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A latent class segmentation analysis of e-shoppers

机译：电子购物者的潜在类别细分分析

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摘要

We apply a latent class modeling approach to segment web shoppers, based on their purchase behavior across several product categories. We then profile the segments along the twin dimensions of demographics and benefits sought. We show that benefits sought can provide more diagnostic information than mere descriptive demographic profiling. Our study has some interesting findings that shed light on consumer perceptions and behavior with respect to online commerce. First, consumers are more concerned about web attributes that are associated with perceived losses than with web attributes that consumers associate with gains. Second, compared to other online purchase-related attributes, getting the lowest price does not appear to be a very important attribute for web shoppers. This finding may also indicate that prices on web pages are somewhat similar, and consumers are moving on to other criteria to continue their evaluation process. Third, demographics do not discriminate between web buyers even though that has been the traditional focus with the Internet. Fourth, there is a large segment of web surfers who dislike buying on the Web; the predominant reason for this appears to be their perception about the security of sensitive information. This segment feels that not only is it the most important of all attributes for online commerce, but also that the Web does a very poor job on this attribute/benefit.
机译：我们根据网络购物者在多个产品类别中的购买行为，对网络购物者应用了潜在类别建模方法。然后，我们沿着人口统计和所寻求利益的双重维度来划分细分受众群。我们表明，所寻求的收益比单纯的人口统计特征描述能提供更多的诊断信息。我们的研究有一些有趣的发现，这些发现揭示了消费者对在线商务的看法和行为。首先，消费者更关注与感知到的损失相关联的网络属性，而不是消费者与收益相关联的网络属性。其次，与其他与在线购买相关的属性相比，对于网络购物者来说，获得最低价格似乎不是一个非常重要的属性。这一发现还可能表明网页上的价格有些相似，并且消费者正在转向其他标准以继续其评估过程。第三，尽管这一直是Internet的传统重点，但人口统计学不会区分网络购买者。第四，有很大一部分网民不喜欢在网上购物。造成这种情况的主要原因似乎是他们对敏感信息安全性的看法。该部分认为，这不仅是在线商务的所有属性中最重要的，而且Web在此属性/优点方面做得很差。

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