The process of analysing opinion expressed about a brand, organization or product in churn management has taking a new dimension in recent times. A subjective opinion, which hitherto renders as positive or negative, can now be positive and negative. By this, the act of channelling an effective acquisition or retention strategy for customers becomes more complex with increasing overhead cost. More so, the increase in false positive and negative churn classification via sentiment analysis affects organizational knowledge, which oftentimes cause other societal challenges. As churn prediction analysis transits dramatically from local transactional data analysis, to social network content analysis for real time churn decision support, the paradoxical nature of opinion may render sentiment analysis as a tool for opinion mining less effective for decision support. This is because the sentiment analysis approach handles customers’ opinion as an independent entity; while oftentimes, the user opinion are used only once for defining the behavioural class of a customer. These processes neglect the relative exclusive association, and the influence of community members over one another in churn management. Here, the research present a Context Based Clustered Conversation Model for churn complexity management via indexed driven opinion within a community of social users. The essence is to cluster organized knowledge opinions that is scalable, incremental and contextual for appropriate churn management decision support tool.