In recent years the demand for collective mobility services has registered a significant growth. In particular, the long-distance coach market has undergone an important change in Europe since FlixBus adopted a dynamic pricing strategy, providing low-cost transport services and an efficient and fast information system. This paper presents a methodology, called DA4PT (Data Analytics for Public Transport), for discovering the factors that influence travelers in booking and purchasing bus tickets. Starting from a set of 3.23 million user-generated event logs of a bus ticketing platform, the methodology shows the correlation rules between booking factors and purchase of tickets. Such rules are then used to train a machine learning model for predicting whether a user will buy or not a ticket. The rules are also used to define various dynamic pricing strategies with the purpose of increasing the number of tickets sales on the platform and the related amount of revenues. The methodology reaches an accuracy of 95% in forecasting the purchase of a ticket and a low variance in results. Exploiting a dynamic pricing strategy, DA4PT is able to increase the number of purchased tickets by 6% and the total revenue by 9% by showing the effectiveness of the proposed approach.