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Social Intelligence: Three Steps to Implementing a Socially Enabled Business via Big Data


social intelligenceSocial-media technologies have fundamentally shifted the way people engage with one another and organizations. Social channels such as Facebook and Twitter have sparked an explosion of business-to-consumer and consumer-to-business interactions, in turn changing consumers' behavior and expectations about how they interact and transact with commercial enterprises.

Social-networking channels capture vast amounts of behavioral information that can be tied back to individuals to create highly valuable customer profiles. Enterprises that can leverage this social data to understand and engage their customers will gain a significant competitive advantage.

Social data is Big Data—voluminous, variegated and generated with high velocity. Recognizing the value of these sources of social data and leveraging them appropriately is a relatively new challenge for many organizations. I would like to define “social intelligence” as a practice that combines social-media data and traditionally sourced customer data with advanced analytics to yield deep insights that drive better marketing and overall business decisions.

Why invest in social intelligence? Key reasons include:

  • Improve customer focus through enhanced visibility and understanding of your customers.
  • Enhance brand reputation by monitoring, managing and responding to market sentiments, perceptions and trends.
  • Foster innovation by leveraging market insights from listening, analyzing and engaging with customers through social media.

As with any other Big Data, traditional BI approaches and tools struggle to analyze and integrate social-media data with other customer data sources, rendering key insights elusive to marketers and other decision makers.

Therefore, a social-intelligence effort will require rethinking and redesigning existing information management ecosystems. New analytical platforms, techniques, tools and governance processes are needed to unlock customer insights. Also required: new roles and skill sets that combine business acumen, data science and technology.

The implementation of a socially enabled business through Big Data includes three main steps:

1. Listen and Gather
The first step of a social-intelligence program is “listening” to consumer dialogue on social networks, sites and communities and collecting the data. You could invest in sophisticated software tools such as Radian6 that can automate and scale listening to huge volumes of interactions and store the data for further analysis. Enterprises can use these tools to scan the Web, listening to the voice of customers (and prospects). 

2. Analyze
The next step is analyzing the gathered information, most of which is opinion, to extract actionable meaning. You will need a new set of analytical tools driven by natural language processing (NLP) algorithms that assess user-generated content to identify the opinion or emotional state of a writer and the most recurrent themes. This is a complex undertaking because of the subjectivity and the context involved in gauging sentiments.

You will also need to integrate this data with other sources and services to create deeper insights that drive better decision making. For example, services such as Kred, PeerIndex and Klout measure the social-media influence of a digital persona based on criteria such as number of followers or total engaged network, or the likelihood that a recommendation will be acted upon.

3. Engage
The final step requires closing the loop by delivering insights back into customer engagement processes such as sales and marketing, thus enabling quick, decisive and appropriate actions. Social intelligence, combined with other customer intelligence, allows enterprises to engage in timely and relevant conversations with customers, and prospects, in their preferred channels.

To succeed in today’s "instantaneous" market, companies must make their social intelligence initiative a priority. In order to harness the true potential of social intelligence, companies must deploy a broad information management and analytics program that is tightly linked with the overall business strategy.  Enterprises that execute an effective social-intelligence strategy will gain a significant advantage by anticipating, understanding and meeting customer expectations.

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Tools such as Social Media Monitoring tools Radian6 give you the analysis of information that is outside any particular enterprise firewall and a general sense of trends emerging from the Social Media. Social Media Analytic tools such as Klout give you a score for a Social profile. However, for all this information to be meaningful to an enterprise, you need to combine it with your existing enterprise data assets. Think about the Inside-Out view that allows an organization to expand and augment what they know about their customers and use that customer level deep insights for engagement and customer experience. This requires a combination of the MDM capabilities with the ability to handle multi-channel interactions and Social data to create this augmented view of your current data assets.  
We do this at Reltio with our Convergence Hub, that allows us to combined Master Data with multi-channel transaction data and Social media interactions to create a complete view of the Customer. Reltio is built on a big data foundation that can handle volume, variety and velocity along with the complexity of such information. In most cases this information is highly inter-linked, think Social graphs constructed out of the combination of data coming from Internal enterprise applications, 3rd party data feeds and Social media sources that are all collated to calculate the Reach, Rank and Relevance of your customers.
Posted @ Friday, June 28, 2013 9:20 PM by Manish Sood
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