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Bio: ​Seppe vanden Broucke received a PhD in Applied Economics at KU Leuven, Belgium in 2014 after obtaining a Master’s degree (magna cum laude) in Busi​ness Economics: Information Systems Engineer from the same institution. Currently, Seppe is working as a postdoctoral researcher at the department of Decision Sciences and Information Management at KU Leuven. His research interests include business data mining and analytics, machine learning, process management, process mining. His work has been published in well-known international journals and presented at top conferences.​

Your Customers are not a Static Picture: A Dynamic Understanding of Customer Behavior Processes Based on Self Organizing Maps and Sequence Mining​

Abstract:​ Various data mining techniques have been devised in the quest for knowledge discovery in data from an exploratory point of view. Clustering techniques, for instance, combined with visualization techniques, allow analysts to get fast insights into the data they are confronted with. When executed at one specific moment in time, however, the aforementioned techniques offer a static picture describing the composition of the data set at hand based on certain patterns derived from the attributes characterizing the instances in this data set. We present an approach aiming to provide insights into the dynamics associated with the items represented in the data base, hence recording a ‘‘movie’’ of the data set instead of static pictures at specific points in time. We do so based on a two-step clustering approach, incorporating both self-organizing maps and k-means that will generate trajectory vectors used as input for a sequence mining technique. The proposed methodology combines these methods to discover prominent customer behavior trajectories in data bases, which together help analysts to understand the behavior process as it is followed by particular groups of customers. We have applied our technique in the context of a market-leading ticketing service provider. From a business perspective, understanding the dynamics of customer behaviors is a logical next step for companies applying segmentation techniques to understand their customers since, by definition, they may not stay indefinitely in the same segments. Capturing these movements becomes then a crucial objective which can only be achieved if comprehensible techniques are proposed. With this in mind, the step-wise visual approach proposed in this work aims not only at identifying the movements but also at reporting them in a way comprehensible for end-users.​

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