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Social Analytics: Your Next Strategic Priority?

By   /  October 30, 2010  /  No Comments






Photo credit: Flickr/Hamed Parham

If your business hasn’t yet begun exploring how it can better understand and respond to the thoughts and opinions about it that consumers share with the world on social media, it may not be long before it does.

Gartner recently released its list of the top ten strategic technologies for 2011, and among the categories on that list was social analytics. The research firm describes social analytics as including techniques ranging from social filtering to social-network analysis to sentiment analysis and social-media analytics.

Those categories – or at least a fair number of the offerings falling into them – owe a lot of their existence to semantic web technologies and standards, from NLP to RDF. As Gartner sums it up, “social network analysis tools are useful for examining social structure and interdependencies” and “involves collecting data from multiple sources, identifying relationships, and evaluating the impact, quality or effectiveness of a relationship.”

That’s a pretty apt description of solutions such as smartRealm, which delivers Social Network Authority and Prestige (SNAP) scores via a semantic web API and uses its SocialML ontology for representing social networks in context. Its early adopter program counts among its users marketing agencies that work with brands and product companies, whose customers need to justify their spend on social media and conversational marketing campaigns, and brand companies that have multiple channels on social media “who just want to know their top ten unpaid loyal army that are spread on their networks,” smartRealm co-founder and executive vp Dr. Yasr Bishr told The Semantic Web Blog in the spring.

Vendors See The Opportunity

Vendors are pressing ahead to take advantage of the opportunity Gartner has identified. Clarabridge, for instance, this week released Version 4.2 of the Enterprise and Professional versions of its software, which now uses native Facebook, Twitter, and popular social media monitoring tools direct API connectors, so that users can quickly download data from these social media giant sources. It’s also added to its support for classification and basic NLP capabilities across languages with multilingual language detection and advanced linguistic support for Spanish that includes advanced sentiment analysis. 

Clarabridge CEO Sid Banerjee says that, as enterprises explore whether social analytics should be at the top of their list of strategic priorities, they should consider technical capabilities such as a product’s ability to integrate content from public as well as more proprietary content sources, and to integrate structured as well as unstructured data from social media and other sources. That way, “content can be analyzed alongside and correlated to customer, product, and experience attributes like purchase ID, customer  ID, date/time of experience, etc.,” he says.

Also important are content transformation capabilities, including how well the platform handles categorization and sentiment ambiguity, he says. Enterprises also should look to how it will meet up to their demanding needs, so platform/architecture scalability; role based and user based security to limit access to data; an analytics interface and support for advanced analytics using BI or predictive models out of the box; and the ability to distribute content  through an open API and integrate it into CRM, workflow management, or other business process solutions should also be considerations.

Not surprisingly, these are features Clarabridge touts for its solutions (recently, for instance, it integrated its solution through its APIs to center workforce optimization vendor Verint’s systems, as covered here). But it’s a fair point that as these products mature their ability more is going to be expected of them  to support enterprise-class expectations. And long-time enterprise business analytics players also are going to try hard to press their advantage with established customers. This week, for example, SAS also had an announcement in this space. It said that early next year it will have a new social media module, SAS Conversation Center, for its Social Media Analytics software that aims at managing customer engagement on Twitter, capturing, prioritizing and routing to customer suport relevant tweets to which it has applied analytics to discern sentiment and influence.

Also on the product update front: OpenMic, an on-demand web application for automatically collecting and analyzing relevant content about a company and reports consumer sentiment, topic trends, emerging alerts, and root-cause relationships, reports that its new version makes it easier to drill down into detailed insights from top-level summaries of consumers’ relevant social media chatter, conducting verbatim comment searches for specific words or phrases.

OpenMic now also features sentiment indicators, source icons for determining where comments are coming from; inline attributes for quickly determining comment details; and profile pictures, author link and thread URL for social sources, to make it easier to respond, the company says.

About the author

Jennifer Zaino is a New York-based freelance writer specializing in business and technology journalism. She has been an executive editor at leading technology publications, including InformationWeek, where she spearheaded an award-winning news section, and Network Computing, where she helped develop online content strategies including review exclusives and analyst reports. Her freelance credentials include being a regular contributor of original content to The Semantic Web Blog; acting as a contributing writer to RFID Journal; and serving as executive editor at the Smart Architect Smart Enterprise Exchange group. Her work also has appeared in publications and on web sites including EdTech (K-12 and Higher Ed), Ingram Micro Channel Advisor, The CMO Site, and Federal Computer Week.

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