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Adaptive Strategies in Online Marketing Using Machine Learning Techniques
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Author(s): Saurabh Chandra (School of Law, Bennett University, Greater Noida, India), Gauri Ghule (Department of Electronics and Telecommunication Engineering, VIIT, Kondhawa, India), Syeda Meraj Bilfaqih (Department of Computer Science, King Khalid University, Abha, Saudi Arabia), Akila Thiyagarajan (Department of Computer Science, King Khalid University, Abha, Saudi Arabia), J. Sharmila (Department of Chemistry, St. Joseph's College of Engineering, OMR, Chennai, India)and Sampath Boopathi (Department of Mechanical Engineering, Muthayammal Engineering College, Namakkal, India)
Copyright: 2025
Pages: 34
Source title:
Digital Transformation Initiatives for Agile Marketing
Source Author(s)/Editor(s): Sérgio Maravilhas (Federal University of Bahia, Brazil)and Rodrigo Ladeira (Federal University of Bahia, Brazil)
DOI: 10.4018/979-8-3693-4466-8.ch004
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Abstract
ML has gained such rapid momentum that it has now drastically shifted the strategies for online marketing toward a much more personal, efficient, and data-driven approach. In this chapter, some of the new processes of machine learning are discussed, which make online marketing more effective, with a view to major developments in areas like automated content creation, predictive analytics, and real-time customer segmentation. Armed with deep learning algorithms and natural language processing, marketers can know much more about consumer behavior to enable them to optimize ad targeting and drive better campaign outcomes. In that respect, reinforcement learning embedded in marketing automation will help drive adaptive strategies responsive to real-time data. The new chapter examines how fast-emerging trends like generative AI are changing content personalization and user engagement. These innovations, all taken together, help fine-tune strategies for marketers to eventually deliver an experience that is more relevant and impactful while maximizing their ROI.
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