Abstract
This paper provides an overview of a user-friendly NPS-based Recommender System for driving business revenue. This hierarchically designed recommender system for improving NPS of clients is driven mainly by action rules and meta-actions. The paper presents main techniques used to build the data-driven system, including data mining and machine learning techniques, such as hierarchical clustering, action rules and meta actions, as well as visualization design. The system implements domain-specific sentiment analysis performed on comments collected within telephone surveys with end customers. Advanced natural language processing techniques are used including text parsing, dependency analysis, aspect-based sentiment analysis, text summarization and visualization.
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Notes
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NPS®, Net Promoter® and Net Promoter® Score are registered trademarks of Satmetrix Systems, Inc., Bain and Company and Fred Reichheld.
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Ras, Z.W., Tarnowska, K.A., Kuang, J., Daniel, L., Fowler, D. (2017). User Friendly NPS-Based Recommender System for Driving Business Revenue. In: Polkowski, L., et al. Rough Sets. IJCRS 2017. Lecture Notes in Computer Science(), vol 10313. Springer, Cham. https://doi.org/10.1007/978-3-319-60837-2_4
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