A dynamic understanding of customer behavior processes based on clustering and sequence mining

•The dynamics of data points is studied based on clustering and sequence mining.•A general methodology based on different algorithms is proposed.•Visual and statistical approaches are used in order to obtain comprehensible results.•Prior knowledge is introduced in order to guide the clustering algorithms.•The framework is applied to a real-life case of an event organizer.

In this paper, a novel approach towards enabling the exploratory understanding of the dynamics inherent in the capture of customers’ data at different points in time is outlined. The proposed methodology combines state-of-art data mining clustering techniques with a tuned sequence mining method to discover prominent customer behavior trajectories in data bases, which — when combined — represent the “behavior process” as it is followed by particular groups of customers. The framework is applied to a real-life case of an event organizer; it is shown how behavior trajectories can help to explain consumer decisions and to improve business processes that are influenced by customer actions.