Social Media Analytics: How our approach blends the best analytics technology

It’s been some time but I remember reading a special report resulting from a collaborative effort between MIT Sloan Management Review and the IBM Institute for Business Value called Analytics: The New Path to Value. At this point in the evolution of social media analytics it may seem quite dated as the document is from October 2010 but it was supposed to be somewhat forward looking as it emphasized the importance of shoring up a company’s analytics capabilities.

It’s been some time but I remember reading a special report resulting from a collaborative effort between MIT Sloan Management Review and the IBM Institute for Business Value called Analytics: The New Path to Value. At this point in the evolution of social media analytics it may seem quite dated as the document is from October 2010 but it was supposed to be somewhat forward looking as it emphasized the importance of shoring up a company’s analytics capabilities. One heading in particular really caught my eye and I have continued to think and dwell on it, as I simply did not understand what type of organization would respond to the author’s questions this way.

Here it is:

“Data is Not the Biggest Obstacle” was the heading and it is true. There is a lot of data out there, volumes and reams of it. But it is the following content, that really made me think “huh, really?”.

“Despite popular opinion, getting the data right is not a top challenge organizations face when adopting analytics. Only about one out of five respondents in our survey cited concern with data quality or ineffective data governance as a primary obstacle. “

I guess I really cannot imagine an organization basing business process workflows or strategic efforts on inaccurate, invalid or misleading data.  One of the biggest challenges to surfacing consumer insights from social media is the:

• Volume – According to the IDC, 95% of the 1.2 zettabytes of data in the digital universe is unstructured; 70% of which is user-generated content. Unstructured data is also projected for tremendous growth, with estimates pegging the compound annual growth rate (“CAGR”) at 62% from 2008-2012.

• Language Complexity and Content Sources – Terms can be difficult to disambiguate and it can be hard to organize social content int appropriate categorizations. Social conversations are unstructured and includes text, images, videos, emails, blogs, tweets and other types of data types that are not part of a database.

• Separating  noise from an authentic social signal – A company’s consumer can blog, tweet and provide status updates on a myriad of topics, including an organization’s product, service or brand. Separating a true social signal on-topic is critical to helping a company better understand their client.

A Blended Approach Yields More a Precise Outcome

CI is able to process, analyze and interpret structured and unstructured data, providing critical intelligence to better inform work flow processes, CRM data management systems, and other outreach programs. And we do this by blending different language technologies:

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Collective Intellect’s solution addresses the inaccuracy and bluntness of keyword search and the speed and cost disadvantages of NLP techniques through the use of advanced statistical language modeling. This unique approach achieves a high-level of categorization accuracy from which customer insights, preferences, including demographic and author profile information can be derived.

The Right Content at the Right Time for the Right Customer

This type of precise social media analytics is what a company needs to create meaningful engagement value for themselves and more importantly their customers.  It’s being able to craft the right message from the most appropriate person to the right consumer on the right platform. If your organization is relying on technology that is to unable to accurately categorize social conversations, allow for white-space or discovery analysis or discern true voice of customer and maximize signal to noise ratio, then valuable consumer insights, brand awareness and market research data cannot be integrated into how your business approaches consumer engagements. Not only are you not aware of the conversation but worse you may be equipped with the wrong data informing your understanding of your customer’s preferences or intentions.