Relationship management has been of strategic importance for businesses that are interested to evaluate the state of the relationship with the customer and if possible to migrate customers to better and more binding states. This work addresses the problem of estimating the relationship state of a customer and examining the migration policy of the customer, using social media analytics. We propose an innovative framework, where clustering, linguistic and emotional analytics are used to automatically assign users to relationship states. Our research is of multi-disciplinary nature, where we are using existing results from surveys on users’ behavior when mitigating states to verify the semantics of our metrics, showing that they follow similar behavior. Our results show that clustering users based on communication, emotions and perceived product mix can result in an automated assignment of users to states. Furthermore, trust, commitment and homophily are defined and our results show that users are migrating states influenced by these values. Our work provides data analytics metrics for businesses that will identify and address the problem of relationship management thus improving the overall users’ satisfaction using a data analytics approach.

对于希望评估与客户之间的关系状态，并尽可能将客户迁移到更好和更具约束力的状态的企业，关系管理一直具有战略重要性。这项工作解决了使用社交媒体分析来估计客户的关系状态并检查客户的迁移策略的问题。我们提出了一个创新的框架，该框架使用聚类分析，语言分析和情感分析来自动将用户分配给关系状态。我们的研究具有多学科性质，在缓解状态时，我们使用调查用户行为的现有结果来验证指标的语义，表明他们遵循相似的行为。我们的结果表明，基于交流对用户进行聚类，情绪和感知的产品组合会导致用户自动分配州。此外，定义了信任，承诺和同质性，我们的结果表明用户正在迁移受这些值影响的状态。我们的工作为企业提供数据分析指标，这些指标将识别并解决关系管理问题，从而使用数据分析方法提高总体用户满意度。