Editorial Record: Original draft submitted to JPRE March 30, 2017. Revision went under review August 7, 2017. Manuscript accepted for publication Oct. 8, 2017. Final edits completed July 20, 2018. First published online August 17, 2018.
Michele E. Ewing, Kent State University
Carolyn Mae Kim, Biola University
Emily S. Kinsky, West Texas A&M University
Stefanie Moore, Kent State University
Karen Freberg, University of Louisville
Teaching Digital and Social Media Analytics: Exploring Best Practices and Future Implications for Public Relations Pedagogy
One of the growing areas within public relations is digital and social media analytics. Teaching the use of analytics to communication students is not new, but studying what is being taught is almost non-existent. The public relations research literature has supported exploring the value of data analysis to gain audience insights, to measure communication strategies, and to evaluate campaign efforts. The purpose of this study is to explore the ways in which faculty are teaching social media analytics. Two content analyses were conducted to explore trends of digital and social media analytics training. Authors analyzed related course syllabi and a Twitter chat on the subject sponsored by the AEJMC PR Division and PRSA Educators Academy. Findings and future implications in teaching digital and social media analytics for educators and public relations practitioners are discussed.
Key words: social media, social media analytics, public relations education, digital analytics
Teaching Digital and Social Media Analytics: Exploring Best Practices and Future Implications for Public Relations Pedagogy
The field of public relations, like many other professional disciplines, has been compelled to respond to the growing demands and shifts in the digital social landscape. Within the public relations education sector, there has been a rise of social media research (Duhé, 2015). One of the challenges in social media research and practice is to determine how to effectively bridge the expectations of practitioners with what is being taught in the classroom. Several pedagogical studies looking at social media (e.g., Kim & Freberg, 2016; Zhang & Freberg, 2018) have attempted to make these connections stronger within the discipline, yet with social media changing so quickly, professors face significant challenges keeping up with the trends, as well as addressing the key areas and skills students need to be successful in the field. Teaching the use of analytics to communication students is not new, but studies examining what is being taught in this area are almost non-existent; thus, an investigation of current curriculum trends related to digital analytics is a goal of the current study.
Literature supports the value of data analysis to gain audience insights and shape and measure communication strategies (DiStaso, McCorkindale, & Wright, 2011; Elkin, 2017; Grates, 2016; Jain, 2016). Kent, Carr, Husted, and Pop (2011) pointed to the benefit of advances in technology to students: “With new tools like analytics in the hands of communication professionals, understanding stakeholders and publics becomes easier, and students become stronger professionals” (p. 543). As Stansberry (2016d) explains, the usefulness of social media goes far beyond sending messages; social media allow practitioners to better understand their target publics. Thus, a key skill students need to learn is how to make sense of the data available. According to Elkin (2017), the majority of marketers (72%) value employees’ data analysis abilities even more than other social media skills (65%). Beyond that, of the 12 “professional values and competencies” listed by the Accrediting Council on Education in Journalism and Mass Communications, five closely connect to the idea of teaching digital analytics. The ACEJMC guidelines have the following requirements:
irrespective of their particular specialization, all graduates should be aware of certain core values and competencies and be able to . . . understand concepts and apply theories in the use and presentation of images and information . . . think critically, creatively and independently; conduct research and evaluate information by methods appropriate to the communications professions in which they work; . . . apply basic numerical and statistical concepts; apply current tools and technologies appropriate for the communications professions in which they work, and to understand the digital world. (para. 9)
The importance of instruction in analytics at all levels was emphasized by Kent et al. (2011), who said introductory students should be presented with the ideas and tools connected to analytics, while actual data gathering should be done regularly by advanced students. The authors pointed toward the ability to understand data and how to communicate the insights clearly and correctly because numbers, by themselves, do not tell the story. According to Kent et al., students need actual data to learn from so they do not rely on “stereotypes and guesses” in their campaigns; “having data allows professionals to make better decisions. Just as many professors use scenarios and case studies to teach ethics, having access to real data and helping students learn how to interpret data is valuable” (p. 541). Teaching data analytics to students in public relations is important because of what can be learned about relevant stakeholders and the environment in which an organization exists.
The purpose of this study is to examine how U.S. public relations professors are teaching digital and social media analytics. Following further examination of literature in the next section, the current study will fill some of these gaps through new research efforts into what is currently taught on the topic of digital analytics and what some experts say should be taught.
Much of the research related to digital training in public relations classrooms focuses on the use of social media (Childers & Levenshus, 2016; Fraustino, Briones, & Janoske, 2015; Kim & Freberg, 2016); however, gaps remain in scholarship that specifically focus on the area of teaching social media analytics. This is an important gap to address, as the use of measurement and the ability to understand data analytics is crucial to future public relations professionals.
