Businesses always looking for a new innovation to gain competitive advantages and increase their stakeholder return of investment (ROI). When the internet was being born businesses harness this new technology and thus the era of e-commerce have began. After the infamous dotcom era, web 2.0 technology have started to emerge and the era of social media was born.
Social media analytic has been used by organizations for creating new products, customer intelligence and market research. A well known skin care producer used social media analytic to “co-create” new deodorant based on people’s experience using deodorant. Walmart uses analytic to learn about what products customers want and put the products on its shelves. Researchers also use combination of social media analytic and traditional way to do market research.
Fan and Gordon (2014) in their journal “the power of social media analytics” outlined a “CUP” framework of social media analytic process. First process is to capture and extract relevant social media data. Second process is understand which “cleanse” data that was gathered during capture process. Those data then will be assess to get meaningful insight. Fan and Gordon (2014) argued that the second process is the most crucial stage since its result are to be used for business decision making. The last process is present stage where result from understand stage will be presented to business user with easy to understand format.
The capture process involves collection of massive data from sources of social media. Some techniques that being used for data gathering are crawling, using social media’s API, and rapid site summary (RSS) feed. Fan and Gordon (2014) outlined other key techniques in social media analysis:
- Sentiment analysis and trend analysis for the understand stage
- Topic modeling and social network analysis primarily being used in understand stages and also in capture and present stages.
- visual analytic for understand and present stages.
Sentiment analysis or opinion mining uses text analytic to find out user opinion about people, products, and services from text sources such as twitter. Bollen, Mao, and Zeng (2011) give example how sentiment analysis can even be used to predict the stock market.
Topic modeling has been defined by Miriam Posner as a method for finding and tracing clusters of words (topics) in large bodies of texts. One application of topic modeling algorithm is to be used to find trending topic in twitter. Thus with topic modeling could be used by organizations to measure how effective their social media marketing campaign.
Social network analysis according to Bonchi, Castillo, Gionis, and Jaimes (2011) has been long used by sociologist to study social phenomena for years. Nowadays, social network analysis is done using data collected from online interaction and social media relation in Facebook and twitter. Social network analysis can be used to identify most influential person in marketing campaign on social media, and therefore useful for predictive modeling for targeted marketing.
Lastly, visual analytic is described by Fan and Gordon (2014) as a collection of techniques that use graphic presentation to summarized information to business user. This is commonly refer to dashboard where it consist of multiple metrics and its performance index.
Social media giants, such as Facebook, Google and Twitter understand how valuable their social media data is for enterprise user. All of those social media moguls sell their user’s data to companies who then provide social media analytic services to other companies. Enterprises who would like to take advantage of social media for their profit are mostly used those third party service providers to market their products, gather their customer insight, and other initiatives. Those framework outlined by Fan and Gordon (2014) can also be used by companies who prefer to harvest social media by doing it in house.
Bonchi, F., Castillo, C., Gionis, A., & Jaimes, A. (2011). Social network analysis and mining for business applications. ACM Transactions on Intelligent Systems and Technology (TIST), 2(3), 22. Retrieved from http://chato.cl/papers/bonchi_castillo_gionis_jaimes_2011_social_network_analysis_business.pdf
Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1-8. Retrieved from http://www.sciencedirect.com/science/article/pii/S187775031100007X
Fan, W., & Gordon, M. D. (2014). The power of social media analytics. Communications of the ACM, 57(6), 74-81. Retrieved from http://www.researchgate.net/profile/Weiguo_Fan2/publication/259148570_The_Power_of_Social_Media_Analytics/links/0deec52a0cb94c71bb000000.pdf
Zeng, D., Chen, H., Lusch, R., & Li, S.-H. (2010). Social media analytics and intelligence. Intelligent Systems, IEEE, 25(6), 13-16. Retrieved from http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=5678581