Consumer preference analysis: A data-driven multiple criteria approach integrating online information
Consumer preference analysis: A data-driven multiple criteria approach integrating online information
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DOI：
10.1016/j.omega.2019.05.010
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
Mengzhuo Guo;Xiuwu Liao;Jiapeng Liu;Qingpeng Zhang
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文献类型：
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作者：
Mengzhuo Guo;Xiuwu Liao;Jiapeng Liu;Qingpeng Zhang
多标准方法可以帮助产品经理了解电子商务环境下的消费者偏好。消费者偏好分析解释了产品的哪些方面会影响以及它们如何影响消费者的购买决定。这个问题在电子商务平台中发挥着重要作用，因为它与广告，推荐和促销等营销决策相关。在这方面，我们提出了一种数据驱动的多标准决策辅助（MCDA）方法来整合在线信息，如明确的（例如，评论和评级）和隐式（例如，点击和购买）的反馈。然而，MCDA方法提出了一个关键的挑战，即使是经验丰富的产品经理也很难预先定义评估产品的标准。为了解决这个问题，我们提出的方法首先利用文本挖掘技术，以帮助产品经理确定的标准，然后确定和收集的相对重要性的标准和它们的值。给定标准信息，我们使用一个抽样过程来提供两个指数，消费者偏好指数和排名接受指数。第一个指数有助于确定产品配对比较的优先顺序，而第二个指数有助于为首次注册的消费者得出默认排名列表。我们记录了消费者所看到的产品，并以两两比较的形式生成他们的偏好信息，以便在聚合-分解范式中进行分析。我们还提供了一个代表性的价值函数，以帮助产品经理深入了解偏好。最后，我们描述了一个真实世界的应用程序，包括产品经理和消费者利用电子商务平台上的建议的方法，采取一个大的一步，以帮助更现实和数据驱动的多标准决策。
Multiple criteria approaches can assist the product manager to know the consumer preferences in the context of e-commerce. Consumer preference analysis explains what aspects of a product affect and how they affect a consumer’s purchasing decision. This issue plays an important role in e-commerce platforms from its relevance in marketing decisions such as advertisements, recommendations and promotions. In this regard, we propose a data-driven multiple criteria decision aiding (MCDA) approach to integrate online information, such as explicit (e.g., reviews and ratings) and implicit (e.g., clicks and purchases) feedback from consumers. However, MCDA approaches present a critical challenge that even an experienced product manager could find it difficult to pre-define the criteria on which a product is evaluated. To address this issue, our proposed approach first utilizes text-mining techniques to assist the product manager identify the criteria, and then determines and collects the relative importance of the criteria and their values. Given the criteria information, we use a sampling process to provide two indices, the consumer preference index and rank acceptability index. The first index helps in prioritizing the pairwise comparisons of products, while the second one helps in deriving a default ranking list for first-time-registered consumers. We record the products viewed by consumers and generate their preference information in the form of pairwise comparisons for analyses within an aggregation-disaggregation paradigm. We also provide a representative value function to help the product manager gain insight into the preferences. Finally, we describe how a real-world application including the product manager and consumers exploits the proposed approach on an e-commerce platform to take a large step toward aiding more realistic and data-driven multiple criteria decision making.