In the 2017 report on undergraduate education from the Commission on Public Relations Education (Toth & Lewton, 2018), both educators and practitioners identified “research and analytics” as a highly desired skill (p. 87). The desirability of that skill was rated 4.30 by educators and 4.08 by practitioners (1 = not desired, 5 = highly desired). The educators participating in the survey also rated how well “research and analytics” is covered in their programs (m = 3.78), and practitioner participants rated how frequently that skill is found in new graduates hired by them (m = 2.70). Additionally, when asked to rate specific topics of importance for PR curriculum, both practitioners and educators rated analytics highly. On a scale of 1 (not essential) to 5 (essential), educators rated the importance of “data analytics” in the curriculum at an average of 4.15, and practitioners rated the topic 3.93 (p. 89). The topic of “measurement and evaluation” was also rated highly by educators (m = 4.57) and by practitioners (m = 4.42), as well as the topic of “social media” (m = 4.60 by educators; m = 4.46 by practitioners) (p. 89).
Social Media Pedagogy Research: Concepts and Skills
Early on, Anderson and Swenson (2008) studied what public relations educators should cover in class related to “new media” (p. 109). They solicited advice from PR professionals about what they should teach to best prepare their students, and one of the emerging themes was measurement. The authors followed up this research effort with a study about digital competencies (Anderson & Swenson, 2013), which also sought advice from PR professionals, specifically via a Twitter chat (#PR20Chat) and a survey of top bloggers, including Brian Solis, Arik Hanson, Gini Dietrich and Deirdre Breakenridge. Prior to the current study, the examination of social media curriculum has been rather broad; no one has yet focused specifically on teaching digital analytics in public relations.
In order to best prepare students for the professional world, researchers have examined the use of social media in the industry (e.g., McCorkindale, 2010; Sundstrom & Levenshus, 2016; Wright & Hinson, 2017). Other researchers have focused on practicing social media skills in the classroom (e.g., Fraustino, Briones, & Janoske, 2015; Kinsky & Bruce, 2016; Kinsky, Freberg, Kim, Kushin, & Ward, 2016; Kinsky, Kuttis, Nutting, & Freberg, 2016; Tatone, Gallicano, & Tefertiller, 2017), including the use of multiple platforms (e.g., Janoske, Briones, & Fraustino, 2016). Researchers have also studied the use of new media by students to communicate with professors outside of the classroom (Waters & Bortree, 2011).
Several studies have focused on the use of particular social media tools. Most of the research about the use of social media in the classroom has focused on Twitter (Anderson & Swenson, 2013; Carpenter & Krutka, 2014; DeGroot, Young, & VanSlette, 2015; Forgie, Duff, & Ross, 2013; Fraustino et al., 2015) and Facebook (Frisby, Kaufmann, & Beck, 2016).
Facebook and Twitter have been the most frequent social media platforms utilized for public relations classroom exercises; however, LinkedIn (Edministon, 2014; Peterson & Dover, 2014), YouTube (Madden, Briones Winkler, Fraustino, & Janoske, 2016), and blogs (e.g., Moody, 2010) have also been used in communication courses. Although much of the extant research examines one platform at a time, some professors have shared their use of multiple social media platforms within their campaign client projects (Childers & Levenshus, 2016; Melton & Hicks, 2011) to teach students in public relations classes about the fundamentals of writing, campaign strategy, and research approaches.
Some researchers, such as Anderson and Swenson (2013), have suggested training students to use social media professionally by using role-playing exercises and case studies, as well as using social media platforms in class. Providing assignments that create a realistic experience allows students, who will become future professionals, the opportunity to apply what they have learned in the classroom setting (Anderson, Swenson, & Kinsella, 2013). Similarly, Neill and Schauster (2015) recommended integrating math practice related to social media analytics into public relations budgeting projects in capstone courses to help students prepare for professional demands.
Although many skills related to social media have been referenced in previous literature, there is a lack of research exclusively focused on what professionals and educators see as needed concepts and skills in the curriculum related to analytics. This lack leads to the first research question:
RQ1: What digital analytic concepts and skills do both public relations students and practitioners need to understand?
Digital Analytics Outcomes
Certain research has focused on particular outcomes rather than platforms, one of which is analyzing target publics. According to Stansberry (2016d), “The information shared by key publics on social media sites has been a goldmine for public relations practitioners looking to understand the concerns, needs, and preferences of their target audiences” (p. 76). The public nature of so many social media platforms gives students access to an enormous amount of data for free. Stansberry (2016d) argued “teaching students to perform publics research not only exposes them to advanced social media analytics tools and techniques, it helps prepare them to thrive in a rapidly changing profession” (p. 88). This training allows students to analyze data while also brainstorming creative ways to apply their findings into campaigns, strategic plans, and situational analyses for clients and brand audits, to name a few possibilities.
Social media provide practitioners with valuable data, but they are not the only digital sources that should be analyzed. Website traffic is also important to consider. Kent et al. (2011) expressed that website analysis is an important addition to social media monitoring in order to gain information “about the full range of organizational visitors” (p. 542). Moody and Bates (2013) also looked at website-related content in their study of students’ knowledge of search engine optimization and of current trends in SEO within the PR industry.
