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Data Monetization Through Tailored Demand Representation

Abstract:

In today's digital economy, data represents a critical strategic resource, necessitating innovative approaches to its monetization and value realization. This study evaluates various methodologies for data monetization through customized demand representations. By examining diverse consumer demand models, we capture the intricate behaviors of digital market consumers. Our contribution includes improving existing modeling techniques by incorporating nuanced dependencies of data value, such as price sensitivity, quality perception, and trustworthiness of services. Moreover, we address non-discrete service consumption scenarios relevant to contemporary offerings like Information-as-a-Service (IaaS) and Answers-as-a-Service (AaaS). This work contributes to the linkage between theoretical models and practical strategies specific to data markets, expanding current understanding and providing actionable insights for effective data monetization strategies. Additionally, the study highlights potential areas for future research, particularly regarding the integration of emerging technological advancements and evolving regulatory landscapes. These insights can guide firms in adapting their data monetization frameworks to maintain a competitive advantage in rapidly changing markets.