Software // Enterprise Applications
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10/11/2010
04:06 PM
Doug Henschen
Doug Henschen
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Social Networks Meet Customer Service

Text analytics technologies bring Twitter and Facebook streams into mainstream CRM workflows.

For better or worse, Twitter, Facebook and other social networks have been legitimized as channels for customer feedback and support.

It was only a matter of time, but technology vendors are joining a race to bring social network comments about brands, products and service experiences into the usual customer service workflows.

Attensity and Verint are among a handful of vendors now using text-analysis technologies to sort, mine and otherwise speed responses to Tweets, Facebook posts, blogs and other forms of social network feedback.

Text analysis is fast maturing into mainstream use, and there's no hotter use of the technology than sentiment analysis. Sentiment applications bring scalability and automation to the otherwise manual task of wading through high volumes of, say, customer surveys.

Marketers and brand managers need the aid of the technology to quickly detect broad trends while also responding to specific comments and complaints.

To date, most sentiment-analysis apps stand alone, but the trend is moving toward integration with enterprise applications and workflows. The Attensity and Verint applications are cases in point, and the degree of sophistication and automation they offer might surprise you.

Announced last week, Attensity's Respond for Social Media application complements and existing Attensity Respond app that was developed to handle incoming e-mail, faxes, text messages, phone call notes and transcripts, and transcribed mail. Messages coming in through these channels are analyzed with Attensity's text-mining algorithms so they can be sorted by topic category and then routed into appropriate queues for contact-center agents or specialists.

Attensity Respond for Social Media is much the same app for Twitter Tweets, Facebook posts, LinkedIn forums, blogs, online discussions and the metadata associated with online videos. One key difference is that the application itself is designed to look like and be used as a social media application.

Respond for Social Media users can monitor Tweets and posts through a familiar TweetDeck/CoTweet-like interface. A management console lets administrators set up customer-care queues and train the system to automatically sort messages. You simply drag and drop Tweets or posts onto categories such as "Wishes and Wants" for product development or service teams, "Intent to Purchase" for the sales team, and "Needs Help" for the support crew.

chart: How does your company monitor social networks?

Attensity's sorting feature spots intersections of topics, categories, keywords and content types. The more messages you drag and drop into a queue, the more accurate the software becomes; but you only need five training examples to launch automated sorting.

Attensity has an optional integration between Respond for Social Media and Rapleaf, a service that tracks social media handles (names used on Twitter, Facebook and the like) and associates them with known e-mail addresses. Integrations are also being developed for leading CRM systems, such as Salesforce.com and SAP CRM. Either way, companies will be able to automatically call up customer service records associated with Tweets or posts so agents can see relevant service histories and better respond to social media comments.

Once you enter into a social media dialog with customers, Respond for Social Media monitors the ongoing conversation. Comments and responses are nested social network style, so contact center agents can see the history of company contacts and customer responses.

It's not too hard to imagine other powerful capabilities. For example, Attensity says it's working ways to prioritize comments based on social media influencer status. So for instance, comments from people with thousands of followers could get immediate attention while those with few online friends might go to the back of the queue.

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