sajm






Optimizing Digital Engagement through Machine Learning Strategies in Viksit Bharat’s Marketing Landscape

Abhishek Raidas* and Priyanka Dhoot**

DOI: https://doi.org/10.62206/sajm.31.5.2024.79-99

PUBLISHED :29 JAN 2025

Abstract

Marketing strategies are undergoing a sea change due to the proliferation of user-generated data and the accessibility of Machine Learning (ML) methods. The vast array of potential presented by ML applications for establishing and sustaining a competitive economic advantage is still mostly unknown to researchers and marketers. We focus on the broader question of how ML impacts optimization of digital marketing performance through a study using an extensive dataset from three organizations: two in retail and one in consumer goods; considering over only for the campaigns conducted in the duration of past seven days. The research considers customer behavior, brand visibility, and digital marketing revenue using a hybrid classification technique, i.e., Random Forest along with Naïve Bayes and Logistic Regression. The ML models will predict audience engagement, reduce waste targeting, and improve the segmentation of different campaign types. According to the findings, search campaigns served as best ROI and conversion rates, with influencer marketing serving younger demographics. By demonstrating ways that ML can be used to enhance campaign efficacy, this research empowers marketers with valuable tools to sharpen their strategies and increase engagement for long-term brand growth.

Key Words

Artificial Intelligence, Digital Marketing, Machine Learning, Online Advertising

Author Biography

Abhishek Raidas*
Assistant Professor, Indira Institute of Management, Pune, India. E-mail: This e-mail address is being protected from spambots. You need JavaScript enabled to view it

Priyanka Dhoot**
Assistant Professor, D.Y.Patil International University, Akurdi, Pune, India. E-mail: This e-mail address is being protected from spambots. You need JavaScript enabled to view it

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