Leveraging Machine Learning for Enhanced Online Marketing Activities

Pratiksha Jha (), Vivek Aggarwal and Ujjwal Kalra
Additional contact information
Pratiksha Jha: School of Business and Management, Christ University
Vivek Aggarwal: School of Business, Galgotias University
Ujjwal Kalra: School of Business and Management, Christ University

A chapter in Digital Advertising and Consumer Behavior, 2026, pp 215-230 from Springer

Abstract: Abstract In today’s digitally connected marketplace, the use of machine learning (ML) in online marketing has shifted from being a novel enhancement to a core necessity. This chapter explores the growing significance of ML technologies in reshaping how businesses interact with customers, manage operations, and develop strategies in e-commerce. It focuses on real-world applications of various machine learning models such as decision trees, neural networks, support vector machines, and ensemble methods to highlight how they support more informed decision-making and customised user experiences. The chapter places strong emphasis on practical implementations. It begins with a detailed discussion of personalised recommendation engines, which have evolved to consider individual preferences, browsing patterns, and contextual factors. It then moves into the realm of dynamic pricing, where algorithms adapt prices in real time based on demand, competition, and consumer behaviour. In addition, the chapter highlights how businesses are using ML to forecast sales, manage inventory efficiently, and reduce both surplus and shortages in stock. Security and customer trust are also explored through the lens of fraud detection systems powered by ML, which can identify suspicious activities with high precision. The chapter also examines customer service innovation through AI-driven chatbots that understand natural language, helping companies provide faster, more responsive support. Beyond that, it investigates how customer segmentation and sentiment analysis help marketers design more relevant campaigns and understand consumer emotions more deeply. This chapter does more than explain technology it connects these tools to business impact. Through examples and clear analysis, it demonstrates how machine learning helps businesses remain agile, customer-focused, and competitive. The goal is to show not just what ML can do but how it can be thoughtfully applied to meet evolving consumer expectations in a fast-paced digital economy.

Date: 2026
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:mgmchp:978-981-95-7809-2_13

Ordering information: This item can be ordered from
http://www.springer.com/9789819578092

DOI: 10.1007/978-981-95-7809-2_13

Access Statistics for this chapter

More chapters in Management for Professionals from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().