Digital analytics training must not just cover collecting data, but should also include identifying the metrics that can be used for evaluation and measurement purposes for public relations professionals and researchers. Kent et al. (2011) recommended testing students on analytic terms (e.g., bounce rate), using case studies to explain how analytics can be used in public relations, and providing real datasets for students to analyze and use to propose strategic communication changes for an organization based on the analytic results gathered. There are still some measurement concerns and issues pertaining to social media. Waddington (2017) discussed how some of the issues that occurred in traditional PR measurements are translating into the same challenges for social media. This concern about what to measure points to the importance of understanding how to analyze and interpret the data collected on social media into actionable strategies.
Kent et al. (2011) recognized the different training opportunities between introductory public relations classes and advanced courses. Beginning students might simply be shown what data looks like, while upper-level courses should involve more advanced tasks such as monitoring website traffic.
According to Kent et al. (2011), students can engage in more advanced work after understanding terms and concepts:
The next move is to be able to understand how one variable influences another (“bounce rate and time on site are related . . .”). The third move is to be able to explain how variables change and interact over time or because of external forces (“the outbreak of Malaria drove up TOS during the month of April and also drove down the bounce rate . . .”). This sort of sequential, cause and effect, reasoning takes some time and practice to master. (p. 543)
In addition, some digital analytics strategies taught in classes do not tie directly into how they impact business or communication objectives. Thus, integrating the principles and framework of social media measurement protocols from AMEC (International Association for the Measurement and Evaluation of Communication) and digital analytics frameworks and connections to DAA (Digital Analytics Association) is necessary. AMEC’s Integrated Framework (2016) helps guide communications professionals in measuring the impact of their work. The interactive website tool guides professionals through the process of “aligning objectives to establishing a plan, setting targets and then measuring the outputs, outtakes and outcomes” (para. 4). The Digital Analytics Association Competency Framework (2015) serves as an industry reference for employers and educators by providing an overview of the necessary knowledge, skills and competencies needed for careers in digital analytics.
Most of the research exploring digital analytics courses and curriculum do not emphasize these two associations’ frameworks, which raises a point of concern. Without this bridge, there is a divide between what is being taught in the classroom and what is being implemented in practice. A first step in filling missing gaps in the curriculum is to find out what is currently expected of students in courses that include analytics training. This leads to the following research question about what students are expected to accomplish by the end of a course related to digital analytics:
RQ2: What outcomes related to analytics do faculty incorporate into syllabi as part of their courses teaching analytics?
Social Media Course Communication Methods
Instructional methods in public relations classes have been examined by many previous researchers, and the discussion of creating a class hashtag goes back to at least 2011 (Lowe & Laffey). However, no previous studies were found
that examined the inclusion of class hashtags or Facebook groups across social-media-related public relations classes. This use of particular social media communication methods within analytics-related classes leads to this study’s third research question:
RQ3: What social media communication methods are embedded into courses that teach social media analytics?
External Training and Certification Opportunities
For students to be prepared to process their future employers’ data, they must be trained. Like previous researchers, Stansberry (2016d) pointed out the necessity of adding new training modules to classes so that public relations students can keep up with industry: “The percentage of individuals who used social media to share multimedia content has risen rapidly, and it has become imperative that future public relations professionals be equipped with the skills to research and measure this popular form of communication” (p. 76). According to Fraustino et al. (2015), “young practitioners increasingly must develop social media skills to be competitive on the job market and successful in the workplace, and such training can start in the PR classroom” (p. 1).
A number of companies have begun to offer training programs online (e.g., Hootsuite Academy, HubSpot), with some programs designed specifically for college classrooms (e.g., Meltwater). Public relations professors have taken advantage of analytics tools and tutorials for their students to learn from, as well as certain programs’ certification options, allowing students to prove their new knowledge and skills (e.g., Kinsky et al., 2016). The increasing availability of free analytics tools has made it easier to incorporate analytics training into the classroom.
In light of research showing employer demand for students to meet today’s digital analytics challenges (Ewing, 2014; Fraustino et al., 2015; Kim & Freberg, 2016; Neill & Schauster, 2015; Stansberry, 2016d) and an increase in social media experiential learning in the classroom (Childers & Levenshus, 2016; Fraustino et al., 2015; Frisby et al., 2016; Kinsky & Bruce, 2016; Kinsky, Freberg, et al., 2016; Kinsky, Kuttis, et al., 2016; Madden et al., 2016), this study will also seek to explore the ways in which faculty are teaching social media analytics by integrating analytics-related certification testing:
RQ4: In what ways do faculty incorporate external certifications as part of their courses teaching analytics?
Incorporating Professional Expertise
In addition to online training programs with analytics tools, professors can recruit public relations professionals with data analysis experience to speak to their classes, whether they are present in the room or joining the class via video chat technology such as Skype. Research has found value in guest speakers sharing experiences from their work (e.g., Riebe, Sibson, Roepen, & Meakins, 2013), which prompts the study’s final question about inviting external professionals as guest speakers related to analytics:
RQ5: How are faculty utilizing professional experts to enhance their courses that teach analytics?
