When providing customers with a personalized shopping experience, there is tremendous value in understanding and applying social data shared by those consumers. Understanding this data and how best to generate business value from it is the core challenge of many businesses today. Friends, family, and experts alike influence consumers in their shopping preferences and purchase decisions. Yet, the ability of a business to analyze data on such influence, and recommend products and services that best respond to its customers' needs or aspirations, is typically limited by fragmented capabilities; a business relies heavily on the use of spreadsheets, manual market analysis, isolated software, or reactive messaging. This paper offers a solution to this fragmentary approach by introducing a social analytics platform for smarter commerce. This platform provides a holistic understanding of the customer by making use of social and enterprise data to present recommendations and related opinions, and to isolate influencers so as to ultimately provide customers with a personalized shopping experience. The functionality described in this paper is in the context of the retail industry but can be applied to other industries. The paper describes the architecture of the social analytics platform and the various analytics components currently implemented as part of the platform.
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