机器学习即服务 (MLaaS) 提高营销成功率的方法

IF 3.4 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Ivo Pereira, Ana Madureira, Nuno Bettencourt, Duarte Coelho, M. Â. Rebelo, Carolina Araújo, Daniel Alves de Oliveira
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引用次数: 0

摘要

在数字时代,数据呈指数级增长,这导致了对以有效和高效的方式评估数据的创新方法的巨大需求。机器学习即服务(MLaaS)是一种服务类别,它为企业从数据中提取有价值的见解提供了巨大的潜力,同时降低了对大量专业技术知识的要求。本文探讨了 MLaaS 在营销应用领域的使用。在本研究中,我们全面分析了营销领域中的 MLaaS 实施及其优势。此外,我们还介绍了一个具有定制和扩展能力的平台,以满足市场营销的独特需求。我们介绍了三个模块:流失预测、一对二产品推荐和发送频率预测。当应用于市场营销时,所提出的 MLaaS 系统展现出了相当大的应用前景,例如在客户流失发生前自动检测、个性化产品推荐和发送时间优化。我们的研究表明,人工智能驱动的营销活动可以提高打开率和点击率。这种方法有可能提高企业的客户参与度和留存率,同时利用从消费者数据中获得的洞察力做出明智决策。这项研究为市场营销领域现有的 MLaaS 研究做出了贡献,并为企业提供了实用的见解,帮助企业利用这种方法增强其在以数据为导向的当代市场中的竞争优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Machine Learning as a Service (MLaaS) Approach to Improve Marketing Success
The exponential growth of data in the digital age has led to a significant demand for innovative approaches to assess data in a manner that is both effective and efficient. Machine Learning as a Service (MLaaS) is a category of services that offers considerable potential for organisations to extract valuable insights from their data while reducing the requirement for heavy technical expertise. This article explores the use of MLaaS within the realm of marketing applications. In this study, we provide a comprehensive analysis of MLaaS implementations and their benefits within the domain of marketing. Furthermore, we present a platform that possesses the capability to be customised and expanded to address marketing’s unique requirements. Three modules are introduced: Churn Prediction, One-2-One Product Recommendation, and Send Frequency Prediction. When applied to marketing, the proposed MLaaS system exhibits considerable promise for use in applications such as automated detection of client churn prior to its occurrence, individualised product recommendations, and send time optimisation. Our study revealed that AI-driven campaigns can improve both the Open Rate and Click Rate. This approach has the potential to enhance customer engagement and retention for businesses while enabling well-informed decisions by leveraging insights derived from consumer data. This work contributes to the existing body of research on MLaaS in marketing and offers practical insights for businesses seeking to utilise this approach to enhance their competitive edge in the contemporary data-oriented marketplace.
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来源期刊
Informatics
Informatics Social Sciences-Communication
CiteScore
6.60
自引率
6.50%
发文量
88
审稿时长
6 weeks
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