Phase 1: Course Syllabi
To understand the ways in which professors teach social media analytics within a classroom, the authors conducted two content analyses. The first was a content analysis of course syllabi (N = 31) from faculty who teach social media analytics to communication, public relations, journalism, business, or advertising students. The syllabi were gathered from universities around the country through requests on the listservs of the Public Relations Division of the Association for Education in Journalism and Mass Communication (AEJMC) and the Educators Academy of the Public Relations Society of America. These syllabi were gathered by May 2016 and represented both undergraduate and graduate courses.
Coding Procedure for Syllabi
The authors coded the information from the course syllabi using 32 factors, including names of the courses, types of assignments, tools used in the class, days dedicated to teaching analytics, and integration of industry professionals within the course. A variety of institutions were represented within the sample, including private and public, large and small, as well as universities from various areas of the U.S. (see Appendix A).
Intercoder Reliability for Syllabi
The codebook and coding procedure were tested by the authors who independently coded each of the syllabi, randomly assigning specific ones to each author. After the initial coding, the authors examined the results, which revealed inconsistencies across multiple coding categories. To address this, the authors adjusted the codebook to provide more clear definitions for manifest syllabus content versus latent content. After the revisions, two of the authors independently coded each syllabus. Despite the initial revisions to the codebook, finding an appropriate way to evaluate the agreement between coders remained challenging due to the non-standardized structure of the syllabi and general topics listed. For example, exams and extra readings were prevalent, but whether they related specifically to analytics (one of the coding items) was not always clear. Another example of coding challenges was found in coding “course outcomes.” Some syllabi listed “objectives,” others listed “goals,” others mentioned “outcomes,” and some had none of the above.
As a result, the researchers used Krippendorff’s Alpha for this study’s inter-coder reliability analysis because it is an appropriate approach when having a number of observers or levels of measurement applied in content analysis (Hayes & Krippendorff, 2007). In addition, this measurement equation looks at “observed and expected disagreement” (Joyce, 2013, para 2).
After the revision of the codebook, the values for agreement among coders for these courses were as follows: courses that employ analytics within the title (α = .93); requiring textbooks (α = .67); requiring additional readings (α = .69); case studies to read (α = .69); students conducting a case study during the course (α = 1); professionals presenting case studies (α = .89); guest lectures by professionals (α = .86); the use of professional certifications as course requirements (α = .85); listing “KPIs” as a course outcome (α = .89); listing specific tools in course outcomes (α = .77); listing “listening” as a course outcome (α = .82); listing “insights” on the course outcomes (α = .68); listing “ethical implications” on the course outcomes (α = .72); incorporating a class hashtag (α = 1); using a class Twitter list (α = 1); and using a class Facebook group (α = 1).
According to Krippendorff (2004), it “is customary to require α > .800. Where tentative conclusions are still acceptable, α > .667 is the lowest conceivable limit” (p. 241). Using these standards of measurement, the above elements each fall within the range of acceptable agreement.
Phase 2: Twitter Chat
The second phase of the study included a content analysis of a Twitter chat, which was held in April 2016 to allow an opportunity for crowdsourcing among public relations professionals and educators with digital analytics expertise (see Appendix B). Social media channels can be beneficial to researchers by cultivating public participation, via an open forum, where participants can respond to questions quickly (Glowacki, Lazard, Wilcox, Mackert, & Bernhardt, 2016). Similar Twitter chats have been analyzed by Anderson and Swenson (2013), Carpenter and Krutka (2014), DeGroot et al. (2015), and Fraustino et al. (2015).
The chat for the current study included 56 participants and 300 tweets. Two professors and two practitioners hosted the discussion. Participants were invited through memberships in public relations academic and professional associations, as well as personal outreach to faculty networks via email and social media channels. Twitter messages were captured during an hour-long live Twitter chat, which used the hashtag #PRAnalytics. Questions were posed by the hosts, who used identifiers (e.g., Q1, Q2, Q3,) to present each question. Participants indicated which question they were responding to using identifiers (e.g., A1, A2, A3). A series of nine questions were proposed to spur discussion about digital analytic concepts both public relations students and professionals need to understand.
A thematic analysis of the tweets was conducted to determine the content that industry leaders and educators thought were best practices and to identify helpful tools for teaching digital analytics. The thematic analysis involved looking for patterns; those emerging themes became categories in the analysis for each question posed in the chat (see Fereday & Muir-Cochrane, 2006). The authors then grouped the data by category (see Riessman, 2005) to identify final concepts that emerged from the Twitter chat.
Summary Statistics from #PRAnalytics Twitter Chat
Concepts and Skills
RQ1 explored the digital analytic concepts and skills that both public relations students and practitioners need to understand. Core themes from the Twitter chat on #PRAnalytics included measurement, contextualizing data, critical thinking skills, social listening skills, knowledge of social media and analytical tools, and digital storytelling skills.
