Exploring the influence of social media engagement on online shopping preferences of youth and young adults: a machine learning approach

Abstract

The modern-day rise of social media platforms and their influence on customer behavior has been a trending topic. It is important for businesses to understand the connection between young people’s engagement with social media and their online shopping habits. This research paper aims to provide how engagement with social media affects an individual’s online purchase decision. This study helps us learn how social media and influencers have an impact on today’s youth. For this purpose, we derived 4 sections of online interactions and shopping i.e., General Engagement, Online Food Purchases, Online Subscription Services, and Online Gaming. The objective of this research is to understand the online shopping preferences of young people by statistical analysis of their social media activity, such as likes, engagement, sharing, and interactions with influencers and followers. Data has been gathered through conducting a curated survey among the youth and young adults. A total of 1018 data has been collected for the research through online survey forms which were utilized for running machine learning models. The outcomes of the study shed insight into how young people’s online buying habits are influenced by their use of social media. It also concluded how high social media engagement ensues high online purchase behavior. Businesses may improve their marketing tactics by creating more social media-based ad campaigns which will be able to reach more modern-day youths. Influencers and creators can leverage this study to increase their reach and capitalize on influencer marketing strategies. Additionally, this research provides consumers with market information, which will in turn encourage a degree of caution when faced with social media ads and marketing strategies designed by businesses.

Description

Cataloged from PDF version of thesis.
Includes bibliographical references (pages 54-55).
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.

Keywords

Online shopping, Social media, Prediction, Machine learning, Consumer pattern and behaviour

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