Increasingly, on-demand nature of customer interactions put pressure on companies to build real-time campaign management systems. Instead of having managers to decide on the campaign rules, such as, when, how and whom to offer, creating intelligent campaign management systems that can automate such decisions is essential. In addition, regulations or company policies usually restrict the number of accesses to the customers. Efficient learning of customer behaviour through dynamic campaign participation observations becomes a crucial feature that may ultimately define customer satisfaction and retention. This paper builds on the recent successes of deep learning techniques and proposes a classification model to predict customer responses for campaigns. Classic deep neural networks are good at learning hidden relations within data (i.e., patterns) but with limited capability for memorization. One solution to increase memorization is to use manually craft features, as in Wide & Deep networks, which are originally proposed for Google Play App. recommendations. We advocate using decision trees as an easier way of mining high-level relationships for enhancing Wide & Deep networks. Such an approach has the added benefit of beating manually created rules, which, most of the time, use incomplete data and have biases. A set of comprehensive experiments on campaign participation data from a leading GSM provider shows that automatically crafted features make a significant increase in the accuracy and outperform Deep and Wide & Deep models with manually crafted features.

越来越多的客户互动的按需性质给公司带来了构建实时活动管理系统的压力。与其让经理决定活动规则，例如，何时、如何提供和向谁提供，创建可以自动执行此类决策的智能活动管理系统至关重要。此外，法规或公司政策通常会限制对客户的访问次数。通过动态的活动参与观察来有效学习客户行为成为一项至关重要的功能，可以最终定义客户的满意度和忠诚度。本文以深度学习技术最近的成功为基础，并提出了一个分类模型来预测客户对活动的反应。经典的深度神经网络擅长学习数据中的隐藏关系（即，模式），但记忆能力有限。增加记忆的一种解决方案是使用手动制作功能，如Wide & Deep网络，最初是为 Google Play 应用程序提出的。建议。我们提倡使用决策树作为一种更简单的挖掘高级关系的方法，以增强广域和深度网络。这种方法具有击败手动创建的规则的额外好处，这些规则在大多数情况下使用不完整的数据并且存在偏差。来自领先的 GSM 提供商的一系列活动参与数据的综合实验表明，自动制作的特征显着提高了准确性，并且优于具有手动制作特征的Deep和Wide & Deep模型。