Twitter chat participants emphasized the importance of students understanding measurement (n = 12 tweets) and contextualizing data (n = 10 tweets). For example, MasterCard’s Bernard Mors (2016a) tweeted, “Digital PR produces a lot of data, the challenge is to turn this data into actionable insights. #PRAnalytics.” PR professional Michael Brito (2016b), from LEWIS Global Communications, said, “THE most important data is audience intelligence. PR & Marketing must understand the behaviors of very specific audiences #PRAnalytics.” PR professor Kathleen Stansberry (2016a) said, “We focus too much on brand mentions/engagement. Need to teach the importance of using data to understand audience concerns #PRAnalytics.”
Measuring results. Participants in the Twitter chat advocated that public relations students should understand definitions of metrics, analysis of metrics, and use of metrics to measure strategic communication. Practitioners tended to emphasize the importance of showing business value for public relations, and one practitioner mentioned that employers are evaluating students’ understanding of digital analytics in terms of how students connect back to business objectives. Jennifer Trivelli (2016) tweeted, “The key is zeroing in on metrics that truly support biz. goals and that you can influence. That which is measured is managed. #PRAnalytics.” During the chat, professor Tim Marshall (2016) wrote, “Employers want students who connect measurement/eval back to overall biz objectives, rather than platform vanity metrics. #PRAnalytics.”
Practitioners and educators also agreed on the differentiation of volume metrics and engagement metrics as one of the most important concepts for students to understand. Rather than looking at vanity metrics such as likes or retweets, these individuals recommended focusing on metrics testing engagement, while not confusing terms like volume, reach, and influence. When people directly interact with a brand through writing a comment, sharing a post and extending the reach or influencing other levels of publics that the brand could not directly reach, this type of social media activity would be considered engagement. In other words, students should understand how to specifically track and measure direct interaction with publics that can show outcomes for social media activities as opposed to simply grabbing quick data points (vanity metrics) that do not show whether the public is truly interacting on social media with the brand.
Understanding context. Contextualizing data (n = 10 tweets) and critical thinking skills (n = 10 tweets) were recurring themes among all Twitter chat participants for questions about concepts, skills, best practices, and pitfalls students have when analyzing data. Participants emphasized the importance of understanding how to transform the data into actionable insights. Critical thinking abilities included asking questions, analyzing metrics, and operationalizing key terms. Overall, both practitioners and educators articulated the struggle with getting lost in the data and recognizing which data to mine and analyze, and then developing meaningful insights to drive communication strategies. For example, Mors (2016b) said, “Same practices 4 social & traditional PR: set objectives & KPIs, tools to capture data, visualize results, derive insights. #PRAnalytics.” PR professor Ai Zhang (2016b) posted, “Contextualize data to draw meaningful conclusions → drive strategic decision-making. #PRAnalytics.” Professor Stansberry (2016c) tweeted, “Learn to speak (and write) in the language of the C-Suite. Ask the right questions. Always be critical of your data. #PRAnalytics.” Brito (2016a) pointed out that “anyone can look at data, run a report, spew out #s. Very few can extract an insight that can drive a narrative/program. #PRAnalytics.”
Using tools and listening. Social listening skills and knowledge of social media and analytical tools also emerged as valuable digital analytic skills for public relations students and graduates, with each topic generating at least eight responses. Listening skills (n = 8 tweets) focused on the ability to monitor social environments, including using listening tools. Winkler (2016a) tweeted, “Social listening is the process of monitoring digital media channels to devise a strategy that will better influence consumers. trackmaven.com #PRAnalytics.” PR professor Katie R. Place (2016) tweeted this assignment suggestion: “Basic one, but we learned so much from taking on a real client and producing monthly social listening/monitoring reports. #PRAnalytics.”
Connected to both RQ1 and RQ4, knowledge of social media tools (n = 8 tweets), native analytic tools (n = 5 tweets), Google Analytics (n = 5 tweets) and Hootsuite (n = 4 tweets) encompassed a student’s ability to stay up-to-date with the latest digital platforms and tools, and the student’s ability to then choose an appropriate platform given an organization’s goals or clients. In line with the Twitter chat, the content analysis of syllabi showed faculty use a variety of tools and resources to prepare students. Some of the popular social media tools mentioned on the syllabi were Google Analytics (n = 11), Hootsuite (n = 10), Facebook analytics (n = 6), Twitter analytics (n = 4), Storify (n = 3), Google Adwords (n=3), Excel (n = 3), Crimson Hexagon (n = 2), Radian6 (n = 1), Canva (n = 1), Klout (n = 1) and Sprout Social (n = 1). Despite the plethora of analytic software available, some Twitter chat participants (n = 3) noted that it is not necessarily important for students to have familiarity with a wide range of tools, but it is more important for them to understand the data and methods behind specific platforms, so they have the ability to transition from platform to platform.
Since analytics tools come and go, professor Itai Himelboim’s syllabus provided a valuable assignment faculty could consider. In his Listening and Engagement course (I. Himelboim, personal communication, Feb. 2, 2016), students are assigned to work in groups for the duration of the semester, and in one of the assignments, they are asked to find, learn, and generate a report based on a new social media analytic or listening tool. Students are required to find a free social media listening tool or one that offers a free trial. Students must choose the tool or tools that help them address their client’s questions/meet their goals best. Their final report is to summarize social media activity related to their client/topic, using Crimson Hexagon, which they learn in class, as well as the free tool used to collect and analyze the data.
