Predicting moviegoer return rates based on the A5 framework of the customer journey

As a cultural-artistic product, cinema plays a significant role in creating culture and filling the free time of people in society. The main problem of the research is to identify the contact points of the 5 stages (awareness, attraction, question, action, and support) affecting the customers' trip to the cinema, which can be effective in predicting the return rate of moviegoers. In the first step, using the content analysis method, 27 points were extracted as the contact points of moviegoers and placed in the 5A framework, and a research questionnaire was designed, the reliability and validity of which were confirmed using Cronbach's alpha and confirmatory factor analysis, respectively. In the second step, the artificial neural network model was used to predict the return rate of moviegoers. For this purpose, 450 questionnaires were randomly distributed among adult moviegoers in Gorgan city, of which 421 questionnaires were found to be usable. The application of neural networks showed that by using the identified contact points based on the 5A framework in the customer journey, the return rate of moviegoers can be predicted with an accuracy of 0.921. The results of the sensitivity analysis showed that the five stages of awareness (virtual advertisements), attraction (film advertising banner), question (opinions of other participants), action (type of personnel treatment), and support (repurchase) as the best predictors of consumer behavior. The results showed that the journey map of moviegoers is complex, highly sensitive, spiral, and continuous, where different touch points are touched by customers, which can affect their return rate.