To Cite: Pan, B., & You, Y. (2017). Conceptualizing and Measuring Online Behavior Through Social Media Metrics. In Xiang & Fesenmaier (eds). Analytics in Smart Tourism Design (pp. 203211). Springer International Publishing. Conceptualizing and Measuring Online Behavior Through Social Media Metrics Bing Pan, Ph.D. Department of Hospitality and Tourism Management School of Business College of Charleston 66 George Street Charleston, SC 29424 Telephone: 001-843-953-2025 Fax: 001-843-953-5697 Email:
[email protected] Ya You, Ph.D. Department of Management and Marketing School of Business College of Charleston 66 George Street Charleston, SC 29424 Telephone: 001-843-953-6770 Fax: 001-843-953-5697 Email:
[email protected] Bios: Bing Pan, Ph.D., is an Associate Professor in the Department of Hospitality and Tourism Management and Head of Research in the Office of Tourism Analysis within the School of Business at the College of Charleston, USA. He has published in the area of information technologies and their adoption in the hospitality and tourism industries. His research publications include using online data to understand, predict, monitor, and forecast tourism economic activities, tourist online behavior, social media, search engine marketing, and research methodologies. Dr. Pan has consulted with the Charleston Area Convention and Visitors Bureau for ten years.
1
Ya You, Ph.D., is an Assistant Professor of Marketing at the School of Business, College of Charleston. Her research interests focus on online word-of-mouth effectiveness and social media strategies. Her research has been published in Journal of Marketing and featured prominently in the book Empirical Generalizations about Marketing Impact, published by the Marketing Science Institute. In addition, her work has received extensive publicity in the business press, in outlets such as Science Daily, Phys.org, etc.
Introduction Social media is almost everywhere now. As of 2015, 70% of the U.S. population has at least one social networking profile and the number of worldwide social media users is predicted to grow to 2.5 billion by 2018 (Statista, 2015). Businesses have realized the power of social media and considered social media marketing as an important part of their integrated marketing communication tools. According to a recent Forrester research, the expenditure in social media marketing would increase from $7.52 billion in 2014 to $17.34 billion in 2019 in the U.S. (Forrester Research, 2014). With social media, brands are able to increase brand recognition, enhance brand loyalty, and improve sales conversion rate. In particular, fans and followers would “advertise” for the brand they love for free on their social networking sites. Undoubtedly, social media will continue to have a significant impact on businesses with the ability to reach out and communicate with their target customers on a personal level and a daily basis. However, the understanding of how to measure social media effectiveness is far behind the exploding speed of social media usage by marketers. For example, the 2014 CMO Survey reveals that only 15% of the responded marketers could quantify the impact of social media on their business (CMO Survey, 2014). Moreover, the 2015 Social Media Marketing Industry Report shows only 42% of over 3,700 surveyed marketers agreed they are able to measure the return on investment of their social media activities (Stelzner, 2015). These survey results are not surprising as the lack of categorization and standardization of metrics across social media platforms limits managers’ ability to truly measure the value. Social media includes not only the comments, shares and links posted on multiple platforms such as Facebook, YouTube, or blogs, but also organizations and people who follow or subscribe the online 2
community. Therefore, an effective and integrated framework of social media metrics is important to understand how social media really works in creating values for businesses. Furthermore, the social media metrics can be used to develop dashboard, which reflects the key drivers and outcomes within the firm, diagnoses excellent or poor performance, and facilitates managerial decision-making. The Evolution of Social Media The need for measurements is coincided with the commercialization of social media. In the earliest days, users logged in their findings of websites on a web page on a daily basis to record their own lives and musing (Oxford English Dictionary, 2015). Apparently there is little need to capture how many people read the web page and for how long. When businesses adopted social media to maximize their revenue and profit, the measurement starts to gain importance. Social media has gone through many stages of evolution. The birth of social media probably happens in 1997, when the first social site sixdegrees.com was formed (Boyd & Ellison, 2007) and web users started a first weblog (Oxford English Dictionary, 2015). The word “social media” first appeared in the same year (Bercovici, 2010). However, it is until 2003-2005, the concept of “social media” started to gain popularity (Figure 1). The first academic report on social media probably appeared in 2003 (Kline & Arlidge, 2003) and the first conference on social media was held in 2004 (Thompson, 2004). It’s really 2005 when the concept started to take off in books and reports (Figure 1). The concept of “Web 2.0” started to gain popularity after 2006, with the wide adoption of social media and great amount of information produced online (Figure 1). Especially in the sharing economy today, when new businesses thrive on the number of users and the way they connected with each other (Cannon & Summers, 2014), the measurement of sharing might be the single determining factor in the life and death of those companies. Thus, the importance of measurement of social media becomes more crucial than ever.
