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：全球零售商正加大针对不同受众开展精准营销的力度，因此提前准确预测最可能流失的客户群体，对营销团队提升商业利润至关重要。本文提出一种深度生存分析框架，用于预测非合约零售场景中面临购买终止风险的客户。通过利用循环神经网络学习生存模型参数，我们仅基于个体客户行为即可获得购买行为的个体级生存模型，从而避免了传统机器学习模型训练中耗时的人工特征工程流程。