Speaking with Actions - Learning Customer Journey Behavior

To provide intelligent care, effortless experience and promote customer loyalty, it is essential that companies understand customer behavior and predict customer needs. Customers "speak" to companies through a sequence of interactions across different care channels. Companies can benefit from listening to this speech. We use the term customer journey to refer to the aggregated sequence of interactions that a customer has with a company. Most existing research focuses on data visualization, descriptive analysis, and obtaining managerial hints from studying customer journeys. In contrast, the goal of this paper is to predict future customer interactions within a certain period based on omni-channel journey data. To this end, we introduce a new abstract concept called "action" to describe customers' daily behavior. Using LSTM and DNN, we propose a systematic two-step framework based on omni-channel care journey data and customer profile data. The framework enables us to perform "action embedding", which learns vector representations of actions. Our framework predicts whether or not a customer will contact in the time period directly following the recent contacts. Comparing the performance on large-scale real datasets to other machine learning techniques such as logistic regression and random forest, our approach yields superior results. In addition, we further cluster the action embedding learned by our model and investigate the intrinsic properties of customer behavior.