操作化
多样性(政治)
漏斗
计算机科学
采购
集合(抽象数据类型)
旅游
消费者行为
隐马尔可夫模型
产品(数学)
营销
数据科学
启发式
计量经济学
广告
数据挖掘
人工智能
业务
数学
地理
工程类
社会学
机械工程
哲学
几何学
考古
认识论
人类学
程序设计语言
操作系统
作者
Anat Goldstein,Gal Oestreicher-Singer,Ohad Barzilay,Inbal Yahav
标识
DOI:10.25300/misq/2022/15524
摘要
The conversion funnel is a model describing the stages consumers go through in their journey toward a purchase. This journey often lasts several days to weeks and can include multiple visits to a seller’s website. A large body of literature has focused on using observable search patterns to identify consumers’ hidden purchasing stages and to estimate their likelihood of conversion. We propose a novel set of measures to better reveal the consumer’s hidden stage in the funnel. These measures are based on the diversity of the searches that a customer engages in while browsing an e-commerce website, and they include not only the number of different products that are searched for, but also measures that rely on unobserved similarities among products, captured in a product network (in which products are assumed to be “similar” if they are frequently co-searched). We operationalize and evaluate our proposed measures using a large-scale dataset from a medium-sized tourism website used for comparing and booking flights. We estimate a hidden Markov model to show that our proposed diversity measures are associated with progress in the funnel and consumers’ conversion likelihood. Specifically, we show that consumers go through different distinguishable stages (states) in their journey, characterized by different values of our proposed diversity measures. To demonstrate the managerial and business implications of our theory, we show that incorporating search-diversity measures into a baseline prediction model significantly improves the model’s performance in predicting purchase likelihood and churn.
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