As retailers around the world increase efforts in developing targeted marketing campaigns for different audiences, predicting accurately which customers are most likely to churn ahead of time is crucial for marketing teams in order to increase business profits. This work presents a deep survival framework to predict which customers are at risk of stopping to purchase with retail companies in non-contractual settings. By leveraging the survival model parameters to be learnt by recurrent neural networks, we are able to obtain individual level survival models for purchasing behaviour based only on individual customer behaviour and avoid time-consuming feature engineering processes usually done when training machine learning models.

翻译：随着世界各地的零售商增加了针对不同受众的定向营销活动的力度，提前准确地预测哪些客户最有可能流失对于营销团队来说至关重要，以增加企业利润。本文提出了一种深度生存框架，用于预测在非合同设置下停止与零售公司购买的风险最大的客户。通过利用递归神经网络学习生存模型参数，我们能够仅基于个体顾客行为获得购买行为的个体级生存模型，并避免训练机器学习模型时通常进行的耗时的特征工程过程。