In recent years, the practice of observing social media analytics (SMA) has gained increasing currency in the world of business. SMA measure three data concepts: awareness, engagement, and reach. Theoretically, these concepts provide information about consumer behavior as reflected in social media platforms like Facebook and Twitter. While many claims have been made about the reliability and business value of social analytics, the business case for this technology is far from convincing.
With the above facts in mind, the basic research question of the current study reads as follows: through observing analytics, do social media platforms like Facebook and Twitter actually impact businesses? As an initial response to the basic research question, observing analytics can impact businesses in positive ways. Observing social analytics can, in fact, help business organizations get a feel for marketplace factors like consumer awareness, consumer engagement levels, and the reach of the company's marketing efforts. Observing analytics does not, however, directly lead to the creation of competitive advantage(s). The limitations of SMA, in this respect, are primarily due to the fact that analytics are often unreliable and do not always provide accurate reads.
As a matter of theory, social technologies offer businesses an opportunity to gauge the marketplace and gain insights into consumer behavior. Social technologies, in this respect, represent a bidirectional means for pulling and pushing data to/from social media platforms like Facebook and Twitter. Even further, proponents of SMA claim that this technology can generate more genuine and timely insights into consumer preferences and trends such that the business decision-making process is far more informed (McKinsley Global Institute 8; Bekmamedova and Shanks).
Again, social analytics measure three data concepts: awareness, engagement, and reach. Understanding the precise meaning of these concepts is important for gaining perspective about the strengths and weaknesses of SMA.
The concept of awareness refers to an estimate of the degree of public cognizance (i.e., knowledge and understanding) of an issue, event, or social phenomenon (Parise, Iver and Vesset). Metrics for awareness include things like: i) the number of webpage hits on a current news topic, ii) the number of views of a video on YouTube and/or other Internet sites, and iii) the number of tweets on Twitter about a current event. As an example, a production studio in Hollywood might use social analytics to gauge public awareness about a scandal involving an actor. Based on findings, the company could attempt to calculate how the scandal might impact advertisement revenues from commercial sponsors.
The concept of engagement attempts to measure the motivation and degree of interaction that users are demonstrating over a topic, issue, or event (Parise, Iver and Vesset). A manufacturer of handheld digital devices might be interested in knowing, for example, whether consumers are responding to a current marketing campaign and promotion. Some common engagement metrics include:
- Retweets (Twitter)
- Facebook fan page additions
- Contest entries
- Reviews (Weber 75)
When measuring engagement, companies assume that if users are repeatedly posting messages, sharing links, and/or stirring up conversation about the company and/or its products, then opportunities exist to act in strategic accordance.
Finally, the concept of reach refers to an estimation of how far news has traveled in the various social media domains (Parise, Iver and Vesset). Measuring reach is important because doing so can help businesses gauge the impact (i.e., scope and size) of a marketing initiative or promotion. Some common reach metrics include:
- Number of web page visits
- Number of unique visitors to a web site
- Number of page views
- Content viewed (Weber 75)
In theory, companies can use SMA reach computations to make critical decisions about current and/or future marketing and promotion plans.
As a matter of formal investigation, Ribarsky, Wang and Dou conducted case studies of real world SMA applications. The basic research question of the study concerned whether or not SMA support competitive advantage. As for the basic methodology, SMA usage was observed for five department stores in the Charlotte, North Carolina region. The five stores included Belk, Macy’s, Dillards, Neiman Marcus, and Saks Fifth Avenue. Each of the organizations used marketing messages with hashtags that identified the store and promotional data. The companies were interested in the response to marketing and advertising campaigns for the purpose of gleaning demographic information about their Twitter followers. Ultimately, the researchers found that statistically significant Twitter response occurred in cases involving “marketing [initiatives] around celebrations, charitable campaigns, and holiday events, [e.g., Macy’s Thanksgiving Day parade]” (Ribarsky, Wang and Dou 3). Findings of the study, therefore, indicate that SMA can facilitate special-occasion marketing campaigns. Yet, SMA do not support any identifiable type(s) of competitive advantage.
Sinha et al. investigated social analytics with the goal of determining whether this technology can be used as a tool for supporting behavior informatics and HR/business processes. By examining multiple industries, the researchers came to a number of important conclusions about viable uses of social analytics. For one, the researchers found strong evidence to show that some business organizations integrate analytics with social media to support the HR practice of sourcing talent (Sinha, et al.). As an example, businesses can use social analytics to supplement/support costly standard human resource recruitment methods like background checks. In this way, social networks provide employers with additional information to use for screening applicants, engaging with applicants, and/or gaining additional insights about the personality of an applicant (Sinha, et al.).
As a caveat, the researchers recognized that Facebook and Twitter data are often inaccurate and/or exaggerated by the account holder. This significantly limits the business value of SMA as the underlying data must be considered somewhat unreliable. SMS reads are, therefore, often inaccurate. As such, SMA is rendered incapable of providing a means of, and/or, supporting competitive advantage.
Bekmamedova and Shanks developed a theoretical framework to explain how organizations can create value with SMA. The model was applied to a case study of a large financial institution that used SMA to support an extremely important marketing and public relations project. At the time of the project, many banks were facing extreme scrutiny by the public. Recent scandals revealed that many banks were using unscrupulous practices to increase fee collections and reduce competition in the marketplace (Bekmamedova and Shanks). In order to quell public suspicions, the financial institution used social media to gauge public opinions of the bank. Specifically, the financial institution was able to use SMA to garner competitive insights about customer preferences, customer expectations, and brand awareness.
