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.