3
Google Books NGram viewer, retrieved January 6, 2016 Figure 1. The Concept of Social Media and Web 2.0 on Google Books The forms of social media is also evolving. The early date of text-based blogs was slowly expand to images and videos; the platforms transformed from the sharing of text and pictures to networks where user got connected. As a result, the ways of measuring social media is also evolving: from the early days of the number of views and reads to later time of network measurement in terms of friends, fans, and connections. However, no matter how much the realm of social media has changed, we argue that the underlying structure is still the connections between users and information artifacts (Pan & Crotts, 2012). In this chapter, we intend to propose a theoretical framework for social media and couch the measurements of social media in an integrated framework. We argue that all various measurements are the representations of different aspects of a multidimensional network and they can be potentially investigated and combined to produce better understanding of the phenomenon.
A Conceptual Framework Many behavioral and psychological theories can abstract and explain the complex phenomenon of social media. The multi-dimensional social network theory can actually capture the nature of the interaction between users and information artifacts and is the inspiration for our social media framework (Contractor, 2009). In this section, we outline a conceptual model of a multi-dimensional network to represent social media and actors. 4
Different from other mass media, social media is based on the structure of social networks. It involves multiple relationships, including relationships between actors and actors, information artifacts with other information artifacts, and actor and information artifacts. A multi-dimensional network framework can actually capture the nature of this communication platform (Contractor, 2009). Here we propose a similar network framework where the network is composed of nodes and links. The nodes are two types which we term actors (a Facebook account, a twitter account, etc) and memes (a text post, a tweet, a video, a picture, etc.). There are multiple types of relationships among these.
Memes
Actors Post
5.Video
1. John Follow
Follow
Comment On
2. A Company
Post
Comment On 6. Comment
Comment On Follow 3. Steven Post Follow 4. Tom Indirectly Follow
7. Tweet
Retweet
Figure 1. Example of Network Framework To give an example, Figure 1 shows a multi-dimensional social media network. The actors are four different accounts: John, Steven, Tom, and A Company; three memes are a video, a comment, and a tweet. First, there are relationships between actors: both John (1) and Steven (3) are following A Company (2) as followers. Tom (4) is following Steven (3). There are relationships between actors and memes. A Company posted a video (5) and later 5
made a comment (6) on it. The video was retweeted (7) by Steven (3) later and then being exposed to Tom (4); A Company (2) and Tom (4) also retweeted that tweet. So in this way, Tom (4) is indirectly “following” A Company (2). There are also relationships between memes: comment (6) is on video (5), and tweet (7) is about video (5). Thus, almost all the relationships are different types of connections with direction, from following to retweeting. With this model, various measurements of social media are actually different measurements of this nodes-links network with different levels of aggregation. For example, one can calculate the following metrics for A Company: number of followers of A Company; the number of memes produced by A Company; the number of tweets and retweets generated by the content; the number of interactions of the followers with the content A Company produced. Thus, this muliti-dimentional network provide a valid way to abstract all the interactions in the social media sphere. Social Media Measurement
Based on this conceptual framework, we can calculate all the social media web-based measurements and marketing-oriented measurements, based on different nodes and connections, and three distinct types of relationships we identified. The following table describes the network components, types of relationship and representations, as well as commonly used social media web-based measurements and marketing-oriented measurements. Some marketing-oriented measurements could be derived from the web-based measurements. For example, audience engagement is calculated by dividing the proportion of visitors who participate in a specific meme by contributing comments or shares by the total number of views. Response rate is the percentage of inquiries or complaints responded by company’s messages. Table 1. Social Media Measurements and Multi-Dimensional Network Network
Type
Representation
Web-based Measurements
Marketing-oriented Measurements
Nodes
Actors
Facebook account, Twitter account, Instagram account
Memes
A text post, a picture, a
Posts; sentiments; articles;
6
Social media involvement
video, a place Connections
Actor – Actor
Meme – Meme
Friend with, follow,
Hyperlink to, explaining,
Followers; Likes; friends;
Awareness; Reach; Lead
fans; group members;
generation; Brand liking
Inbound links, comments
Response rate; Conversion rate; Amplication rate
Actor – Meme
Tweeting, retweeting,
Shares; hides; retweets;
Recommendations; Virality;
posting, commented on,
tweets; check-ins; views;
Audience engagement
impressions; redemptions; bookmarkings; response time; clickthroughs;
Furthermore, these metrics only measure the technical aspects of social media but not directly correlated with a business’s performance. Kaushik proposed a few more meaningful measurements such as conversion rates, amplication rate, applause rate, and economic value (Kaushik, 2011). Using the conceptual framework proposed above, one can easily measure these metrics using the numerical values in the multi-dimensional network.
