Abstract
In this book, a data-driven user-friendly NPS-based recommender system for improving customer loyalty was presented. The first version of the system (CLIRS) was built based on both structured and unstructured data of customer feedback. The structured data was used to mine for actionable patterns and the unstructured data for the associated meta actions that act as triggers for action rules. Based on the triggering the overall impact on NPS could be calculated for different combinations of meta-actions. The second proposed version of the recommender system (CLIRS2) was built solely based on text customer feedback. The work included changing the processes (algorithm) within the first version of the system, as well as adding a step of building a numerical decision table from the text, based on detecting the numerical values of sentiment polarity towards certain aspects of the service. This chapter concludes work done within this project and well as depicts plans for the future developments.
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Tarnowska, K., Ras, Z.W., Daniel, L. (2020). Conclusions. In: Recommender System for Improving Customer Loyalty. Studies in Big Data, vol 55. Springer, Cham. https://doi.org/10.1007/978-3-030-13438-9_10
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DOI: https://doi.org/10.1007/978-3-030-13438-9_10
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Publisher Name: Springer, Cham
Print ISBN: 978-3-030-13437-2
Online ISBN: 978-3-030-13438-9
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