Social networking platforms, such as WeChat and Facebook, increasingly become an important channel for advertising and transaction. Despite the growing importance of social network in facilitating social commerce, there has been limited research focusing on the dynamic implicit communities within the social network. This paper proposes a framework for gathering business intelligence from one popular social media platform in China (WeChat) by collecting and analyzing social network contents and consumer’s interaction networks. It is one of the first studies to our knowledge that identifies and analyzes implicit communities from social media platform for social commerce. We conduct case studies in a relationship-based online social group of WeChat. We first extract and analyze implicit communities, representing interactions among users and product vendors, from a large dataset. This is then combined with interaction-based content analysis to identify position of product vendors from these identified implicit communities. After examining the influence of network properties and community structures on consumer’s purchase, we propose a chat log-based content analysis to identify product-related information (including product attribute information, user experience information, social support information, and entertainment information). Finally, by combining those information and network structure, we build weighted implicit communities and calculate the influence of these weighted implicit communities on product vendors’ income. Our case studies demonstrate how to use the framework and appropriate techniques to effectively collect, extract, and analyze implicit communities related to the topics of interest, reveal novel patterns in the interactions and communities, and answer important business intelligence questions in the domains.