Customer Analytics Using Sentiment Analysis and Net Promoter Score

Abstract

In business, customer satisfaction with a product or service is essential. It is especially effective in campaigns to analyze customer sentiment and satisfaction with the brand or measure customer service quality. Nowadays, users can efficiently perform transactions such as shopping, ordering food and drink online, and then leave feedback on the company's e-commerce websites. Businesses want to analyze customers' opinions and feelings to determine users' sentiment and views towards a specific product or service. This study proposes a customer satisfaction analysis method based on sentiment analysis and net promoter score (NPS). First, a dataset consisting of 48,471 online reviews in Vietnamese on websites in the online food ordering service sector was collected. Next, the pre-processed data is put into experimental machine learning models to evaluate and select the best model. Experimental results show that the proposed method has an accuracy of up to 90%. Finally, NPS is calculated based on customer rating. The result is visualized on dashboards with critical information dimensions.