One of the notable aspects of the online social networking phenomenon is that the full breadth of the interactions between participants can be documented. Systems capture who the interacting parties are and what they're communicating to one another, along with statistics about the level of their social media activity.
And of course, in any scenario in which transaction histories can be recorded, there's an opportunity to analyze those histories for business purposes -- in this case, using social media data analysis tools to monitor social networking sites for comments about a company and its products, and to measure the effectiveness of an organization's social media strategy in influencing things such as brand recognition and customer sentiment.
The term social media analytics is a catch-all phrase that's used to describe any type of reporting and data analysis associated with the business impact of social networks. But there are different facets of social media analytics that offer different kinds of insights. Let's consider three of them: reporting on the quantifiable aspects of social media interactions, analyzing social media content, and modeling and understanding the behavior of specific individuals within a social network.
Run the numbers on social media usage
Some examples of the statistical data that can be collected from social networks and then analyzed include audience distribution, number of impressions for posts, mobile device interactions and responses by users -- such as Twitter retweets and click-through numbers for embedded URLs. Those metrics can be broken down by different dimensions, including time of day, geographic location, browser type and corporate domains. In addition to providing useful information themselves, they're the foundation upon which the other types of social media analytics metrics are built.
Content analytics attempts to discern actionable information about the messages that are being posted by the users of social media sites. The types of analytics that can be done include identifying designated keywords in social media posts, monitoring for posts that refer to specific products or corporate brands, tracking customer sentiment based on positive or negative references to a company, and detecting problems that could pose a threat to a company's reputation and revenues.
These analyses blend emerging text analytics tools and techniques with more traditional reporting approaches to scan social media content for specific language and to apply semantic analysis concepts to the collected text. Instead of just looking at the mechanics of social media interactions, such as numbers of followers, you're now looking at how the content of those interactions could affect the way your organization is viewed by people -- and, ultimately, its business performance.
Get to know people via social media analysis
That brings us to the third facet of social media analytics: examining the characteristics of the individual entities within social networks and how they're connected to one another. Again, these analyses start with fundamental ideas, such as correlating demographic information with social media users. For example, an organization might collect data about the people following a particular Twitter account -- sex, age, location, educational attainment, and even things such as annual income or predisposition to purchasing particular kinds of products.
Such information allows companies to seek to relate individual or aggregate user profiles to possible actions in response to specific types of social media content or promotional offers. Social media data analysis could point, say, to unmarried males between the ages of 18 and 34 who are likely to respond positively to a discount offer broadcast on a company's Twitter feed or Facebook page (e.g., "Use code OFF15 for 15% off until 6:00PM EDT!"). Ongoing analysis of the uptake on the offer, and comments posted about it, could help the organization refine the message and how it's communicated to improve the response rate and the online feedback.
Analysis of that sort feeds into a more complex layer: looking at how different participants in social media communities interact with each other. Doing so can help identify influential people within a community -- for example, Twitter users with a large group of followers or who post information -- such as links to product reviews in a blog -- that is likely to influence the opinions of others. Such people could then be singled out for closer monitoring to help spot trends or discussion threads that a company needs to respond to, either to reinforce positive comments or to answer negative ones.
Each of these different parts of the social media analytics process provides some level of insight into corporate perception and the effectiveness of a social media strategy. Integrated together, they can be even more powerful in pointing the way toward opportunities for improving both strategic and tactical approaches to the social media environment -- ideally resulting in more revenue and higher profits.
About the author:
David Loshin is president of Knowledge Integrity Inc., a consulting and development services company that works with clients on big data, business intelligence and data management projects. He also is the author or co-author of various books, including Using Information to Develop a Culture of Customer Centricity. Email him at firstname.lastname@example.org.
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