通过原型检测与反事实解释分析用户旅程，提升营销策略有效性
Analysis of Customer Journeys Using Prototype Detection and Counterfactual Explanations for Sequential Data
- 基于序列距离识别典型用户路径并可视化
- 预测购买概率，关键路径识别准确率显著提升
- 生成反事实建议路径，适合优化营销转化场景
近年来，全渠道平台的兴起使客户旅程分析成为营销策略制定的重要议题。然而，由于数据具有序列性且分析复杂，定量研究仍较为匮乏。本文提出一种三步法：首先定义序列间距离，识别并可视化代表性路径；其次基于该距离预测购买可能性；最后针对未购买序列，提出一种方法生成反事实解释，并推荐可提升购买概率的替代路径。通过问卷调查收集数据并分析，结果表明可有效提取典型路径，识别影响购买的关键环节。本方法有望支持各类营销活动的优化。
原文摘要 · Abstract (English)
Recently, the proliferation of omni-channel platforms has attracted interest in customer journeys, particularly regarding their role in developing marketing strategies. However, few efforts have been taken to quantitatively study or comprehensively analyze them owing to the sequential nature of their data and the complexity involved in analysis. In this study, we propose a novel approach comprising three steps for analyzing customer journeys. First, the distance between sequential data is defined and used to identify and visualize representative sequences. Second, the likelihood of purchase is predicted based on this distance. Third, if a sequence suggests no purchase, counterfactual sequences are recommended to increase the probability of a purchase using a proposed method, which extracts counterfactual explanations for sequential data. A survey was conducted, and the data were analyzed; the results revealed that typical sequences could be extracted, and the parts of those sequences important for purchase could be detected. We believe that the proposed approach can support improvements in various marketing activities.
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