Exploring Unsupervised Learning as a Way of Revealing User Patterns in a Mobile Bank Application
Abstract: The purpose of this interdisciplinary study was to explore whether it is possible to conduct a data-driven study using pattern recognition in order to gain an understanding of user behavior within a mobile bank application. This knowledge was in turn used to propose ways of tailoring the application to better suit the actual needs of the users. In this thesis, unsupervised learning in the form of clustering was applied to a data set containing information about user interactions with a mobile bank application. By pre-processing the data, finding the best value for the number of clusters to use and applying these results to the K-means algorithm, clustering into distinct subgroups was possible. Visualization of the clusters was possible due to combining K-means with a Principal Component Analysis. Through clustering, patterns regarding how the different functionalities are used in the application were revealed. Thereafter, using relevant concepts within the field of human-computer interaction, a proposal was made of how the application could be altered to better suit the discovered needs of the users. The results show that most sessions are passive, that the device model is of high importance in the clusters, that some features are seldom used and that hidden functionalities are not used in full measure. This is either due to the user not wanting to use some functionalities or because there is a lack of discoverability or understanding among the users, causing them to refrain from using these functionalities. However, determining the actual cause requires further qualitative studies. Removing features which are seldom used, adding signifiers, active discovery as well as conducting user-tests are identified as possible actions in order to minimize issues with discoverability and understanding. Finally, future work and possible improvements to the research methods used in this study were proposed.
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