Methodology: Draft/Free writing (part 3)
My goal has been to write 200 words a day on this section this week. I haven’t quite gotten there but there is enough that I feel like I should post an update. I figured out how to get better cites for methodologies and definitions. I am having some issues on case studies as they pertain to case studies as my ideal is Australian sport or sport and social media. There aren’t always ones out there that work easily.
Methodology
When conducting social media research, there are ten general methods that can be used to gather and analyze data. These are:
- Individual case studies for how a business uses social media and the web;
- Search and traffic analytics analysis;
- Sentiment analysis and reputation management;
- Content analysis;
- Usability studies;
- Interaction and collaboration analysis;
- Relationship analysis to try to determine how people interact and to identify key influencers;
- Population studies;
- Online target analysis of behavior and psychographics; and
- Predictive analysis.
Each of these methods offers insights into various aspects of the web and its population. The type of analysis used is often specific to the purpose of the research, involved blended approaches from traditional analysis types, and different methods are often used in conjunction with each other. These methods often blend quantitative and qualitative analysis. Choosing the correct method of gathering analyzing data can be one of the biggest hurdles for being able to measure ROI and understand how a community works.
This section will provide a brief summary of each type, explain how to conduct this type of research and give examples that used that methodology.
Individual case studies for how a business uses social media and the web,
Case studies on social media usage are often done to measure the effectiveness of specific actions taken by an organization.
Bronwyn et al. (2005) say case studies “typically examine the interplay of all variables in order to provide as complete an understanding of an event or situation as possible. This type of comprehensive understanding is arrived at through a process known as thick description, which involves an in-depth description of the entity being evaluated, the circumstances under which it is used, the characteristics of the people involved in it, and the nature of the community in which it is located.”
This methodology often incorporates components of all the other methods discussed in this section. The specific methods often depend on the goals of the person or organization conducting the case study.
Vincenzini (2010) did a case study regarding the use of the social media by the NBA in an attempt to define why they have been successful in using it to promote the league. The author used quantitative analysis to measure the size of the community, the volume of content they were viewing on sites like YouTube and the volume of content they were creating on sites like Twitter. The quantitative analysis was synthesized with explanations from NBA employees to explain their practices in the context of their own business decisions as they pertained to social media. This was followed up with an explanation as to what worked and what did not worked and offered advise for others involved with sport and social media to help them leverage their own position.
Case studies are a mixed methodology approach, borrowing from other approaches. The major difference is that the case study focuses on a narrower perspective with the goal of tracking behavioral changes, or in advising others on how an organization changed practices and how those lessons can be applied elsewhere.
Search and traffic analytics analysis
Search engine and traffic analytics generally is done internally to determine how to optimize a site in order to increase the amount of visitors a site gets and the total number of pages that they view. This method involves identifying how people arrive at a specific site and the pages they visit while at the site. Traffic analytics analysis often includes six different components: Search engine visitors, paid search advertisements, pay per click, organic traffic, direct traffic and internal site traffic.
Ramos and Cota (2009) define traffic analytics as “Tools that analyze and compare customer activity in order to make business decisions and increase sales. Analytics tools can report the number of conversions, the keywords that brought conversions, the sites that sent converting traffic, conversion by campaign, and so on.”
There are a number of different tools that allow for this type of analysis. Kaushik (2010) recommends Google Analytics, a free tool that involves putting a bit of code on all pages of a site. Kaushik (2010) points out that various types of traffic analysis can be done using the various tools provided by Google Analytics. The author claims that Google Analytics allows you to break the analysis down into “three important pieces: campaign response, website behavior, and business outcomes.” (Kaushik 2010)
Sentiment analysis and reputation management
Sentiment analysis involves identifying content related to a topic and identifying the emotion connected to that content. In a sport context, sentiment analysis could involve determining if newspapers are providing positive or negative coverage of a team. In a social media context, sentiment analysis would involve determining attitudes being expressed on Twitter in individual tweets.
