Analysing the Impact of video game on Consumer Engagement and Brand Loyalty: A Comparative Study of Traditional Marketing and Machine Learning -based Strategies

Abstract

In contemporary times, the video game industry has experienced remarkable growth, captivating a wide audience with its immersive offerings. Undoubtedly, it stands as a significant global contributor to revenue generation. This sector wields a considerable influence, drawing in individuals with sharp and innovative skills to foster the expansion of video games worldwide. Exploring the substantial profit generated by this sector, machine learning technologies have become instrumental in creating highly effective models that can analyze and forecast computer game sales well in advance. The realm of machine learning offers a diverse array of models for predicting future sales, employing techniques such as Linear and Multiple Regression, Random Forest, Decision Trees, Support Vector Machines, among others. Each of these approaches processes data using various mathematical concepts and formulas to estimate sales. The selection of an appropriate model depends on a thorough comparison of their accuracy and performance, considering the nature of the data. Model accuracy is commonly assessed by measuring the total number of correct predictions relative to all predictions made. As a key performance metric for evaluating the efficacy of the models, the R-square statistic is widely employed. Four algorithms have been tested on a selected dataset, and their performance has been compared to identify the most effective model for the given data.