It is of utmost importance for marketing academics and service industry practitioners to understand the factors that influence customer satisfaction. This study proposes a novel framework to analyze open-ended survey data and extract drivers of customer satisfaction. This is done automatically via deep learning models for natural language processing. According to 11 drivers acknowledged by the marketing literature to determine customer experience, the data is cast into a multi-label classification problem. This expert system not only supports the automatic analysis of new data but also ranks the drivers according to their importance to various service industries and provides important insights into their applications. Experiments carried out using 25,943 customer survey responses related to 39 service companies in 13 different economic sectors show that the drivers can be identified accurately
"An Econometric Model for Assessing University Enrollment and Marketing Efforts". Intelligent Data Analysis, Vol. 21 (4).
We replicate the work of Schmitt et al. (2011) who find that referred customers are more loyal and valuable than customers acquired through other channels. While our results confirm that rewarded…
Yet most of these studies assume that social interactions do not change over time, even though actors in social networks exhibit different likelihoods of being influenced across the diffusion...
El año 2010 había comenzado mucho mejor. En este contexto, Subaru Chile, un actor del mercado con una cuota relativamente pequeña (anexo 1), decidió introducir un nuevo modelo: Subaru XV, un...
El ciclo de vida del destino turístico constituye uno de los temas de mayor relevancia en los análisis de competitividad turística. Muestra la evolución de un destino en el tiempo y puede...
In this monograph we examine the extent to which word-of-mouth communication (WOM) plays a complementary and/or substitute role with regard to advertising. A review of the existing literature...