The information obtained from customers' feedback can help hotel managers improve their provided services in a targeted manner according to customers' expectations. Besides, other customers consider online hotel scores an efficient tool for quickly evaluating the quality of hotels' services. Therefore, a higher online score indicates customer satisfaction and would lead to more bookings, price acceptance, and higher financial performance. In this article, we extracted the shortcomings related to hotel attributes utilizing a novel methodology that comprises machine learning algorithms, text mining, and a combination of customers' comments and scores. Then we examined the quantitative effect of fixing these problems on hotels' online scores. Furthermore, considering the origin of the problems, the cost required for fixing them, and the quantitative effect of solving them on improving the hotels' online scores, we provided some prescriptions for hotel managers as the last phase of business analytics. This model and its resulting prescriptions can be used to increase hotels' online scores significantly by improving service quality at the lowest cost. Finally, to describe the most important attributes, we used The Nordic European School of thought and classified them based on the technical and functional dimensions of Gronroos' service quality model.