Abstract

Data analytics plays a significant role within the context of the digital business landscape, particularly concerning online sales, aiming to enhance understanding of customer behaviors in the online realm. We review the recent perspectives and empirical findings from several years of scholarly investigation. Furthermore, we propose combining computational methods to scrutinize online customer behavior. We apply the decision tree construction, GUHA (General Unary Hypotheses Automaton) association rules, and Formal concept analysis for the input dataset of 9123 orders (transactions) of sports nutrition, healthy foods, fitness clothing, and accessories. Data from 2014 to 2021, covering eight years, are employed. We present the empirical discoveries, engage in a critical discourse concerning these findings, and delineate the constraints inherent in the research process. The decision tree for classification of the year’s fourth quarter implies that the most important attributes are country, gross profit category, and delivery. The classification of the morning time implies that the most important attributes are gender and country. Thus, the potential marketing strategies can include heterogeneous conditions for men and women based on these findings. Analyzing the identified groups of customers by concept lattices and GUHA association rules can be valuable for targeted marketing, personalized recommendations, or understanding customer preferences.

中文翻译：

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通过决策树、GUHA关联规则和形式概念分析对在线消费者行为进行时间分析

摘要

数据分析在数字商业环境中发挥着重要作用，特别是在在线销售方面，旨在增强对在线领域客户行为的理解。我们回顾了几年学术调查的最新观点和实证结果。此外，我们建议结合计算方法来审查在线客户行为。我们对运动营养、健康食品、健身服装和配饰的 9123 个订单（交易）的输入数据集应用决策树构建、GUHA（一般一元假设自动机）关联规则和形式概念分析。采用2014年至2021年八年的数据。我们提出实证发现，对这些发现进行批判性讨论，并描述研究过程中固有的限制。今年第四季度分类的决策树意味着最重要的属性是国家、毛利润类别和交付。上午时间的分类意味着最重要的属性是性别和国家。因此，潜在的营销策略可以包括基于这些发现的男性和女性的异质条件。通过概念格和 GUHA 关联规则分析已识别的客户群体对于有针对性的营销、个性化推荐或了解客户偏好非常有价值。