AI-Powered Revenue Management and Monetization: A Data Engineering Framework for Scalable Billing Systems in the Digital Economy

Abstract

Artificial Intelligence (AI) takes today's billing indicators and extends them with the monetization of singular, attributed revenue lines. We present the data engineering framework of a new approach to scalable systems for AI-powered revenue management and monetization. In this digital economy, we are transforming the rapid-release train engineering framework of a Tier 1 billing platform, converging to a unified schema with a converged data graph. Our experiments show the opportunity to increase revenue by 8.4% on gaming use cases. We describe case studies, enabler capabilities, and transition services, and uniquely contribute a world-first analysis of accelerator tools for rapid re-convergence of trade state back to real-time data during these transformations. Our case study and evaluation indicate the benefits of moving billing systems to this universal, integrated data platform.

Research highlights include insights into how AI and data management are positioning revenue penetration toward deep monetization and discovering the confluences of revenue events and operations that will make disruption possible. Insights regarding data usage transform the traditional perspective on billing from a cost center to the strategic data utilization and revenue multiplier for which it is currently under-invested and reveal the value and relevance of billing data for research in the areas of digital monetization and market pricing. We outline the practical emphasis on the need for a big data engineering platform rather than efficient research insights that are dataset-independent and show how easy and flexible legislation on internal projects led to a release of unique digital systems knowledge. Data-driven insights highlight the importance of extant detailed context over clean and aggregated data for leading indicators work and indicate a wider potential for our revenue engineering findings well beyond gaming. The results of a systems review encourage agile, multidisciplinary innovation as well as engineering systems interventions that are immediately profitable through revenue increases. We outline a data-driven revenue engineering approach drawing upon earlier insights and leveraging modern revenue processing that can be multilaterally productized for crowdsourced business intelligence revenue drift estimation. In contrast to traditional classifiers, our approach considers in detail real-world systems complexity and has resulted in over 20% annual margin increase through operations cost reduction.