In another analytics course evaluated in the study (S. Moore, personal communication, March 21, 2016), students worked individually and in groups to define, measure, analyze and report on a client’s website activity based on the client’s objectives. Students identified and included key performance indicators (KPIs) and a summary of their findings along with recommendations for improvement. They incorporated visualizations and graphics to best represent and accurately communicate important data and findings to the client. They used Excel and created a custom Google dashboard for reporting.
Another project related to those found in the syllabus analysis was found in the review of literature. Stansberry (2016d) created a five-week project where her students worked in teams and used free tools (e.g., Hootsuite, Google Trends, BuzzSumo, IssueCrawler) to identify key publics and to conduct a content analysis, a social media audit, an online social network analysis and content tracking, which her students rated as valuable; they appreciated the applied, experiential lesson as something that would help distinguish them from others applying for the same job in the future.
Storytelling. Another prevalent digital analytic concept identified by participants was digital storytelling, or the ability to look at data, extract insights, and then present the data in a compelling manner. When it comes to analytics, students need to integrate their critical thinking skills with their storytelling abilities to share the data in a meaningful way that connects with audiences. For example, PR professor Hilary Fussell Sisco (2016) said, “I always want . . . students to visualize data. Infographics and other visual tools to explain data makes it #munchable. #PRAnalytics.” Zhang (2016a) tweeted, “Tell digital stories. Use live videos. I am playing with @Animoto & PowerDirector. Love them very much #PRAnalytics.” While Stansberry (2016b) commented, “Seems counterintuitive, but writing & visual comm. Again, if you can’t give the data meaning, it’s pointless. #PRAnalytics.”
Other concepts discussed during the Twitter chat included understanding Excel pivot tables, functions, and formulas (n = 4 tweets) and search engine optimization (n = 3 tweets). The Twitter participants commented that students shouldn’t be “afraid of math” and should learn how to use Excel to sort and analyze data.
RQ2 focused on understanding stated outcomes for courses that teach digital and social media analytics. Many outcomes stated on the syllabi contained more generic wording with only 6% listing “KPIs” (n = 2); 35% listing specific tools (n = 11); 10% listing “insights” (n = 3); and 13% mentioning ethical implications (n = 4). The most frequently mentioned analytics tools included Google Analytics (n = 11), Hootsuite (n = 10), Facebook Insights (n = 6) and Twitter Analytics (n = 4).
RQ3 focused on understanding specific social media communication methods that were used in courses. Results from the content analysis of syllabi indicated that a class hashtag was the most popular, with 26% of the syllabi incorporating this (n = 8). Based on the syllabi, it was difficult to know if hashtags were used for synchronous Twitter discussions or if they were simply used to categorize and share online resources among the class. Additional required online interactions noted on syllabi included participating in live-tweeting events, reading and/or posting to a course or professor’s blog, tagging a professor in tweets, and working to improve individual Klout scores. Only one syllabus mentioned using a Facebook group, and none mentioned a required Twitter list.
RQ4 focused on what ways professors were utilizing external certifications to train students in analytics. Findings from the syllabus content analysis indicated that the majority of courses did not require students to complete an external certification that had an analytic element. The 28% that did incorporate certifications (n = 9) primarily required Hootsuite, Google Analytics, or Google AdWords. Results from the Twitter chat related to RQ4 included three participants advocating Google Analytics certification as one of the most valuable certifications in the industry. Additional online resources mentioned on syllabi to supplement classroom instruction included Code Academy, Google’s Analytics Fundamentals, Khan Academy, Lynda and the Marketing Analytics Initiative at Darden website.
RQ5 focused on the ways faculty utilized outside professionals or organizations to help teach analytics. Based on the content analysis of the syllabi, 66% of courses (n = 21) relied on outside professionals to share their expertise.
Also related to RQ5, the Twitter chat participants discussed the use of several outside resources, including the Institute for Public Relations, AEJMC, and other relevant academic or professional organizations. For example, PR pro Mors (2016c) suggested, “The @InstituteForPR has some great resources on website http://instituteforpr.org #PRAnalytics.” Twitter chat participants also mentioned outreach to professors and practitioners to serve as class speakers and/or to offer insight about teaching digital analytics. Professor Rowena Briones Winkler (2016b) said she wanted to “give a shout out to my @AEJMC_PRD friends” for being “SO helpful, re: teaching help! #soblessed #PRAnalytics.” Further, online tools such as Microsoft, Lynda, and Google Video were emphasized during the Twitter conversation. Professor Matt J. Kushin (2016) tweeted, that Microsoft has “an academic alliance program that provides many tools.”
During the Twitter chat, several themes emerged for assignments focused on teaching digital analytics, such as working with an actual client, using dashboards, performing listening projects, and generating reports. Educators stressed the importance of tying these assignments to real-world clients. The responses indicated that these assignments would give students realistic application by requiring them to submit client-monitoring reports and to develop strategic-communication recommendations based on insights gleaned from the data analysis. Responses from students who participated in the Twitter discussion indicated that assignments requiring the creation of a blog and the teaching of SEO best practices helped them understand digital analytics and drive traffic on their own websites.
