With the fast growth of e-commerce and the emerging new retail trend—online and offline integration—it is important to recognize the target market and satisfy customers with different needs by analyzing their online search behaviors. Accordingly, we propose sequential search pattern analysis and clustering to analyze consumers’ search behavior throughout the entire shopping process from the perspective of consumer need-states. We seek to understand how recommendation functions (RFs) or popular non-RF web features help consumers to shop online from a need-state perspective. We adopt maximal repeat patterns (MRPs) and lag sequential analysis (LSA) to analyze the sequence of search paths and identify significant repeated search patterns. Furthermore, to investigate the behaviors of customers with different types of need-states, we analyze webpages related to RFs and non-RF features using clustering to connect the evaluation results of search patterns with page traversal behaviors. This yields four groups of consumers who browse for information, adopt recommendations, consult reviews, and conduct searches with different levels of goal-oriented or exploratory-based need-states. The results show that consumers with strong goal-oriented need-states have the simplest search paths compared to other groups, whereas exploratory-based consumers have the most complicated search paths. Furthermore, consumers with higher need-states tend to search directly, consult reviews carefully, and have stored sequential search patterns, whereas consumers with exploratory-based need-states tend to explore the categories of products and adopt product classification hierarchy as a pivot to explore web features and then adopt specific types of RFs. Interestingly, consumers in the review-consulting group all belong to the goal-oriented need-states type with strong knowledge-building behaviors compared to others. The results reveal that each group employs its own particular web features to facilitate the shopping process and we can identify consumer types based on shopping behavior in the early stage of shopping. This suggests that e-store sellers can refine web features and deploy marketing strategies tailored to the search patterns for different levels of need-states.

随着电子商务的快速发展和新兴零售趋势的出现（在线和离线集成），重要的是要识别目标市场并通过分析其在线搜索行为来满足具有不同需求的客户。因此，我们提出了顺序搜索模式分析和聚类，以从消费者需求状态的角度分析整个购物过程中消费者的搜索行为。我们试图了解推荐功能（RF）或流行的非RF网络功能如何从需求状态的角度帮助消费者在线购物。我们采用最大重复模式（MRP）和滞后顺序分析（LSA）来分析搜索路径的序列，并确定重要的重复搜索模式。此外，要调查具有不同类型需求状态的客户的行为，我们使用聚类分析将搜索模式的评估结果与页面遍历行为联系起来，从而分析与RF和非RF功能相关的网页。这样就产生了四类消费者，他们浏览信息，采纳建议，查阅评论并以不同级别的面向目标或基于探索的需求状态进行搜索。结果表明，与其他群体相比，具有强烈目标导向需求状态的消费者具有最简单的搜索路径，而基于探索性的消费者具有最复杂的搜索路径。此外，需求状态较高的消费者倾向于直接搜索，仔细查看评论并存储了顺序搜索模式，而具有基于探索性需求状态的消费者则倾向于探索产品类别，并以产品分类层次结构为中心来探索网络功能，然后采用特定类型的RF。有趣的是，评论咨询小组中的消费者都属于面向目标的需求状态类型，与其他人相比，他们具有较强的知识积累行为。结果表明，每个组都使用自己的特定网络功能来促进购物过程，并且我们可以在购物初期根据购物行为来识别消费者类型。这表明电子商店卖家可以优化网络功能并部署针对不同需求状态的搜索模式量身定制的营销策略。与其他人相比，评论咨询小组中的消费者都属于面向目标的需求状态类型，具有较强的知识积累行为。结果表明，每个组都使用自己的特定网络功能来促进购物过程，并且我们可以在购物初期根据购物行为来识别消费者类型。这表明电子商店卖家可以优化网络功能并部署针对不同需求状态的搜索模式量身定制的营销策略。与其他人相比，评论咨询小组中的消费者都属于面向目标的需求状态类型，具有较强的知识积累行为。结果表明，每个组都使用自己的特定网络功能来促进购物过程，并且我们可以在购物初期根据购物行为来识别消费者类型。这表明电子商店卖家可以优化网络功能并部署针对不同需求状态的搜索模式量身定制的营销策略。