Predictive Analytics by Integrating Google Analytics and Pega AI
DOI:
https://doi.org/10.47941/jts.1704Keywords:
Customer Behavior Fotrecasting, Data-Driven Decision Making, Google Analytics, Predictive Analytics, Pega AIAbstract
Purpose: This paper explores the transformative integration of Google Analytics and Pega AI in predictive analytics, highlighting its potential to revolutionize data-driven decision-making.
Methodology: By harnessing Google Analytics' extensive user interaction data alongside Pega's advanced AI algorithms, businesses can create predictive models with unprecedented accuracy.
Findings: This integration facilitates enhanced customer behavior predictions, optimized marketing strategies, and improved operational efficiencies. Through real-world case studies, the paper evidences the successful application and significant impacts of this synergy.
Unique Contributor to Theory, Policy and Practice: Ultimately, the integration of Google Analytics with Pega AI emerges as a pivotal advancement, offering businesses unparalleled insights and a competitive edge in today's data-centric landscape.
Downloads
References
“Lending solutions for financial services,” Pega, https://www.pega.com/industries/financialservices/lending (accessed Dec. 11, 2023).
“AI-powered decisioning to elevate every outcome,” Pega, https://www.pega.com/products/platform/ai-decisioning (accessed Dec. 11, 2023).
“Optimize process automation with Pega process AI,” Pega, https://www.pega.com/technology/process-ai (accessed Dec. 11, 2023).
R. Banjade and S. Maharjan, “Product recommendations using Linear Predictive Modeling,” 2011 Second Asian Himalayas International Conference on Internet (AH-ICI), 2011.doi:10.1109/ahici.2011.6113930
D. Mishra, A. K. Das, M. Mausumi, and S. Mishra, “Predictive data mining: Promising future and applications,” International Journal of Computer and Communication Technology, pp. 178–186, 2011. doi:10.47893/ijcct.2011.1090
M. Rustagi and N. Goel, “Predictive analytics: A study of its advantages and applications,” IARS International Research Journal, vol. 12, no. 01, pp. 60–63, 2022. doi:10.51611/iars.irj.v12i01.2022.192
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Praveen kumar Tammana
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution (CC-BY) 4.0 License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