During the Twitter chat discussion, both educators and practitioners advocated for ongoing opportunities to access, mine, and analyze data. These activities were thought to be key to creating an understanding of digital analytics in the practice of public relations. Professor Jamie C. Higdon (2016) said, “Integrate analytics throughout educational journey. Require SMART objectives and metrics plan for all major projects. #PRAnalytics.”
DISCUSSION AND CONCLUSION
Incorporating digital and social media analytic training is a crucial component of the future of social media education (Kent et al., 2011). This study examined specific pedagogical practices identified within manifest content on syllabi and in a Twitter chat among educators and practitioners in order to explore current practices and standards for analytic training.
To address whether courses were meeting employers’ demand for new analytic skillsets, it made sense to begin this study by examining learning outcomes stated on syllabi. Outcomes are designed to set the tone for a course and also identify the primary goals of student learning. Therefore, looking at student learning outcomes stated on syllabi is particularly important when examining an instructor’s approach to teaching digital analytics.
With the growing efforts to measure and evaluate digital activities, analytic competencies were a natural focus for social media and digital communication courses. Thus, it was expected that courses would have clearly identified learning outcomes for students related to digital analytics. However, very few courses had outcomes specifically mentioning analytics. While educators embedded analytic concepts and training within their courses, the wording of their learning outcomes did not reflect the focus on digital analytic competencies.
For example, only two of the syllabi reviewed mentioned KPIs, and only three mentioned listening or insights, which are basic analytical competencies. This initial finding indicated that, while analytics are taught in these courses, classes might not be focusing on this area, resulting in the course outcomes often ignoring or only leading to inferences about course expectations in this area.
With the Commission on Public Relations Education report (Toth & Lewton, 2018) identifying the value both educators and professionals place on analytics and measurement competencies, it seems important for educators to not only embed these competencies within courses but to also explicitly identify them as a learning outcome that students will be gaining through these courses. The Twitter discussion among educators and practitioners clearly conveyed the importance of public relations students and graduates understanding digital analytics.
Based on feedback from practitioners, existing research, and analysis of syllabi, the following are recommended learning outcomes faculty might consider incorporating in their digital analytics course syllabi:
- To identify the importance of online data in strategic planning and validating ROI.
- To identify online influencers and the major users of various types of digital and social media.
- To use analytics tools and technologies to capture data, generate reports and glean insights.
- To analyze ethical implications associated with interpreting and using online data.
- To discuss the impact of digital and social media on relationships between organizations and their stakeholders.
- To evaluate how stakeholder engagement on social media channels affects organizational operations.
- To articulate definitions and measurements of social media engagement and website traffic.
- To apply basic numerical and statistical concepts to evaluate, plan, and implement strategic digital tactics.
- To apply concepts and theories in presenting findings and in creating visualizations and dashboards to share with management/client.
- To become Hootsuite and/or Google Analytics certified.
One of the key areas that is suggested in social media education is for faculty to help students understand professional uses of the platforms (Kim & Freberg, 2016), including analytic information (Anderson & Swenson, 2013). Recognizing this need, the current study examined the ways in which faculty incorporate professionals into the classroom. Numerous educators who participated in the Twitter discussion shared that they either taught a digital analytics course or included digital analytic concepts in existing courses. The majority of syllabi indicated that faculty were including professionals by bringing them in for guest lectures; however, it was difficult to identify within the syllabi whether these professionals specifically addressed topics of analytics or other areas incorporated within the class such as campaign management, content creation, or platform functions.
An area of growth between professional organizations and the classroom has been the opportunity for student certifications on specific platforms such as Hootsuite, Google, and HubSpot (Kinsky, Freberg, et al., 2016). While this is an increasingly popular choice to help students gain competencies, the authors were surprised to find only about a fourth of the 31 syllabi mentioned an external certification as part of the course requirements. In addition to previous literature pointing to the value of certifications (e.g., Kinsky, Freberg, et al., 2016), three Twitter participants mentioned the importance of external certification. The availability of free, high quality, external training programs offered online (e.g., Hootsuite, HubSpot, Google) makes it easier for educators to provide up-to-date, industry-relevant preparation for students, and educators should take advantage of these programs. We predict their inclusion on future syllabi will increase.
