Análise inteligente de mídias sociais para potencializar gestão de relacionamento com clientes

Abstract

The popularization of social media platforms has transformed the relationship between companies and consumers. Through these platforms, consumers and companies can interact, collaborate, create and share content in a simple, fast and cheap way. This interactive content production greatly influences purchasing decisions, as around two-thirds of consumers check the opinions and evaluations of products and services before purchasing them. Neglecting this phenomenon can negatively impact the daily operation of a company, but when managed well, this scenario represents a rich source of knowledge about customers, products, and services. In this context, Social CRM presents itself as a business strategy supported by processes and technologies that allow social media integration into traditional CRM systems. In this sense, the use of Social CRM is essential for integrating and using the knowledge extracted in all of the company’s processes and operational areas, which in practice allows for customer loyalty. However, the task of extracting knowledge from content is established as a non-trivial activity; the challenges of using large volumes of data, unstructured and from multiple sources, are examples of points that limit the full use by companies. In view of this, this dissertation presents a compendium of four articles that analyze points of improvement in Social CRM systems by optimizing the analysis of the effectiveness of business communications in publications on Facebook and the use of data from online complaints platforms to extract knowledge. These studies allowed the exploration and definition of the best approach for automating the analysis of company publications, as well as demonstrating the feasibility, applicability, and restrictions of using claims data to improve Social CRM strategies.