Conversion rates: the number of audience comment per post. The number of Commenting connections between memes and memes. Amplication rate (facebook): the number of shares per post. The average number of Sharing connections between actors and memes. Applause rate (facebook): the number of likes per post. The average number of like connections per post meme.
However, the last metrics, economic values cannot be easily measured with this network structure. The fundamental question is: how do we measure the economic values of social media? We argue that the total numerical matrix of all these connections and actors, are the total social value of the network; the ego-network of the company is the total social value of a company, including the number of followers, the number of posts, retweets, shares, etc. By measuring these numerical values in time as well as tracking the revenue and profits of
7
the company, one will be able to build the economic values of these through econometric methods such as Granger modeling techniques (Granger, 1969)or graph structure learning (Heckerman, Geiger, & Chickering, 1995).
Applications This section gives a demonstration an application of this model in a viral video case. For example, Disney Cruise Line produced a viral video and posted it on its youTube channel. This video meme will be liked, shared, tweeted, blogged, discussed, and even modified and reposted. The numbers of those connections to this meme, the numbers of likes, comments, and followers, are different measurements of the reach of this video meme. In addition, the subscribers to DisneyCruiseLineNews increase by thousands of fans. All these will lead to a change of the ego-network structure of the social media platform. According to historic time series models, one can test the monetary value of this video is 2% of cruise customer in the following year which is $2 million; the profit generated is $200,000 and the Return on Investment is 150%.
Conclusions In this chapter, we proposed a conceptual framework of social media and couched the metrics under this multidimensional network. We argue that social networks are multi-dimensional networks of nodes and links between actors and memes. All the different measurements of social media could be calculated based on the numerical values of these nodes or connections. Furthermore, more meaningful measurements, such as conversion, amplication, and applause, could be measured through this multi-dimensional network. The amalgamate of all the meaurements of this network could be tracked along a time scale and analyzed with the revenue and profit of a business’s performance. This way, the economic values of different social media could be quantified.
References Bercovici, J. (2010). Who coined “social media”? Web pioneers compete for credit,”. Forbes. Disponível. Boyd, D. M., & Ellison, N. B. (2007). Social network sites: definition, history, and scholarship. Journal of ComputerMediated Communication, 13(1). Retrieved from http://jcmc.indiana.edu/vol13/issue1/boyd.ellison.html Cannon, S., & Summers, L. H. (2014). How Uber and the Sharing Economy Can Win Over Regulators. Harvard business review, 13. CMO Survey (2014), CMO Survey report: highlights and insights. 8
Contractor, N. (2009). The emergence of multidimensional networks. Journal of Computer Mediated Communication, 14(3), 743-747. Forrester Research (2014). Forrester Research Social Media Forecast, 2014 To 2019 (US), Q3 2014 Update. Granger, C. W. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica: Journal of the Econometric Society, 424-438. Heckerman, D., Geiger, D., & Chickering, D. M. (1995). Learning Bayesian networks: The combination of knowledge and statistical data. Machine learning, 20(3), 197-243. Kaushik, A. (2011). Best Social Media Metrics: Conversation, Amplification, Applause, Economic Value. Retrieved from http://www.kaushik.net/avinash/best-social-media-metrics-conversation-amplification-applauseeconomic-value/ Kline, S., & Arlidge, A. (2003). Online gaming as emergent social media: A survey. Media Analysis Laboratory, Simon Fraser University. Online: http://www. sfu. ca/medialab/onlinegaming/report. htm. Oxford English Dictionary. (2015). "weblog, n.": Oxford University Press. Pan, B., & Crotts, J. C. (2012). Theoretical models of social media, marketing implications, and future research directions. In E. C. M Sigala, U Gretzel (Ed.), Social Media in Travel, Tourism and Hospitality: Theory, practice and cases (pp. 73-83). Statista. (2015). Statistics and facts about Social Networks. Retrieved from http://www.statista.com/topics/1164/social-networks/ Stelzner, Michael A. (2015), 2015 social media industry report, Social Media Examiner. Thompson, C. (2004). Chris Shipley Announces BlogOn 2004: The Business of Social Media Conference to Explore Rising Business Opportunities in Blogging and Social Networking [Press release]. Retrieved from http://www.prnewswire.com/news-releases/chris-shipley-announces-blogon-2004-the-business-ofsocial-media-conference-to-explore-rising-business-opportunities-in-blogging-and-social-networking74420252.html
9
