User Friendly NPS-Based Recommender System for Driving Business Revenue

摘要

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.