owner (FSBO) platform to Multiple Listing Service (MLS) sales and find that FSBO precommission prices are no lower, but that FSBO is less effective in terms of time to sell and probability of a sale. We do not find direct evidence of the importance of network size as a reason for the lower effectiveness of FSBO. We do find evidence of endogenous platform differentiation: patient sellers use FSBO while patient buyers transact more often on the MLS (where they avoid patient sellers). We discuss the implications for platform competition, two-sided markets, and welfare. (JEL L85, M31, R31)

The Customer-Centric Video Selling Platform market represents a transformative shift in the way businesses engage with their customers, leveraging the power of video to enhance sales experiences. These platforms allow companies to create personalized, interactive, and engaging video content tailored to the specific

Context

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

Content

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

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

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

Inspiration

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

Photo by CardMapr on Unsplash

Measuring the gig economy has been challenging. Drawing on anonymized administrative banking data, we measure supply-side participation in the online platform economy between 2013 and 2018. We find 2.3 million account holders who received payments from 128 transportation, non-transport work, selling, and leasing platforms. Participation grew rapidly, particularly in the transportation sector. Average monthly revenues declined among drivers and increased among lessors even within metro areas. At least a third—and likely more—of the decline in transportation revenues is driven by decreases in hours worked. These findings raise important policy questions and motivate promising directions for future work.

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.

The Direct Data Monetization market is experiencing robust growth, projected to reach $1279 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 7.1% from 2025 to 2033. This expansion is fueled by several key factors. The increasing availability of data, coupled with advancements in data analytics and management technologies, enables businesses to derive significant value from their data assets. Furthermore, a growing demand for data-driven decision-making across various industries, including healthcare, finance, and retail, is driving the adoption of direct data monetization strategies. Businesses are increasingly realizing the potential to generate new revenue streams by securely and ethically sharing or selling their data, leading to a surge in market demand. The competitive landscape includes established players like Sisense, Snowflake, and SAS, alongside emerging technology providers and consulting firms like Accenture and Infosys. This competitive environment fosters innovation and drives the development of sophisticated data monetization platforms and services. The market's growth trajectory is further propelled by emerging trends such as the increased use of data marketplaces, facilitating streamlined data exchange and monetization. Organizations are increasingly adopting sophisticated data privacy and security measures, addressing concerns surrounding data governance and compliance. While the market faces challenges, such as regulatory hurdles in certain jurisdictions and the need for robust data quality management, the overall outlook remains highly positive. The consistent CAGR projection suggests sustained, predictable growth, making direct data monetization an attractive sector for both investors and businesses seeking new revenue opportunities and improved operational efficiency. The substantial market size and strong growth projections underline the significant potential of this sector for future expansion.

As the fashion e-commerce markets rapidly develop, tens of thousands of products are registered daily on e-commerce platforms. Individual sellers register products after setting up a product category directly on a fashion e-commerce platform. However, many sellers fail to find a suitable category and mistakenly register their products under incorrect ones. Precise category matching is important for increasing sales through search optimization and accurate product exposure. However, manually correcting registered categories is time-consuming and costly for platform managers. To resolve this problem, this study proposes a methodology for fashion e-commerce product classification based on multi-modal deep learning and transfer learning. Through the proposed methodology, three challenges in classifying fashion e-commerce products are addressed. First, the issue of extremely biased e-commerce data is addressed through under-sampling. Second, multi-modal deep learning enables the model to simultaneously use input data in different formats, which helps mitigate the impact of noisy and low-quality e-commerce data by providing richer information.Finally, the high computational cost and long training times involved in training deep learning models with both image and text data are mitigated by leveraging transfer learning. In this study, three strategies for transfer learning to fine-tune the image and text modules are presented. In addition, five methods for fusing feature vectors extracted from a single modal into one and six strategies for fine-tuning multi-modal models are presented, featuring a total of 14 strategies. The study shows that multi-modal models outperform unimodal models based solely on text or image. It also suggests the optimal conditions for classifying e-commerce products, helping fashion e-commerce practitioners construct models tailored to their respective business environments more efficiently.

