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

A. Manimuthu, U. Udhayakumar, A. Cathrine Loura, Jose P Peter, Antony S Alexander
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Abstract

In contemporary times, the video game industry has experienced remarkable growth, captivating a wide audience with its immersive offerings. Undoubt-edly, 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 this sector, machine learning technologies have become instrumental in creating highly effective models that can analyse 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
分析电子游戏对消费者参与度和品牌忠诚度的影响:传统营销与基于机器学习的策略比较研究
在当代,电子游戏产业经历了显著的增长,以其身临其境的产品吸引了广大观众。毋庸置疑,它是全球创收的重要贡献者。该行业具有相当大的影响力,吸引了具有敏锐和创新技能的人才,促进了电子游戏在全球的发展。为了探索这一领域所产生的巨大收益,机器学习技术在创建高效模型方面发挥了重要作用,这些模型可以提前分析和预测电脑游戏的销售情况。机器学习领域采用线性和多元回归、随机森林、决策树、支持向量机等技术,为预测未来销售额提供了多种多样的模型。每种方法都使用不同的数学概念和公式处理数据,以估算销售额。选择合适的模型取决于对这些模型的准确性和性能进行全面比较,同时考虑到以下性质
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