Enterprises are increasingly using social media forums to engage with their customer online- a phenomenon known as Social Customer Relation Management (Social CRM). In this context, it is important for an enterprise to identify "influential authors" and engage with them on a priority basis. We present a study towards finding influential authors on Twitter forums where an implicit network based on user interactions is created and analyzed. Furthermore, author profile features and user interaction features are combined in a decision tree classification model for finding influential authors. A novel objective evaluation criterion is used for evaluating various features and modeling techniques. We compare our methods with other approaches that use either only the formal connections or only the author profile features and show a significant improvement in the classification accuracy over these baselines as well as over using Klout score. Part of this research was conducted during author's internship at IBM Research, India. Copyright © 2012, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.