User-created content, like on-line product reviews, is a precious source of consumer penetration. Such unstructured data that was big is created in real-time, is readily reached, and contains messages consumers need managers to discover. Examining such data has potential to revolutionize market research and competitive analysis, but how can the messages be taken out? How can the vast amount of data be condensed into insights to help lead companies' strategy? We describe a nonproprietary technique that may be implemented by anyone with statistical training.
Latent Dirichlet Allocation (LDA) can analyze huge amounts of text and characterize the content as focusing on hidden traits in a particular weighting. Aggregating the content from numerous consumers permits US to understand what is, together, on consumers' minds, and from this we can infer what consumers care about. We may even highlight which attributes are seen positively or negatively. The value of this technique extends well past the CMO's office where they matter most: in the minds of consumers, as LDA can map the relative tactical locations of adversaries.
Uncovering the message from the mess of big data Case Study Solution
PUBLICATION DATE: January 15, 2016 PRODUCT #: BH722-PDF-ENG
This is just an excerpt. This case is about TECHNOLOGY & OPERATIONS
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