Use in social media is soaring. Over 900 million people worldwide check their Facebook account on a month account, while there are more than 150 million active users in Twitter. And LinkedIn has claimed that it had more than 160 million users, with two new members joining every second.
More and more people are offering up posts and tweets about their likes and dislikes. The idea of using this data for consumer insights is increasingly enticing to marketers. They are listening to what is being said about their products, brands and competitors, plucking the latest trend, and searching for ways to take advantage of all this data.
This reminds me of what we are taught in the course of E-business Strategy. The Internet provokes 1st and 2nd order effects. The 1st order effect brings in increased efficiency and effectiveness by reducing transaction costs, as we see in ebay. What’s interesting is the 2nd order effect, which is a qualitative shift with unintended consequences. Take the example of GHX in Canada. GHXC tries to integrate the suppliers and hospitals in the same platform for opener and more efficient transactions. Yet GHXC is more than just an exchange, it’s a resource for business intelligence: it could offer vastly new services such as demand forecasting, benchmarking, market trend analysis etc, namely, the 2nd order effect.
However, these social media are not so interested in the companies to the same extent. They are slow in making data analysis and reluctant to share information with firms. In the post of “Why Social Networks Are Cool on Sharing (Their Data)”, Jack Neff identified three reasons for this. Firstly, they have not yet figured out a “value exchange” that would make consumers or business professionals willing to trade their personal data. Another one is the intensifying privacy concerns. Anyway, no one enjoys being watched 24-7 by the “big brother”. Most importantly, they are more focused on the advertising than the less lucrative market research. According to the American Marketing Association’s Honomichl Report, the annual U.S. revenue in market research is $ 17 billion to $ 18 billion in recent years, far less than the $ 130 billion to $ 150 billion spent last year in advertising research. Just like any other form of media, such as print, TV, magazines, the revenue in advertising is the key issue.
Another reason for this in my opinion is that the data in social media is far more complicated and fragmented than these specialized B2C websites. With millions of users and numerous casual data, it can be so difficult for the social media to fully process what is feasible in regard to capturing, managing, analyzing and creating social intelligence in order to take action on this user-generate content.
So what could companies do then? Fine-tune their ability to monitor social media network is clearly under way for many companies, as we discussed in the class. But in Judith Aquino’s post of “Transforming Social Media Data into Predictive Analytics”, he argued that companies should go one step further, fusing social media with predictive. A good example will be MTV’ss new show “Teen Wolf”. MTV turned to Networked Insights, a professional social analytics firm, for help
fine-tuning its market efforts. Based on the social conversations about Teen Wolf and other teen topics that the analytics firm tracked on Facebook and Twitter, Networked Insights provided several insightful suggestions for MTV. The show turned out to be a big hit, attracting a total of 2.17 million views. As said by Burrel, MTV’s vice president of consumer marketing, “Teen Wolf was probably the best social engaged-type program that we had put together because we were using social data and social insights to help drive marketing decisions. “
This is just a tip of the iceberg. Social analytics firms like Networked Insights are booming and helping companies to better tap into the social intelligences. However, despite the promises, there are several pitfalls to keep in mind. Firstly, the comments on social media networks represent the perceptions of only one portion of a company’s customer base, or even their target customers seldom use social media. Therefore, it is important to have a good understanding of your target customers at the very beginning, and then look carefully at the audience mix in social media and determine if it is the target group. Anyway, it is not always fans who are following you. Furthermore, some companies are so relying on software analytics that they neglect the context and nuances of how people feel and what they really mean in a conversation. It is important to bear in mind that social media is a dialogue and relationship venue; the deepest value comes from actually having conversations with your customers, listening patiently and respectfully.
In a word, social data is an additional, powerful tool in the marketing tool pool, not a replacement, not to mention a cure-all.
Resources:
1. Jack Neff (May, 2011). “Why Social Networks Are Cool on Sharing (Their Data)”
Retrieved from http://adage.com/article/digital/social-networks-cool-sharing-data/227337/
2. Judith Aquino( November, 2013). “Transforming Social Media Data into Predictive Analytics”
Retrieved from
http://www.destinationcrm.com/Articles/Editorial/Magazine-Features/Transforming-Social-Media-Data-into-Predictive-Analytics-85687.aspx