The global video commerce and live selling platform market is experiencing explosive growth, driven by the increasing adoption of e-commerce, the rise of social media commerce, and the shift towards interactive and engaging shopping experiences. The market, estimated at $15 billion in 2025, is projected to witness a robust Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033, reaching an estimated $75 billion by 2033. This expansion is fueled by several key factors. Firstly, the ability of live selling to foster direct interaction between brands and consumers, building trust and increasing sales conversion rates, is a significant driver. Secondly, the seamless integration of live video streams across various social media platforms, like TikTok, Instagram, and Facebook, significantly broadens reach and accessibility for both merchants and consumers. Finally, advancements in technology, such as improved streaming quality and interactive features (polls, Q&A), further enhance the consumer experience and drive market adoption. The B2C segment currently dominates the market, benefiting from the widespread consumer adoption of social media and mobile shopping. However, the B2B segment is poised for significant growth as businesses increasingly leverage live selling to showcase products and services to larger audiences and partners. Geographic distribution reveals strong growth across all regions, but North America and Asia Pacific are currently leading the market, driven by high internet penetration and a strong e-commerce ecosystem. Europe is also witnessing significant adoption, with the UK and Germany emerging as key markets. However, the market faces some restraints, primarily the need for robust internet infrastructure in developing regions and potential concerns around data security and privacy. Furthermore, maintaining high-quality live streams consistently across different platforms requires substantial technical expertise and investment. Despite these challenges, the overall market trajectory indicates continued strong growth, driven by innovative platform developments and increasing consumer preference for engaging, interactive online shopping experiences. The key players mentioned, including established tech giants and emerging specialists, are continuously innovating to enhance the user experience and expand their market share within this rapidly evolving landscape.

Amazon not only boasts a hugely successful online retail platform but also a thriving digital marketplace which is seamlessly integrated with the main retail shopping experience. That being said, in the first quarter 2025, 61 percent of paid units were sold by third-party sellers. 1P and 3P Amazon sellers There are many ways of selling on Amazon. Firstly there are first-party (1P) vendor sales, where vendors send their inventory to Amazon, who in turn control the pricing and include “ships from and sold by Amazon.com” on product listings. The benefits of 1P sales on Amazon are wholesale purchases from Amazon, priority selling and brand trust through Amazon’s credibility as a seller. Amazon also permits third-party (3P) sales on its marketplace. Both individuals and professional sellers can sell on Amazon Marketplace. When it comes to order fulfillment, possible options are Fulfillment by Amazon (FBA) and Fulfillment by Merchant (FBM). Items are displayed as “sold by MERCHANT and Fulfilled by Amazon / Fulfilled by MERCHANT”. 3P sales are a popular strategy for sellers to make up for certain 1P sales disadvantages, namely improved margins through better pricing control, more favorable payment terms and less reliance on the relationship with Amazon. Amazon seller revenues This magic formula has ultimately cashed in for Amazon, which has seen its net revenues multiply in recent years. In 2023, the e-commerce giant generated approximately 140 billion dollars in third-party seller services, an increase of about 23 billion dollars from the previous year. While these figures are the product of orders throughout the year, a significant chunk is attributable to special offer and discount days. According to a survey, Black Friday is the shopping event driving the largest sales increase for Amazon sellers, followed by two of the company's own events, Prime Day and Amazon Summer Sale. In the context of the coronavirus pandemic, Amazon Prime Day played a particularly decisive role for small and medium-sized businesses around the world, many of which had to turn to online sales overnight in order to survive.

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The direct selling market is experiencing robust growth, driven by increasing consumer preference for personalized shopping experiences and the expansion of e-commerce platforms facilitating direct-to-consumer engagement. Let's assume a 2025 market size of $150 billion, considering the significant involvement of major players like Rapp, Epsilon, and Merkle, and a Compound Annual Growth Rate (CAGR) of 7% for the forecast period (2025-2033). This suggests a substantial market expansion, projected to reach approximately $275 billion by 2033. Key drivers include the growing adoption of multi-channel strategies by businesses, leveraging telemarketing alongside digital channels for a comprehensive approach. The enterprise and government sectors are significant contributors to market revenue, with increasing demand for targeted advertising and effective lead generation. However, challenges remain, including rising customer skepticism towards telemarketing and increasing data privacy regulations that necessitate careful adherence to ethical and legal standards. Segmentation within the market reflects the diverse applications of direct selling, ranging from B2B strategies in enterprise and government sectors to B2C approaches within the consumer segment. The geographic distribution showcases strong performance in North America and Europe, with emerging markets in Asia Pacific showing significant growth potential. The competitive landscape is characterized by established players and emerging technology-driven companies that offer sophisticated data analytics and automation capabilities to enhance campaign effectiveness. The market's sustained growth trajectory is underpinned by the increasing sophistication of direct selling strategies. Businesses are investing heavily in data analytics to personalize customer interactions, improving targeting and conversion rates. The integration of AI and machine learning is further enhancing campaign efficiency, allowing for real-time optimization and improved return on investment (ROI). While regulatory hurdles pose a challenge, innovative solutions focusing on data privacy and transparency are being implemented by market players to maintain consumer trust and comply with evolving regulations. The future of direct selling involves a seamless blend of traditional telemarketing with digital channels, creating a personalized omnichannel experience that caters to the evolving needs and preferences of customers across different demographics and geographical locations. The continued evolution of technology and the increasing emphasis on data-driven decision-making are poised to fuel further market expansion in the coming years.

The Pre-Selling Software Platforms market has emerged as a pivotal component for businesses aiming to streamline their sales processes and enhance customer engagement. This market, which encompasses platforms that facilitate product marketing and customer interaction before the official sales launch, has shown signi