Sterne (2010) suggests that content being ReTweeted on Twitter can be seen as a tool to measure positive sentiment. Sterne (2010) suggests that the ratio of follows/followers is not an effective tool for measuring sentiment on Twitter.
Content analysis,
Content analysis involves looking at the individual components of something larger and analyzing it. In a social media context, the content could be comments on a Facebook fanpage, or all the tweets made by a person or group.
With content analysis, the researcher views data as “data as representation not of physical events but of texts, images and expressions that are created to be seen, read, interpreted, and acted on for their meanings, and therefore be analyzed with such uses in mind.” (Krippendorff 2007) Krippendorff (2007) defines the basic methodology used in content analysis as unitizing, sampling, recording, reducing, inferring, and narrating.
Usability studies,
Interaction and collaboration analysis,
Relationship analysis
Relationship analysis involves examining the relationships between users on a social network, message board or mailing list. The goal is to identify cliques of different sizes or people who are particularly influential in a particular group online. This type of analysis is important to many brands including Starbucks (Plimsoll, 2010). The purpose of relationship analysis is to identify key influencers and social who influencers who are or who have the potential to be brand evangelists. (Plimsoll, 2010)
Lord and Singh (2010) define social media influence marketing as being “about recognizing, accounting and tapping into the fact that as your potential consumer makes a purchasing decision, he or she is being influenced by different circles of people through conversations with them, both online and off.”
The methodology for influence identification is not clearly spelled out as identifying influencers can be heavily dependent on the network being examined and how the community on a specific site functions. As a result, social media marketers suggest an array of tools like Twitalyzer that can be used to help determine your own influence. (Ankeny 2009) Twitalyzer’s Peterson and Katz (2010) explain their site-specific method of determining influence as including the following variables: Engagement level, total followers, total following, hashtags cited, lists included on, frequency of updates, references by others, references of others, times content is retweeted, urls cited and a number of other variables. Sterne (2010) suggests using WeFollow.com to find people who use topic specific #hashtags on Twitter. The people who tweet the most about a topic are likely to be influencers in that others looking for tweets around a topic are likely to read them. In a wider web context, Sterne (2010) suggests using Technorati to identify bloggers who have clout and influence around a certain topic.
This type of research can be viewed as a fundamental component to sentiment analysis; social media marketing companies like Razorfish often package the two together. (Lord & Singh, 2010)
Population studies
Online target analysis of behavior and psychographics
Online targeting of and marketing towards a specific audience because of their demographic characteristics is extremely common on the Internet. Psychographics is a term that includes targeting towards a specific demographic group except it includes the offline component.
Sutherland and Canwell (2004) define psychographics as “market research and market segmentation technique used to measure lifestyles and to develop lifestyle classifications.” (p. 247) Nicolas (2009) defines online behaviorial analysis as a series of steps: Collecting user data across several sites, organizing information about users based on the sites they visit and their behavior on those sites, “infer demographics and interest data”, and classifying new users based on the collected data in order to deliver relevant ads and content based their demographic profiles. Kinney, McDaniel, and DeGaris (2008) define psychographics as attitude towards something such as a brand or involvement with an organization.
Given the methodology involved, much of this type of research involves action research in that it is done in a specific content, based on internal models to address specific situations.
An example of this type of research was done by Kinney, McDaniel, and DeGaris (2008) who investigated the demographic characteristics of NASCAR fans and their attitudes towards NASCAR, its sponsors and sponsor involvement with NASCAR. The research found that age, gender and education were all important variables in determining sponsor recall: Younger, more educated males had the best brand recall amongst NASCAR fans.
This type of research can be viewed as a subcomponent of a population study in that demographic information is sought about the population. In an online context, it often works in conjunction with search and traffic analytics analysis, content analysis, and interaction and collaboration analysis.
