Customer Sentiment Analysis

Advanced analytics are all about understanding your customers better. Being able to understand the subjective feelings of your customers is what sentiment analysis is all about. Customer sentiment analysis, also called opinion mining, is the automated process of identifying emotions in digital interactions to glean how your customers feel about your products, services, or brand. This level of insight into customers allows brands to message their customers as fully-rounded people.

How Does Customer Sentiment Analysis Work?

Customer sentiment analysis leverages natural language processing (NLP) and advanced machine learning algorithms to detect patterns in text to classify customers feelings as positive, negative, or neutral.

Algorithms can go further by distinguishing opinions, whether they may be subjective or objective, comparative or direct, or, explicit or implicit. By applying customer sentiment analysis, unstructured data is turned into structured information.

How Do Companies Use Customer Sentiment Analysis to Improve CX?

This data on customer likes and dislikes, can be unified with other customer data and used by marketing, sales, product development, and customer service to develop more customer-centric products and services. Other use cases for customer sentiment analysis include brand strategy optimization, monitoring of brand reputation, and tracking customer sentiment over time.

Learn More About Customer Data Analytics

Amy Onorato
Amy Onorato
Amy Onorato is the Managing Editor of and Senior Content Marketing Manager at Treasure Data. Prior editorial and creative roles include journalism, content marketing and content strategy for CBSNewYork, Newsday, DMN, and Publicis Sapient.

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