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首页> 外文期刊>Review of Managerial Science >Predicting customer quality in e-commerce social networks: a machine learning approach
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Predicting customer quality in e-commerce social networks: a machine learning approach

机译：预测电子商务社交网络中的客户质量：一种机器学习方法

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摘要

The digital transformation of companies is having a major impact on all business areas, especially marketing, where audiences are most volatile and loyalty is at its scarcest. Many large retail brands try to keep their client base interested by becoming partners in cashback websites. These websites are based on a specific type of affiliate marketing whereby customers access a wide range of merchants and obtain financial rewards based on their activities. Besides using this mix of traditional marketing strategies, cashback websites attract new target customers and increase existing customers' loyalty through recommendations, using a word-of-mouth marketing strategy built on economic incentives for users who refer others to these sites. The literature shows that this strategy is one of the major areas of success of this business model because customers who join following recommendation are more active and are therefore more profitable and loyal to the brand. Nevertheless, the new users who are referred to these sites vary considerably in terms of the number of transactions they make on the site. This study advances research on the design of recommendation-based digital marketing strategies by providing companies with a predictive model. This model uses data science, including machine learning methods and big data, to personalize financial incentives for users based on the quality of the new customers they refer to the cashback website. Companies can thus optimize and maximize the return on their marketing investment.
机译：公司的数字化转型对所有业务领域（尤其是市场营销）产生重大影响，在这些市场中，受众最不稳定，忠诚度最薄弱。许多大型零售品牌试图通过成为现金返还网站的合作伙伴来保持其客户群的兴趣。这些网站基于特定类型的联属网络营销，因此客户可以访问各种各样的商家并根据其活动获得经济回报。除了使用传统的营销策略组合之外，现金返还网站还利用推荐的口碑营销策略，通过推荐他人的口碑营销策略吸引新的目标客户，并通过推荐来提高现有客户的忠诚度。文献表明，该策略是该业务模式成功的主要领域之一，因为按照建议加入的客户更加活跃，因此更有利可图，并忠于品牌。但是，在这些站点上进行交易的新用户在这些站点上进行交易的数量差异很大。本研究通过为公司提供预测模型来推进基于推荐的数字营销策略设计的研究。该模型使用数据科学，包括机器学习方法和大数据，根据他们引用现金返还网站的新客户的质量来个性化针对用户的财务激励措施。公司因此可以优化和最大化其营销投资的回报。

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