Wireless network personalization is an emerging technology that has considerable potential to achieve the ultimate balance between resource allocation and user satisfaction. One of the main enablers of personalized networks is the continuous monitoring and prediction of dynamic user satisfaction levels in various contexts. Accurate satisfaction prediction requires a lot of data, and unfortunately, data and the process of acquiring it are expensive. A closer look at user behavior and satisfaction levels reveal that certain users share certain behavioral similarities. A group of users who share similar user behavior and satisfaction patterns is referred to as a persona. Associating users with preexisting user personas will enable networks to provide highly personalized services with a minimal amount of data, thereby improving the efficiency of personalized networks. In this paper, we propose a novel big data-driven framework to predict user personas in personalized wireless networks. Also, we conduct a comprehensive study to investigate the impact of different amounts of data and confidence levels on the performance of the overall framework. Finally, using a simulated personalized wireless network, we compare the behavior of different personas in terms of the amount of saved resources and achieved satisfaction levels.