Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal TEPIAN
Mario Sutardiman
Information Technology, Pradita University
Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Improving Meta Ads Efficiency through Multi-Level Campaign Structuring and Budget Optimization Mario Sutardiman; Teddy Mantoro
TEPIAN Vol. 6 No. 2 (2025): June 2025
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v6i2.3386

Abstract

The rise of digital advertising has transformed the way businesses interact with consumers, making platforms like Meta Ads a cornerstone of marketing strategies. However, achieving optimal efficiency in Meta Ads remains challenging due to the complexity of campaign setups and budget allocation. This study addresses the issue by examining key configurations at three levels: campaigns, ad sets, and individual ads. The research explores how advertisers can tailor campaigns to specific objectives, such as driving traffic or increasing sales, while leveraging ad set customization for audience targeting, placement optimization, and A/B testing. To improve ad performance, this study emphasizes the importance of refining content at the ad level, ensuring alignment with campaign goals. Budget management is also highlighted, contrasting Campaign Budget Optimization (CBO) with Ad Set Budget Optimization (ABO), and offering insights into leveraging these tools to maximize returns. The study further recommends adjusting budgets based on audience behavior patterns, such as spikes in purchasing activity during twin dates or paydays. By providing actionable strategies for configuring Meta Ads, this study contributes to the field of digital marketing by bridging practical implementation and theoretical insights. Evaluation of these strategies is supported through examples of best practices, with recommendations for advertisers to enhance their Meta Ads efficiency through continual testing and strategic budgeting.
Advertising Business Processes through Data-Driven Enterprise Architecture: A Conceptual Model of PT Akuratman Mario Sutardiman; Richardus Eko Indrajit; Erick Dazki
TEPIAN Vol. 7 No. 1 (2026): March 2026
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v7i1.3390

Abstract

The advertising industry is undergoing a profound transformation driven by rapid advancements in data analytics, digital infrastructure, and artificial intelligence. Traditional marketing methods, which once relied heavily on intuition and generalized audience segmentation, are now being replaced by hyper-targeted strategies that utilize real-time insights to deliver more effective and measurable outcomes. This paper presents a conceptual study aimed at designing and optimizing business processes within PT Akuratman, a fictional digital advertising agency that adopts a data-driven operational model. Using the ArchiMate enterprise architecture framework, the study structures and analyzes four core revenue streams: Campaign Management Fees, Leads-Based Pricing, Technology Licensing, and Performance-Based Advertising. Each stream is examined through a multi-layered integration of business functions, application systems, and supporting technological infrastructure. The proposed architecture leverages cloud platforms, AI-driven analytics, and scalable data pipelines to support real-time decision-making, campaign personalization, and strategic agility. The model not only enhances operational efficiency but also reinforces client engagement and marketing ROI in a competitive digital environment. Furthermore, it serves as a practical reference for industry practitioners and scholars aiming to align enterprise architecture with emerging technological innovations. The study also suggests potential areas for future research, including adaptive architecture evolution, automation strategies, and regulatory considerations in big data ecosystems.
Co-Authors Dazki, Erick Richardus Eko Indrajit Teddy Mantoro