In recent years, we have witnessed an exponential increase in the amount of data collected and managed by companies of all sizes. And this dynamic is certainly linked to the spread of digitalization. There are data generated by customers, products, suppliers, distribution partners, all the different digital and physical touchpoints and increasingly from machines. Alongside this growing collection of data, we are simultaneously witnessing an increase in the costs of managing and protecting this information. For example, costs related to cybersecurity and costs for privacy management and compliance. As large digital companies (OTTs) demonstrate, data, if correctly exploited, represent a great productive asset that transcends individual contexts of use.
Data Monetization is a term used with different meanings, causing quite a bit of confusion among professionals. In general terms, Data Monetization refers to all those processes that use data to produce measurable economic value. After all, companies have always dealt with their data: the substantial difference in today’s approach is being able to exploit their processing capacity in a much more extensive way. But the determining factor driving this process is the need for all companies to identify competitive advantages in increasingly liquid and fast contexts. In a market, in fact, without clear boundaries, a company’s ability to innovate and adapt quickly is fundamental. And data monetization comes into play as a strategic lever.
We are therefore witnessing a phenomenon that redefines the business strategies of modern companies, marking an era in which data are transformed into true economic assets.
The role of Artificial Intelligence in data monetization.
And it is in this context that Artificial Intelligence plays an increasingly important role, which, in the final analysis, is a formidable system for activating available data.
Artificial intelligence will increasingly be at the center of digital transformation, also because it will act precisely as a catalyst in data monetization. AI, in fact, can discover hidden patterns in the large volumes of available information, providing companies with valuable insights to make strategic decisions, improve operational efficiency, and personalize the customer experience, increasing profitability. But also to process new data and indicators that will increasingly be an added value for partners and third parties. This ability to quickly leverage available data will be increasingly essential.
From a methodological point of view, we can distinguish between direct and indirect monetization.
Direct data monetization strategies: Data As a Service as a new business model.
Direct data monetization represents a strategy through which companies capitalize on their data, transforming them into directly marketable assets. This includes the sale of high value-added profiled data to advertisers, to companies that can use them for targeted advertising campaigns, for the direct sale of products and services (not in competition with the Data Seller), or for the analysis of specific market trends.
In recent years, we have been witnessing a growing demand for external data and information to complement those already available internally.
The world of financial operators (investment and retail banks, insurance companies, hedge funds), for example, is showing an increasing interest in new sets of refined data and unconventional indicators, which are integrated to fuel and create new investment strategies or to personalize financial services, highlighting the transversality and value of data as assets.
Pharmaceutical companies are also significantly expanding the use of external data, not only to improve research and support internal product development, but also for the identification of new therapeutic targets. The ability to manage and integrate data generated in all phases of the value chain, from R&D to real-world use after regulatory approval, has become fundamental. Through advanced analytics leveraging predictive models, machine learning, and AI algorithms, pharmaceutical companies can gain a deeper understanding of patient characteristics and how these influence treatment outcomes. For example, algorithms linking laboratory and clinical data can create automated reports that identify related applications or compounds and raise red flags regarding safety or efficacy.
Direct Data Monetization is becoming a valuable resource for other sectors as well, such as Consumer Goods, Fashion, and Automotive brands, etc.
Companies in these sectors, by leveraging detailed analysis of even external profiled data, can both refine their marketing strategies, developing products that better reflect consumer expectations, and optimize advertising campaigns by creating micro-targeting actions.
Furthermore, through the acquisition of data on consumer behavior, a brand can identify new emerging trends, personalize offers, and gain a significant competitive advantage.
Data Buyers in the Fashion and Automotive sectors, where customer behavior and preferences are constantly evolving, use external data sets that allow them to anticipate changes in consumption habits and quickly adapt everything from sourcing and production to sales and distribution activities.
Even the most sophisticated Consumer Goods brands are increasingly becoming Data Buyers. On the other hand, by using a combination of internal and external data, these operators can optimize the supply chain, predict market demand (bypassing the information block of distribution), and create targeted promotions that better respond to the specific interests of their customers.