Another key finding of the study is the lack of consistency in resources on the subject of digital analytics, including required textbooks and online sources. Syllabi included a wide range of industry books used to teach students about the subject (see Table 1). This is, in part, due to the content and structure of the course and whether analytics was the sole topic or if it was only a smaller component of the social media or digital curriculum. This inconsistency in required books is something that has been noted in previous studies looking at the social media curriculum (Kim & Freberg, 2016). Due to the nature of the rapid changes in the field, educators have to frequently update their sources. Textbook and resource choices are also impacted by where the class is being taught within a university (e.g., marketing programs may use different textbooks than public relations programs). In addition to books, many syllabi included references to required online articles, white papers, and PDFs, but few syllabi specified titles of these resources.
|Required Textbooks for Digital and Social Media Analytics Classes|
|Likeable Social Media, Revised and Expanded: How to Delight Your Customers||3|
|Measure What Matters||3|
|Groundswell Expanded and Revised Edition||2|
|Web Analytics 2.0||2|
|What Happens on Campus Stays on YouTube||2|
|Advertising and Public Relations Research||1|
|The Basic Practice of Statistics||1|
|Cutting-Edge Marketing Analytics||1|
|Digital Marketing Analytics||1|
|Good Strategy Bad Strategy||1|
|How to Measure Social Media: A Step-by-Step Guide to Developing and Assessing Social Media ROI||1|
|How to Use Google Analytics the Tutorial||1|
|Maximize Your Social: A One-Step Guide to Building a Social Media Strategy for Marketing and Business Success||1|
|Measuring the Networked Nonprofit: Using Data to Change the World||1|
|The Power of Visual Storytelling||1|
|Primer of Public Relations Research||1|
|ProBlogger: Secrets for Blogging Your Way to a Six-Figure Income||1|
|Real-Time Marketing & PR||1|
|The Social Current||1|
|Social Media Intelligence||1|
|Social Media Marketing||1|
|Social Media ROI||1|
|Socialnomics: How Social Media Transforms the Way We Live and Do Business||1|
|The Signal and the Noise||1|
|Your Brand, the Next Media Company: How a Social Business Strategy Enables Better Content, Smarter Marketing and Deeper Customer Relationships||1|
For RQ3, in examining pedagogical practices to teach social media and analytics, the authors examined other social media communication methods professors had incorporated in their syllabi to facilitate online interaction. Some of the interactions mentioned involved the use of a class hashtag, Facebook groups, Twitter chats, Storify, and live tweeting.
Future Research and Limitations
This study explored basic questions related to pedagogical practices and teaching social media analytics. In order to provide a foundational knowledge, the authors examined the manifest content of 31 syllabi and a Twitter chat among 56 public relations practitioners and educators. One limitation of the study is that the themes identified through the Twitter chat were based on a small number of affirmative responses; however, this is typical because of the dynamics of a Twitter conversation. Participants are unlikely to tweet the same theme to minimize repetitive content. Another limitation to using the chat data is that people who valued the topic were more likely to participate in the Twitter chat than people who were disinterested or didn’t value it.
Future studies may consider in-depth explorations through discussions with the faculty who are teaching the courses. Future studies could incorporate a mixed-method approach involving focus groups and interviews with professionals to determine if these digital analytics assignments were effective in preparing students for their new roles, perhaps following the methods of Gallicano, Ekachai, and Freberg’s (2014) study of an infographic assignment. In addition, testing the effectiveness of certification programs (e.g., Kinsky, Freberg, et al., 2016) for analytics could be beneficial as well.
Educators can also integrate and test how certain assignments are implemented and accepted within digital analytics by using guidelines and frameworks accepted in digital analytics associations and professional circles. Many frameworks, like the ones proposed by AMEC and DAA, can be integrated and used in current courses for lessons and used as inspiration to create assignments for students to test their knowledge and application skills in digital analytics. Further research could explore classes that use a specific framework for assignments and those that do not, and compare the end results. In addition, interviews with digital analytics professionals who are a part of these associations could be explored in future research to determine what they feel are key areas to emphasize, growing trends, and challenges and opportunities in the field.
Although course syllabi provided a general overview, often information seemed missing or vague. It does not mean faculty failed to incorporate certain pedagogical practices in their classes; their absence may indicate that they were simply not shared through the syllabus, and this could have been done with the purpose of keeping the class nimble as technology changes. Future researchers can learn from and anticipate coding challenges encountered in this foundational study. Direct conversations with professors would allow more specific details of each course’s content to be explored. In addition, in-depth interviews with practitioners who are experts in this area would allow for deeper exploration of digital analytics concepts, tools, techniques, and resources that could be used to teach the subject.
Social media pedagogy, especially exploring digital and social media analytics, is one of the emerging research concentrations that will help align the public relations profession and education community for the foreseeable future. A bridge can be created to help teach digital and social media analytics for both educators and professionals to agree on, for the sake of the young professionals entering the workplace. Like most research concentrations and perspectives within a discipline, identification of future directions, questions, and calls-to-action must be recommended to address some of the growing challenges and opportunities involved when it comes to social media pedagogy, especially in the area of teaching digital analytics.
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University Syllabi Used for Coding
|University Name||Course Names|
|Carnegie Mellon University||
|Cleveland State University||
|University of Florida||
|University of Georgia||
|Kent State University||
|Louisiana State University||
|University of Maryland||
|Massachusetts Institute of Technology (MIT)||
|Ohio Northern University||
|University of Oregon||
|New York University (Stern School of Business)||
|San Diego State University||
|University of Southern California||
|University of South Dakota||
|Texas Christian University||
|University of Virginia||
|West Texas A&M University||