Predictive analysis
A search on 13 July 2010 on SPORTDiscus had three results for “predictive analysis.” A search on the same date on Scopus had 605 results, 275 of which were in engineering, 132 in computer science and 102 in medicine. Predictive analysis is probably one of the least used analysis methods, especially in social media and fandom.
What is predictive analysis? At its simplest, it is identifying a future event or events, monitoring selection actions that precede the event and seeing if those events can be used to predict the outcome of similar events in the future. If a predictive value is found, an organization can monitor behaviors to help make more informed decisions.
An example of this type of research is “Predicting the Future With Social Media” by Asur and Huberman (2010). Their goal was to determine if tweet volume and sentiment on Twitter prior to a movie being released could be used to predict how well a movie performs at the box office. Their methodology involved identifying movie wider release dates that took place on a Friday, creating a list of keyword searches related to those movies, and using the Twitter API to collect all tweets and aggregate date that mention those keywords over a three month time period. The authors then compared the tweet volume to box office performance. They concluded that social media “can be used to build a powerful model for predicting movie box-office revenue.” (Asur & Huberman, 2010)
This type of research can be used in conjunction with other methods. It can be used along side a population study to see if certain actions will result in demographic changes.
Ankeny, J. (2009). HOW TWITTER IS REVOLUTIONIZING BUSINESS. Entrepreneur, 37(12), 26-32. Retrieved from Business Source Premier database.
Asur, S., & Huberman, B. A. (2010). Predicting the Future With Social Media. Social Computing Lab. Retrieved from http://www.hpl.hp.com/research/scl/papers/socialmedia/socialmedia.pdf
Bronwyn, B., Dawson, P., Devine, K., Hannum, C., Hill, S., Leydens, J., Matuskevich, D. Traver, C, and Palmquist, M. (2005). Case Studies. Writing@CSU. Colorado State University Department of English. Retrieved August 26, 2010 from http://writing.colostate.edu/guides/research/casestudy/.
Kaushik, A. (2010). Web analytics 2.0: The art of online accountability & science of customer centricity. Hoboken, N.J: Wiley.
Kinney, L., McDaniel, S., & DeGaris, L. (2008). Demographic and psychographic variables predicting NASCAR sponsor brand recall. International Journal of Sports Marketing & Sponsorship, 9(3), 169-179. Retrieved from SPORTDiscus with Full Text database.
Krippendorff, K. (2007). Content analysis: An introduction to its methodology. Thousand Oaks, Calif.
Lord, B., & Singh, S. (2010). Fluent: The Razorfish Social Influence Marketing Report. Razorfish. Retrieved August 25, 2010, from http://fluent.razorfish.com/publication/?m=6540&l=1
Nicolas, P. (2009, December 17). “Online audience behavior analysis and targeting.” Patrick Nicolas Official Home Page. Retrieved August 1, 2010, from http://www.pnexpert.com/Analytics.html
Peterson, E., & Katz, J. (2010). Twitalyzer Help and Company Information | Twitalyzer: Serious Analytics for Social Media and Social CRM. Twitalyzer. Retrieved August 25, 2010, from http://www.twitalyzer.com/help.asp
Plimsoll, S. (2010). Find and target customers in the social media maze. Marketing (00253650), 10-11. Retrieved from Business Source Premier database.
Ramos, A., & Cota, S. (2009). Search engine marketing. New York: McGraw-Hill.
Sterne, J. (2010). Social media metrics: How to measure and optimize your marketing investment. Hoboken, N.J: John Wiley.
Sutherland, J., & Canwell, D. (204). Key Concepts in Marketing. Palgrave Key Concepts. Hampshire, England: Palgrave MacMillan.
Vincenzini, A. (2010, July 14). A Case Study: The NBA’s Social Media Strategy & Tactics. Retrieved August 26, 2010, from http://www.slideshare.net/AdamVincenzini/the-nba-and-social-media-a-case-study