How to use big data to stop customer churn

By Allen Bernard, CIO |  IT Management, Analytics

That data, combined with transactional information, gives companies a good picture of an individual customer's value, de Roos says. Add sentiment analysis from the IBM Infosphere Streams Twitter API and the MicroStrategy Wisdom engine, which tracks the 15 million "Likes" of some 65,000 Facebook users and their friends, and you start to get a much clearer picture of what that customer thinks of you and, just as importantly, your competition.

Customers Tweeting, blogging and liking things on Facebook simultaneously accumulate an "influence score" and engage with your brand, says Wilson Raj, global customer intelligence director for SAS. These "digital traces," in turn, can be harnessed to get a full view of that customer.

Since this level of customer experience analysis remains in its early days, only the most adventurous clients combining Twitter feeds with YouTube headers, aggregated sentiment analysis from Web-generated data sets and the blogosphere and wrapping it all in a natural language processing (NLP) engine in order to deem a customer happy or sad. As these technologies mature and corporate IT departments find the time, talent and resources, they'll catch up with this trend.

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They'll have to, says Rita Sallam, Gartner's BI analyst and research vice president. "Advanced analytics must be more pervasive to deliver significant value and competitive advantage to an organization," Sallam wrote in her February report, Advanced Analytics: Predictive, Collaborative and Pervasive. "To date, use of tools and processes for building advanced analytic applications and deriving and consuming insights have been limited to a small number of highly trained and experienced statisticians, analysts and operations research professionals."

Moving these tools into the hands of line-of-business users may not be as hard as you would imagine, says IBM's Director of Emerging Technologies David Barnes. "The cost of getting started with this isn't that great. That's the cool part of these massively scalable systems. I can start on my MacBook, decide I like it, take that exact same code and spread it across 100 servers."

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Originally published on CIO |  Click here to read the original story.
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