• 02/17/2012
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The Many Styles Of Social Media Analytics

Whether provided as self-service or with the backing of consultants, for marketing, CRM, or operations, social media analytics are snowballing in importance.
10 Cool Social Media Monitoring Tools
Slideshow: 10 Cool Social Media Monitoring Tools
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Why is social media analytics important? Let me count the ways. Actually, I'm not sure how to count them.

Last week, I presented on "5 Styles of Social Media Analytics" at a Virtual Enterprise 2.0 event, along with some other great speakers. The "5 Styles" label was supposed to be a working title that I would go back and update once I figured out what the right number was, but then I got distracted. Smart editors have also told me I should learn to love lists of 5 things or 10 things because they're traffic magnets. In this case, I still haven't figured out the right number, but it's more than 5.

I knew from my reporting that, although there are some common base technologies and techniques applied across all of social media analytics, there is great variation in the simplicity or sophistication of the options on the market, ranging from basic keyword filtering to advanced text mining and natural language processing. They can provide self-service or white-glove service. The basic principle of extracting trends or knowledge from social streams is being applied in many different ways, sometimes with industry-specific variations.

[ Why should Facebook matter to the enterprise? Read Facebook: The Database Of Wealth And Power. ]

Many of the social media listening platforms got their start providing public relations and marketing departments with next-generation "clipping services," monitoring blogs and social media for brand and product mentions, much as they had previously watched for mentions in the traditional media.

Market research applications treat social media as providing a variation on the kind of intelligence obtained from polls or focus groups, looking for marketing opportunities, threats to their reputation, and sometimes new product ideas. Customer support teams favor monitoring tools that provide some sort of support for "engagement," the ability to respond to a complaint or question aired in social media rather than letting it go unanswered. I'm intrigued by the possibilities for feeding issues identified through social media monitoring back into operations, as in my case study on Social Analytics Cracks Case Of The Jalapeno Cocktail.

"For organizations that talk about social media as it if were a single capability, this view is often a rude shock," Gary Angel, president of the consulting firm Semphonic, writes in the process of giving his own breakdown of the types of social media measurement and analytics. He comes up with six categories, each of which requires a different set of metrics and base technologies. It looks something like this:

-- Customer Support -> Operational Metrics
-- Public Relations -> Influence Categorization & Tracking
-- Campaigns -> Listening & Web Analytics
-- Communities -> Engagement & Attrition
-- Social CRM -> Customer Data Integration
-- Products & Customer Research -> Advanced Natural Language Processing

I also consulted with Forrester Research analyst Zach Hofer-Shall, who helped me out on a panel about big data and social analytics at Enterprise 2.0 Boston last year, and Marshall Sponder (aka WebMetricsGuru), the author of the book Social Media Analytics (McGraw-Hill, 2011).


re: The Many Styles Of Social Media Analytics

"You may think you are listening to the voice of the customer, but you may just be listening to a couple of irate Twitter followers." That idea of perspective is huge. A company I met with today has tools that allow companies to manually change, say, a comment flagged as neutral to one of high importance based on what the human knows versus what the analytics system knows. The system learns from that change and applies it moving forward. But with the volumes of data being dealt with and the vagaries of language and human nature, there are huge challenges involved with effectively making sense of data derived from social tools.

Deb Donston-Miller
Contributing Editor, The BrainYard