Life event prediction is an important tool for customer relationship management (CRM), because life events shift customers’ preferences towards different products and services. Existing life event research mainly uses cross-sectional data, whereas in the CRM field, incorporating longitudinal data is increasingly common. Because longitudinal data can capture the dynamics of customer behavior, opportunities arise to benchmark the power of longitudinal customer data for predictions of cross-sectional versus longitudinal life events. Therefore, this study compares statistical and machine learning (SaML) classifiers, such as logistic regression, random forest, and XGBoost, with long- and short-term memory networks (LSTM), using data represented in both cross-sectional and longitudinal setups for life event prediction. Through a real-life longitudinal customer data set from a European bank, the authors represent the longitudinal data in a cross-sectional data format, using featurization in the form of aggregation. The available data cover 42 end-of-month snapshots for 760,438 unique customers. For marketing decision-making literature, this article (1) introduces three novel life events (i.e., primary, secondary, and rental residence purchases) to life event predictions; (2) offers guidance for how to leverage longitudinal customer data, according to the comparison of various featurization approaches and benchmarking SaML classifiers against LSTM; and (3) clarifies the importance of features and timing for improving marketing decision-making dynamically. The results show that aggregating features over time is preferable as a featurization approach for cross-sectional modeling using SaML classifiers. Furthermore, LSTM can capture behavioral changes over time, unlike SaML classifiers. It also performs significantly better than SaML classifiers on the area under curve and F1 metrics. Insights into the uses of integrated gradients reveal that feature importance changes over time. An integrated gradients method can assist decision-makers in their efforts to plan effective communication with customers in advance, such as by allocating more resources to customers who exhibit high probabilities of a particular life event occurrence.

中文翻译：

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通过人生事件预测改进决策：金融服务行业的案例研究


生活事件预测是客户关系管理 （CRM） 的重要工具，因为生活事件将客户的偏好转向不同的产品和服务。现有的生活事件研究主要使用横断面数据，而在 CRM 领域，结合纵向数据越来越普遍。由于纵向数据可以捕捉客户行为的动态，因此有机会对纵向客户数据在预测横截面与纵向生活事件的能力方面进行基准测试。因此，本研究将统计和机器学习 （SaML） 分类器（如逻辑回归、随机森林和 XGBoost）与长期和短期记忆网络 （LSTM） 进行了比较，使用横截面和纵向设置中表示的数据进行生命事件预测。通过来自一家欧洲银行的真实纵向客户数据集，作者使用聚合形式的特征化，以横截面数据格式表示纵向数据。可用数据涵盖 760,438 个独特客户的 42 个月末快照。对于营销决策文献，本文 （1） 介绍了生活事件预测的三种新颖的生活事件（即主要、次要和出租住宅购买）;（2） 根据各种特征化方法的比较和对 SaML 分类器与 LSTM 的基准测试，为如何利用纵向客户数据提供指导;（3） 阐明功能和时间对动态改进营销决策的重要性。结果表明，作为使用 SaML 分类器进行横截面建模的特征化方法，随时间聚合特征是更可取的。 此外，与 SaML 分类器不同，LSTM 可以捕获随时间推移的行为变化。它在曲线下面积和 F1 指标上的表现也明显优于 SaML 分类器。对集成梯度用途的见解表明，特征重要性会随时间而变化。集成梯度方法可以帮助决策者提前规划与客户的有效沟通，例如将更多资源分配给表现出特定生活事件发生可能性较高的客户。