But the most significant impact of direct Data Monetization is occurring in the world of Media Advertising, particularly in the context of Programmatic, which is increasingly seeking information to profile users in a targeted way. Indeed, the phase-out of third-party cookies is pushing the media advertising market toward a more advanced use of Artificial Intelligence to intercept relevant information. AI, in fact, plays a fundamental role in compensating for the loss of data resulting from the decline of cookies, allowing for the maintenance of personalization and effectiveness in advertising campaigns.
From a data supply perspective (Data Providers or Sellers), direct monetization is taking shape as an arena where various players are exploring and exploiting this opportunity. The world of Publishers and Media has long been at the center of this process, but now multi-channel Retailers and e-commerce platforms are also discovering untapped potential. Given that only a small percentage of site-generated traffic converts into core sales revenue, valuing all the remaining interactions with their customers and the high value-added information on resulting purchase behaviors is becoming a new business opportunity.
These companies, therefore, are leveraging data collected through online and offline transactions to assist strategic decisions, personalize the customer experience, and, now increasingly, to open new revenue channels.
The Mobile App sector is also growing very strongly in the direct monetization of collected data. In particular, the world of gaming Apps stands out as a prolific generator of user data, which is used for targeted and personalized advertising offers directly in-App. Thanks to the amount of data collected from millions of daily interactions, gaming apps, in fact, can offer behavioral and preference details that are pure gold for marketers.
In this context, we cannot ignore the significant role of telecommunications companies (Telcos), which, with their direct access to traffic data and usage habits, find themselves in a privileged position to monetize information directly.
Given this diversified landscape of supply and demand interest, it is not surprising that over the years, traditional large data providers (think of entities in the finance world such as Bloomberg, Thomson Reuters, and Nasdaq itself, which today also aggregate third-party data) have been joined by new dedicated marketplace platforms that offer the exchange of various types of data, operating with the Data As a Service (DAAS) model. These platforms simplify customer identification and the scalability of sales models for the supply side and provide the possibility to establish benchmark values for available data sets; for the demand side, platforms facilitate the search and comparison of data sets and their purchase, also offering quality checks and rigorous security protocols to protect sensitive information.
Indirect data monetization strategies: transforming businesses into marketplaces and beyond.
Indirect data monetization is a process in which businesses use data to optimize internal operations, enrich information (CRM enrichment), increase customer profitability, open new markets, and innovate products that more effectively respond to consumer expectations. However, from a more evolved perspective, it is not just about improving existing offers, but also about enabling the creation of new business models, such as integrated service platforms that amplify the value of the corporate proposition.
Within a journey that transitions from more tactical adoption logic of tools based on more or less sophisticated Business Intelligence systems to an evolutionary strategic path in indirect data monetization, we could say that businesses are called to evolve from a ‘Foundational Phase’ to an ‘Advanced Phase’. At the base of this path, technology is not the determining factor and indeed, very often, it does not necessarily require a revisit of existing technical infrastructures or the adoption of new expensive platforms. Many companies already have extensive data collection thanks to investments made over the years, and what is often missing is a clear strategy to transform the available data and information into business intelligence and consequently into tangible economic value.
In the ‘Foundational Phase’, companies begin using data to inform operational decisions and to enrich customer knowledge. This includes personalizing customer interactions (CRM enrichment), cost optimization, and improving operational efficiency. In this phase, information is primarily used to increase the profitability of current customers and to open new markets.
The transition toward an ‘Advanced Phase’ implies greater sophistication in the use of data. Here, companies begin to leverage the power of predictive analysis and artificial intelligence not only to predict market needs and consumer expectations but also to actively influence those expectations and create new innovative products and services. This strategic shift focuses on three key areas:
Data-Driven Decision Making: Moving from intuition-based decisions to data-driven decisions, using predictive analysis models to guide product development and service innovation.