In the final analysis, the researchers reported that the financial institution successfully used SMA to support differentiation strategies in the marketplace. As a caveat, the researchers acknowledged that the company enjoyed an extraordinary response to the PR campaign by the mainstream media. Public opinion was, in fact, significantly and positively influenced by favorable media coverage of the company’s PR campaign. Thus, at best, SMA served as a supplemental PR management tool.
Limitations concerning the reliability and accuracy of SMA reads raise critical questions about the true strategic value of this technology for businesses. Along these lines, Parise, Iver and Vesset investigated SMA in relation to strategies to capture and create value from big data. The research was predicated on the assumption that streamed data and other sources of data on the Internet represent virtual “modern-day treasure troves that can be mined to glean insights into products, services and customers” (Parise, Iver and Vesset). The researchers found that streamed data and other sources of data on the Internet can be highly valuable for business organizations in supporting business intelligence, predictive modeling, market trends, and more. However, SMA is not considered a viable stand-alone technology for leveraging the value of big data. At best, SMA should be classified as a support tool alongside performance management, decision science, and data exploration (Parise, Iver and Vesset). And even further, other researchers point out that Big Data, itself, is still in the nascent stages of development (Kim, Pelaez and Winston). Therefore, the business value and impact of SMA must be placed in proper and limited perspective as an ancillary big data tool, at best.
In tying the evidence together, observing analytics does not directly lead to the creation of competitive advantage(s). As hypothesized, the limitations of SMA are primarily due to the fact that analytics are often unreliable and do not always provide accurate reads.
The Sinha et al. shows that SMA are useful for certain but limited HR applications like supplementing background checks as a way of getting a better feel for a job applicants’ personality. Given the fact, however, that people often include exaggerated and/or false data about themselves on social media sites like Facebook, SMA cannot be considered a reliable substitute and/or alternative for traditional HR tools.
Although the Bekmamedova and Shanks case appears to provide significant evidence of the reliability of SMA for supporting strategic objectives of a business, the application is limited. For starters, the case study represents a very specific application of SMA in the financial, banking sector. Special circumstances and mitigating factors skewed the results of the study. Most notably, the heightened tension surrounding the scandal put the mainstream media on alert. Therefore, when the financial institution launched its PR campaign, the media was predisposed to jump on the story. Public perceptions for the company did, in fact, improve significantly – providing the company with a short-lived public relations advantage. In the final analysis, however, the success of the PR campaign was largely due to favorable press coverage, not SMA.
Along similar lines of criticism regarding the limitations of SMA, the findings of Parise, Iver and Vesset help place discussion in proper perspective. SMA is not a silver bullet solution for businesses seeking to use data streams as a source of competitive advantage. At best data acquired from social media platforms like Facebook and Twitter provide a piece of the information puzzle. Compared to tried and tested big data methods from disciplinary fields like performance management, decision science, and data exploration, SMA remains in the nascent phases of development.
In further clarifying the findings, it would seem that much of the discussion about SMA is due to proprietary, commercial hype. In recent media reports, for example, JetBlue Airlines successfully used social media analytics to understand “the reach, volume and impact of a campaign, and how it aligns with demographic and psychographic profiles of the audience” (LeClaire). A closer review of the JetBlue Airlines case reveals, however, that SMA is not being used by JetBlue Airlines in a proactive/strategic manner. To the contrary, Jet Blue Airlines is using SMA as a defense mechanism to monitor potentially bad PR. For example, the company watches Twitter activity to make sure JetBlue Airlines customers are not tweeting bad news about JetBlue that could go viral and hurt the company. SMA data are not reliable and do not provide accurate reads of the marketplace. At best, the JetBlue Airlines case shows that SMA do not provide actionable data - i.e., information that could be used to support strategic decision making and competitive advantage. This is most likely due to the fact that analytics are often unreliable and do not always provide accurate reads.
Is a similar case, Barker et al. reported on the findings an SMA case study involving AAA. As indicated in the study, AAA had an average of 8,500 social media mentions per month to which the company only responded to about 200 maximum (Barker et al.). The low response by AAA suggested that the company was using SMA for spot monitoring PR red flags. This is akin to a half-hearted use of social media and analytics to prop up a company’s CSR profile (Lee, Oh and Kim). Thus, like the JetBlue Airlines case, the AAA case study shows that SMA is not providing competitive advantage. At best, SMA is an ancillary public relations tool that is limited by unreliable data and inaccurate reads.
In conclusion, the current study has demonstrated that observing SMA can impact businesses in positive ways. By observing SMA, businesses can get a feel for awareness, engagement, and reach. SMA can also support HR functions like job applicant screening and analysis. SMA can further help companies monitor social media platforms like Facebook and Twitter for negative PR. However, it must be kept in mind that SMA technology remains in the nascent stages of development. The business value and impact of SMA must be placed in proper and limited perspective as an ancillary big data tool, at best. Observing analytics does not, in other words, lead directly to the creation of competitive advantage(s). SMA come up short because analytics are often unreliable and do not always provide accurate reads. As a final recommendation, businesses should keep in mind that shifting advertising and consumer budgets to SMA will not yield significant business impacts in the way of promoting competitive advantage (McKinsley Global Institute 9). Observing analytics (i.e., SMA) can, therefore, be summed up as a process/activity that yields relatively minor strategic impacts for businesses.
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Ribarsky, William, Xiaoyu Wang, and Wenwen Dou. Social Media Analytics for Competitive Advantage. Presented at the EuroVis Workshop on Visual Analytics from The Charlotte Visualization Center. 2013: 1–5. Web. 19 March 2014
Sinha, Vinita., K. S. Subramanian, Sonali Bhattacharya, and Kaushik Chaudhuri. “The Contemporary Framework on Social Media Analytics as an Emerging Tool for Behavior Informatics, HR Analytics and Business Process.” Journal of Contemporary Management Issues, 17.2, December 2012: 65-84. Print.
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