Impact of Social Media Marketing on Online Consumer Behavior using Self-Organizing Maps

The effect of digital and social media marketing on consumer actions is characterized in this study. This study examines the benefits and drawbacks of digital and social media marketing to influence consumer behaviour, which has become more important as digitalization continues to expand at a rapid pace. An entirely new digital culture has emerged as a result of the far-reaching effects of the internet, smartphones, and social media on consumer behaviour. Data preprocessing, model training, and feature extraction are the three stages that make up this suggested method. To deal with data noise, one method is data pre-processing. By characterising users and their actions on a social media site, these feature extractions should make it easy to distinguish between lead users and other users. It used a SOM all through training. Outperforming MLP and ANN, this novel approach achieves an average accuracy of 93.41 %.

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Publication Details

Published
2024-10-18
DOI
https://doi.org/10.1109/ssitcon62437.2024.10796154
Primary Topic
Digital Marketing and Social Media
Type
article
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article

Impact of Social Media Marketing on Online Consumer Behavior using Self-Organizing Maps

Nandita Dinesh, Vinayak Anil Bhat, Prashant Gupta, Gourav Kalra et al.
Digital Marketing and Social Media
article

Impact of Social Media Marketing on Online Consumer Behavior using Self-Organizing Maps

Nandita Dinesh, Vinayak Anil Bhat, Prashant Gupta, Gourav Kalra, Sweta Priya, C Shakeela Banu
article en

Abstract

The effect of digital and social media marketing on consumer actions is characterized in this study. This study examines the benefits and drawbacks of digital and social media marketing to influence consumer behaviour, which has become more important as digitalization continues to expand at a rapid pace. An entirely new digital culture has emerged as a result of the far-reaching effects of the internet, smartphones, and social media on consumer behaviour. Data preprocessing, model training, and feature extraction are the three stages that make up this suggested method. To deal with data noise, one method is data pre-processing. By characterising users and their actions on a social media site, these feature extractions should make it easy to distinguish between lead users and other users. It used a SOM all through training. Outperforming MLP and ANN, this novel approach achieves an average accuracy of 93.41 %.

Jain University (IN), Maharishi Markandeshwar University, Mullana (IN), Prin. L. N. Welingkar Institute of Management Development and Research (IN)
Openalex Percentile: Top 61%
Digital Marketing and Social Media
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Impact of Social Media Marketing on Online Consumer Behavior using Self-Organizing Maps — Nandita Dinesh, Vinayak Anil Bhat, et al. · (2024) | TGRS Research Map | TGRS