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Machine Learning in Marketing

机译：机器学习在营销中

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摘要

The widespread impacts of artificial intelligence (AI) and machine learning (ML) in many segments of society have not yet been felt strongly in the marketing field. Despite such shortfall, ML offers a variety of potential benefits, including the opportunity to apply more robust methods for the generalization of scientific discoveries. Trying to reduce this shortfall, this monograph has four goals. First, to provide marketing with an overview of ML, including a review of its major types (supervised, unsupervised, and reinforcement learning) and algorithms, relevance to marketing, and general workflow. Second, to analyze two potential learning strategies for marketing researchers to learn ML: the bottom-up (that requires a strong background in general math and calculus, statistics, and programming languages) and the top-down (focused on the implementation of ML algorithms to improve explanations and/or predictions given within the domain of the researcher's knowledge). The third goal is to analyze the ML applications published in top-tier marketing and management journals, books, book chapters, as well as recent working papers on a few promising marketing research sub-fields. Finally, the last goal of the monograph is to discuss possible impacts of trends and future developments of ML to the field of marketing.
机译：在营销领域中尚未感到强烈地感受到人工智能（AI）和机器学习（ML）在许多社会中的广泛影响。尽管短缺，但ML提供了各种潜在的好处，包括为普遍化科学发现的概括应用更强大的方法。试图减少这种缺口，这本专着有四个目标。首先，通过ML的概述提供营销，包括审查其主要类型（监督，无监督和强化学习）和算法，与营销相关，以及普通工作流程。其次，分析了营销研究人员的两个潜在学习策略，以学习ML：自下而上（这需要一般数学和微积分，统计和编程语言的强大背景）和自上而下（专注于实现M1算法的实现改进研究人员知识的领域内给出的解释和/或预测）。第三次目标是分析在顶层营销和管理期刊，书籍，书籍章节中发布的ML申请，以及一些有前途的营销研究子领域的最近工作文件。最后，专着的最后一个目标是讨论趋势和将ML的未来发展的可能影响到营销领域。

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