User-Specific Loyalty Measure and Prediction Using Deep Neural Network From Twitter Data

Abstract

Analyzing the social media data posted by users reveals insights about the business such as customers’ sentiment, limitations, improvements in services, users’ expectations, customer loyalty, and satisfaction. Customer satisfaction and loyalty are measured using net promoter score (NPS), which is based on a survey and often lacks the background of customer context. Several recent methods, net brand reputation (NBR) and net sentiment score (NSS), overcome NPS limitations and utilize social media data to gauge customer loyalty. The NBR and NSS also have the limitation that they do not provide the user-level score. In social media, existing user has an influence on a new user, which cannot be estimated in NBR and NSS. The main contribution of this research is developing novel user-and product-level metrics, which measure the influence, satisfaction, and customer loyalty. User sentiment score (USS) measures customer loyalty, overall influence factor (OIF) computes influence factor, effective influence on user (EIU) gives the effective impact on a new user, and satisfaction score (SATS) estimates satisfaction using user tweets. Social data influence score (SDIS) is proposed to measure the combined impact of user satisfaction and influence. We prepare a Twitter dataset of five popular online learning platforms: Coursera, Udemy, Udacity, Khanacademy, and edX, and analyzed our proposed metrics on them. Prediction of USS into loyal and not loyal customers is carried out using Twitter data and dense neural networks (DNNs). The performance of DNN is compared with random forest classifier (RFC), eXtreme gradient boosting (XGB), and support vector classifier (SVC). DNN gave the highest classification of 98.62% on tenfold cross validation.