A Data-Driven Evaluation of Telco-Based Deterministic Targeting for Programmatic Advertising Systems

Abstract

Deterministic audience targeting based on verified identity data has emerged as a promising alternative to probabilistic approaches in programmatic advertising, particularly in response to increasing data fragmentation and privacy constraints. However, empirical evidence evaluating its effectiveness in real-world campaign environments remains limited. This study presents a data-driven evaluation of telco-based deterministic targeting using campaign data from a programmatic advertising platform. A mixed-methods approach is employed by integrating quantitative performance metrics, including click-through rate (CTR), viewability rate, cost-per-click (CPC), and cost-per-mille (CPM), with qualitative insights obtained from platform users. The analysis focuses on assessing the impact of deterministic targeting on audience relevance and engagement performance across multiple campaigns. The results indicate that telco-based deterministic targeting improves audience precision and maintains stable engagement performance, particularly in terms of CTR and viewability. However, the findings also reveal that improved targeting accuracy does not necessarily translate into higher conversion outcomes, as campaign performance is influenced by additional factors such as media placement, creative execution, and platform optimization capabilities. This study contributes to the field of data-driven advertising systems by providing empirical evidence on the effectiveness of identity-based targeting in real-world settings. It further highlights the practical limitations of deterministic approaches and emphasizes the need for integrated optimization strategies in programmatic advertising ecosystem