Abstract
The Net Promoter Score (NPS) is often used in customer experience programs for measuring customer loyalty. Increasingly more companies seek to automatically process millions of pieces of customer feedback from social media per month in order to estimate their NPS, leveraging advanced analytics like machine learning (ML) and natural language processing (NLP). Discovering trends and themes in customer interactions helps explain the NPS, empowering companies to improve products and customer experience. In this paper, we describe an end-to-end solution for NPS estimation and explanation from social media. The process includes sentiment analysis on user comments, estimating product information based on text semantics, grouping and tagging user comments for text discovery, and NPS explanation. The solution gives companies the capability to identify overall customer sentiment and common topics in a unified platform, allowing faster analysis and insights on NPS based on customer feedback.
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