Open Access   Article Go Back

Machine Learning-Driven KPIs for Revenue Optimization in Adtech

Naga Harini Kodey1

  1. Principal QA Engineer, Viralgains INC, Boston, United States.

Section:Research Paper, Product Type: Journal Paper
Volume-12 , Issue-9 , Page no. 14-17, Sep-2024

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v12i9.1417

Online published on Sep 30, 2024

Copyright © Naga Harini Kodey . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library

XML View
PDF Download

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: Naga Harini Kodey, “Machine Learning-Driven KPIs for Revenue Optimization in Adtech,” International Journal of Computer Sciences and Engineering, Vol.12, Issue.9, pp.14-17, 2024.

MLA Style Citation: Naga Harini Kodey "Machine Learning-Driven KPIs for Revenue Optimization in Adtech." International Journal of Computer Sciences and Engineering 12.9 (2024): 14-17.

APA Style Citation: Naga Harini Kodey, (2024). Machine Learning-Driven KPIs for Revenue Optimization in Adtech. International Journal of Computer Sciences and Engineering, 12(9), 14-17.

BibTex Style Citation:
@article{Kodey_2024,
author = {Naga Harini Kodey},
title = {Machine Learning-Driven KPIs for Revenue Optimization in Adtech},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {9 2024},
volume = {12},
Issue = {9},
month = {9},
year = {2024},
issn = {2347-2693},
pages = {14-17},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=5718},
doi = {https://doi.org/10.26438/ijcse/v12i9.1417}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v12i9.1417}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=5718
TI - Machine Learning-Driven KPIs for Revenue Optimization in Adtech
T2 - International Journal of Computer Sciences and Engineering
AU - Naga Harini Kodey
PY - 2024
DA - 2024/09/30
PB - IJCSE, Indore, INDIA
SP - 14-17
IS - 9
VL - 12
SN - 2347-2693
ER -

VIEWS PDF XML
193 189 downloads 107 downloads
  
  
           

Abstract

As the AdTech industry evolves, it increasingly relies on Key Performance Indicators (KPIs) to measure success. Traditional KPIs such as ad-impressions, ad click-through rates and survey responses have long served as benchmarks for campaign performance. However, with the rise of machine learning (ML) and automation, the need for more sophisticated and predictive KPIs is apparent. This paper introduces a novel approach, proposing machine learning-driven KPIs designed to optimize revenue streams and address challenges like ad fatigue, cross-device behavior, and accessibility. By automating KPI validation and implementing advanced metrics—such as Ad Accessibility Optimization, Ad Fatigue Prevention Index, and Cross-Device Path Efficiency—this paper offers an innovative framework for enhancing data-driven decision-making in real time. These new KPIs aim to predict optimal ad strategies and improve campaign performance, ultimately maximizing ROI.

Key-Words / Index Term

Key Performance Indicators (KPI), automation, machine learning, AdTech, revenue optimization, accessibility, ad fatigue, cross-device efficiency.

References

[1] A. Abran, Software Metrics and Software Metrology. John Wiley, 2010.
[2] Z. Bo and B. Zhang, "Precise Marketing of Precision Marketing Value Chain Based on Big Data,” Journal of Intelligent and Fuzzy Systems, 2018.
[3] A. Goldfarb and C. E. Tucker, "Standardization and the Effectiveness of Online Advertising," Operations Research, 2017.
[4] S. Muthukrishnan, "Ad exchanges: Research issues," in Proc. Int. Workshop Internet Netw. Econ., pp.1-12, 2009.
[5] C. Perlich, B. Dalessandro, R. Hook, O. Stitelman, T. Raeder, and F. Provost, "Bid optimizing and inventory scoring in targeted online advertising," in Proc. 18th ACM SIGKDD Int. Conf. Knowl. Discov. Data Mining, pp.804-812, 2012.
[6] H. B. McMahan et al., "Ad click prediction: A view from the trenches," in Proc. Int. Conf. Knowl. Discov. Data Mining, pp.1222-1230, 2013.
[7] M. A. Badri, D. Davis, and D. Davis, "A study of measuring the critical factors of quality management," Int. J. Qual. Reliab. Manag., Vol.12, No.2, pp.36–53, 1994.
[8] J. Shu, X. Shen, H. Liu, B. Yi, and Z. Zhang, "A content-based recommendation algorithm for learning resources," Multimedia Systems, Mar., Vol.24, No.2, pp.163-173, 2018.
[9] M. Bernard, S. Gianni, and A. Neely, "Intellectual capital-defining key performance indicators for organizational knowledge assets," J. Bus. Process Manag., Vol.10, No.5, pp.551–569, 2004.
[10] H. Meier, H. Lagemann, F. Morlock, and C. Rathmann, "Key performance indicators for assessing the planning and delivery of industrial services," Procedia Cirp, Vol.11, pp.99-104, 2013.
[11] Yash Patil, Samidha Ashtikar, Sakshi Shirodkar, Krishna Dudhate, Shraddha V. Pandit, “Recommendation Systems in Online Retail: A Comprehensive Survey of AI Techniques,” Feb., Vol.12, Issue.2, pp.30-36, 2024.
[12] Shashank Saroop, Radha, “Blockchain Based Data Privacy through Artificial Intelligence: Review” Dec., Vol.11, Issue.12, pp.32-37, 2023.