New Business Models or Servitization: Developing new business models by enriching products with integrated solutions or transforming them into a service logic, which is the basis of Servitization models that will also have a strong impact in the world of manufacturing companies.
Using available data on their customers and suppliers, companies will be able to conceive new models that are not based on the legacies of existing products, but on the possibility of devising new ones that, for example, will incorporate logic strongly oriented toward service provision. At the base of this important corporate transformation is a customer knowledge driven paradigm, based on advanced governance of available data and AI systems. Think of the incredible ability of digital OTTs to penetrate new markets with impressive speed based precisely on deep knowledge and user/customer engagement.
Partner Selection and Marketplace Dynamics: Strategically collaborating with third parties to expand the offer of services and products, creating a richer ecosystem that more completely responds to consumer needs.
In the market, aside from large digital players, various types of marketplaces exist today, including vertical ones, which satisfy various customer needs, such as simplified sourcing of products or services, direct sales between companies in the same sector, or the possibility for companies to group their offer on a single platform, thus facilitating sales and order management.
An emerging phenomenon in the landscape of indirect data monetization, however, is the transformation of traditional companies, such as banks, insurance companies, energy, and telecommunications companies, into true marketplaces of products and services. These players, through the strategic use of data, can now offer their customers a wider range of services, often in collaboration with third parties, creating new revenue sources and improving customer loyalty. This approach allows companies to cross the traditional boundaries of their sector and explore new commercial horizons.
The adoption of these strategies, as mentioned, is not only a matter of technology but also of corporate culture and processes. The key lies in knowing how to adopt and integrate advanced analytics systems and increasingly AI, which are measurable and transparent, so that a clear and quantifiable value can be attributed to every data-driven activity. Instead of revolutionizing existing infrastructures, companies should therefore focus on improving the capabilities of these technologies, with a targeted integration of AI tools, to work with available data in a smarter and more strategic way.
The road ahead in indirect data monetization is defined by a strategic transition that places data at the center of corporate innovation, leveraging existing resources and tools to create new opportunities and a sustainable competitive advantage. This path allows companies to transform into dynamic and reactive marketplaces, ready to face the challenges of a constantly evolving market.
The Importance of First-Party Data for Data Monetization.
As privacy regulations evolve and third-party cookies decline, there is an increasing emphasis on the responsible and compliant use of data. In this new era, collected first-party information becomes a treasure of inestimable value, allowing precise user profiling in full compliance with privacy and increasingly stringent regulations. Data monetization thus emerges not only as a path for commercial innovation but also as a strategic necessity to diversify revenue streams and consolidate positions in an increasingly digital market.
First-party cookies, collected directly from the domain the user visits, ensure that information is used in a compliant and responsible manner. This allows companies to maintain a high level of personalization in services, which is essential for maintaining customer trust.
Integrating first-party cookies into Data Monetization strategies means possessing a solid base of reliable data to drive business decisions. The abandonment of third-party cookies, as previously anticipated, will increasingly push companies toward a more advanced use of artificial intelligence specifically to intercept relevant information. AI plays a fundamental role in compensating for the loss of data resulting from the decline of cookies, allowing for the maintenance of personalization and effectiveness in advertising campaigns. There is a shift from a logic of quantity, which is not very respectful of users’ rights to protect their privacy, to one of quality, which must increasingly be based on informed consent and new modes of information extraction. Ultimately, first-party cookies are not just complementary but essential in an ecosystem of direct and indirect data monetization.
In conclusion, Data Monetization represents a fundamental pillar for companies intending to successfully navigate today’s complex economic landscape. The integration of artificial intelligence and the adoption of innovative business models, such as servitization, the adoption of Data As a Service (DAAS) models, and the transformation into digital marketplaces, are key strategies for exploiting the potential of data. The companies that succeed in capitalizing on these trends, while respecting data privacy and security, will be the ones leading the transformation of their sectors, creating value for themselves and their customers.
MIT Technology Review Italia
https://www.technologyreview.it/mercato-della-data-monetization-una-nuova-sfida-dellai/
