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Analyzing Customer Satisfaction Through Online Reviews Using Topic Modeling and Linguistic Model: A Study of a Hotel Chain in Madrid
(Springer, 2025) Shu, Ziwei; Sánchez Figueroa, María Cristina; Springer
Abstract With the widespread use of social media and review platforms, Electronic Word of Mouth (eWOM) has a greater reach and influence than traditional Word of Mouth (WOM), becoming a vital information source for customers and companies. Online reviews, a representative type of eWOM, are essential for guiding customer hotel choices and helping hotel managers gain valuable insights for necessary improvements. Mining online reviews and sentiment analysis are popular research areas, where sentiment analysis typically classifies the overall sentiment of customer reviews as positive, neutral, or negative. This study presents an approach for analyzing hotel customer satisfaction using the Latent Dirichlet Allocation (LDA) model and the 2-tuple linguistic model. The LDA model, widely used for topic modeling, is employed to identify the topics that matter more to customers and influence their satisfaction. The 2-tuple linguistic model is applied in sentiment analysis to address information loss when converting compound scores into sentiment labels, offering a clearer and more intuitive representation of sentiment while retaining the information. The proposed model analyzes 26,740 TripAdvisor reviews from twenty hotels in Madrid that are part of the same hotel chain. The results highlight the importance of considering variations in hotel scores and rankings across different languages, as identical rankings do not always correspond to the same sentiment scores from English-speaking and Spanish-speaking reviews, and vice versa. This study contributes to online reviews research and hotel management by offering a clearer and more accurate expression of customer review sentiment through 2-tuple values, and the most frequent topics from reviews.
Capítulo 53: Segmentación de clientes en el comercio minorista Fundamentos de ciencia de datos con R (McGraw-Hill Interamericana de España)
(McGraw-Hill Interamericana de España, 2023) Fierro Martín, Jaime; González Martínez, Rocío; Sánchez Figueroa, María Cristina; Fernández Avilés, Gema; Montero, José María
Los comercios minoristas (retailers) se mueven en un entorno turbulento y necesitan acercarse a sus clientes para asegurar su supervivencia. Su producto, o servicio, es nexo clave en el proceso de segmentación. En este contexto, conocer el perfil de los clientes permitirá detectar en qué momento de su ciclo de vida con la empresa se encuentran y desarrollar propuestas de valor que convengan en cada momento.
Segmentar se define como el proceso de dividir a los clientes, actuales o potenciales, en diferentes grupos o segmentos consistentes en individuos con características y niveles similares de interés (véase el Cap. 31 para una explicación detallada de las técnicas del clúster no jerárquico). Es un proceso creativo e iterativo con el fin de satisfacer con mayor acierto las necesidades de los clientes, proporcionando una ventaja competitiva y sostenible a la compañía. La segmentación viene dada por las necesidades de los clientes, no de la compañía, y debería ser revisada periódicamente. Este caso práctico de negocio está basado en un proyecto real impulsado por el departamento de marketing de una empresa del sector retail que necesitaba mejorar el conocimiento de sus clientes, agrupándolos en función de su comportamiento de compra. Los resultados obtenidos fueron clave para definir la estrategia de marketing relacional de la compañía.
An RFM model customizable to product catalogues and marketing criteria using fuzzy linguistic models: Case study of a retail business
(MDPI, 2021-08-04) G. Martínez, Rocío; A. Carrasco, Ramón; Sánchez Figueroa, María Cristina; Gavilán, Diana; MDPI
Gaining customer loyalty has become one of the main objectives of all companies. Retailers, especially the online ones, have the advantage of knowing their customers’ historical purchase data, which provides them with an understanding of the customers’ buying patterns. A widely-used tool in strategic marketing and customer loyalty is segmentation based on the traditional Recency, Frequency and Monetary (RFM) model. Subsequently, the fuzzy RFM model proved to be an improvement on the traditional RFM model. There has been a change in the retail customer profile, with the growth of a new cluster, the “One-Shot Customer”, new customers that buy from a retailer just once and never come back. In response to this change, the fuzzy RFM model has been modified to include a new dimension capturing Length or Duration. This study presents the new fuzzy RFMD model (Recency, Frequency, Monetary and Duration model), which can be used to better identify that new, large group of customers. The paper also provides a case study based on an e-commerce clothing retailer. Its customer database was segmented using the k-means algorithm and the Isolation Forest algorithm was applied to identify and correctly treat possible anomalies. The Customer Lifetime Value and the weights for the RFMD attributes were calculated by applying the Analytic Hierarchy Process (AHP) model. Results reveal the improvement that the weighted fuzzy RFMD model offers to retailers, enabling them to detect the One-Shot Customers and thus optimize their strategic marketing plans.