Tasks critical to enterprise profitability, such as customer churn prediction, fraudulent account detection or customer lifetime value estimation, are often tackled by models trained on features engineered from customer data in tabular format. Application-specific feature engineering adds development, operationalization and maintenance costs over time. Recent advances in representation learning present an opportunity to simplify and generalize feature engineering across applications. When applying these advancements to tabular data researchers deal with data heterogeneity, variations in customer engagement history or the sheer volume of enterprise datasets. In this paper, we propose a novel approach to encode tabular data containing customer transactions, purchase history and other interactions into a generic representation of a customer's association with the business. We then evaluate these embeddings as features to train multiple models spanning a variety of applications. CASPR, Customer Activity Sequence-based Prediction and Representation, applies Transformer architecture to encode activity sequences to improve model performance and avoid bespoke feature engineering across applications. Our experiments at scale validate CASPR for both small and large enterprise applications.
翻译:对企业赢利至关重要的任务,如客户量预测、欺诈性账户检测或客户终生价值估计,往往通过以表格形式根据客户数据设计的特征而培训的模型来解决。具体应用特点工程随着时间推移而增加开发、操作和维护费用。最近的代表性学习进展为简化和概括各种应用的特征工程提供了机会。在将这些进展应用于表格数据研究人员时,涉及数据差异性、客户参与历史的变化或企业数据集的纯量。在本文件中,我们提出一种新办法,将包含客户交易、购买历史和其他互动的列表数据编码成客户与企业联系的通用代表。然后,我们将这些嵌入作为培训多种应用模型的特征加以评价。CASPR、客户活动序列预测和演示,应用变换器结构来编码活动序列,以改进模型性能,避免在各种应用中说特色工程。我们在规模上进行的实验验证了大小企业应用程序的CASPR。