Topline hotels are now shifting into the digital way in how they understand their customers to maintain and ensuring satisfaction. Rather than the conventional way which uses written reviews or interviews, the hotel is now heavily investing in Artificial Intelligence particularly Machine Learning solutions. Analysis of online customer reviews changes the way companies make decisions in a more effective way than using conventional analysis. The purpose of this research is to measure hotel service quality. The proposed approach emphasizes service quality dimensions reviews of the top-5 luxury hotel in Indonesia that appear on the online travel site TripAdvisor based on section Best of 2018. In this research, we use a model based on a simple Bayesian classifier to classify each customer review into one of the service quality dimensions. Our model was able to separate each classification properly by accuracy, kappa, recall, precision, and F-measure measurements. To uncover latent topics in the customer's opinion we use Topic Modeling. We found that the common issue that occurs is about responsiveness as it got the lowest percentage compared to others. Our research provides a faster outlook of hotel rank based on service quality to end customers based on a summary of the previous online review.
* Test Engineering and Management, Vol. 83: March/April 2020 * 5 pages, 6 figures, 5 tables
The availability of social media simplifies the companies-customers relationship. An effort to engage customers in conversation networks using social media is called Social Customer Relationship Management (SCRM). Social Network Analysis helps to understand network characteristics and how active the conversation network on social media. Calculating its network properties is beneficial for measuring customer relationship performance. Financial Technology, a new emerging industry that provides digital-based financial services utilize social media to interact with its customers. Measuring SCRM performance is needed in order to stay competitive among others. Therefore, we aim to explore the SCRM performance of the Indonesia Fintech company. In terms of discovering the market majority thought in conversation networks, we perform sentiment analysis by classifying into positive and negative opinion. As case studies, we investigate Twitter conversations about GoPay, OVO, Dana, and LinkAja during the observation period from 1st October until 1st November 2019. The result of this research is beneficial for business intelligence purposes especially in managing relationships with customers.
* The 11th SCBTII 2020 : Sustainable Collaboration in Business,
Technology, Information and Innovation presents Virtual International
Conference * 5 pages, 2 figures, 5 tables