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Big data on a smaller scale: A social media analytics assignment.

Fischbach, Sarah Zarzosa, Jennifer

Journal of Education for Business. Apr2018, Vol. 93 Issue 3, p142-148. 7p. 2 Charts.

Article

BIG data SOCIAL media VISUAL analytics INTERNET marketing EXPERIENTIAL learning

It is truly important for students to understand how to monitor online marketing buzz. This assignment, social media analytics, utilizes the content analysis research method to build student's in-depth understanding on how to evaluate and interpret user- generated content (UGC) to create social media campaigns. The authors adapted Resnik and Stern's (1977) coding scheme for UGC. Through experiential learning, students immerse themselves in data and analyze UGC. The assignment scored high in knowledge acquisition as a pedagogical tool. Finally, the authors provide an updated social media analytics coding scheme, guidelines for instructors, student rubric information, and student learning outcomes. [ABSTRACT FROM AUTHOR]

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Big data on a smaller scale: A social media analytics assignment

It is truly important for students to understand how to monitor online marketing buzz. This assignment, social media analytics, utilizes the content analysis research method to build student's in-depth understanding on how to evaluate and interpret user-generated content (UGC) to create social media campaigns. The authors adapted Resnik and Stern's (1977) coding scheme for UGC. Through experiential learning, students immerse themselves in data and analyze UGC. The assignment scored high in knowledge acquisition as a pedagogical tool. Finally, the authors provide an updated social media analytics coding scheme, guidelines for instructors, student rubric information, and student learning outcomes.

Content analysis; experiential learning; social media; user-generated content

The rise of social media platforms has challenged the position of a passive customer (Malthouse, Haenlein, Skiera, Wege, & Zhang, [ 13] ), providing marketing professionals access to individual preferences and likes. Consumers are reading product reviews, blogs, and other social media resources to gain information useful for making product decisions. The growth of social media, user-generated content (UGC), and social tagging continues to be a growing area of interest for academia, marketing practitioners, and students. Web 2.0 has disrupted traditional marketing strategies and increased the complexity of the consumer decision-making process. Our discussion provide resources on how to implement social media tools in the classroom to inform marketing students about brand experiences and loyalty. First, we argue how UGC affects branding, marketing communications, and consumer behavior. Second, we recommend content analysis as a research method through which students can examine the effects of UGC on marketing practices. Last, we propose a selective experiential learning project to answer the call to blend conceptual knowledge with technical skills.

Social media is a component of the online world in which people interact with one another, often changing roles from reader to author and seeking various benefits of social interaction (Moriarty, [ 15] ). UGC refers to media content created by users to share information or opinions with other users (Tang, Fang, & Wang, [ 20] ). Brand managers can use UGC such as social tags as a proxy to measure brand familiarity (Nam & Kannan, [ 16] ). Additionally, encouraging individuals to communicate through UGC improves when there is a shared vision (Li, Yang, & Huang, [ 11] ). As more companies become users of UGC tools, it is important for academic researchers to assist in establishing relevant guidelines for management and business leaders.

Communities are created around a theme, idea, or subject, and integrate the original postings, the links, and the readers' comments (Droge, Stanko, & Pollitte, [ 4] ). Of course, research on media differences and their effects on the consumer are not uncommon in academic circles. J. Huang, Su, Zhou, and Liu's ([ 6] ) research on viral videos found that managers need to consider purchase intention as well as their sharing intention as the platforms of communication continue to evolve. Droge et al.’s ([ 4] ) study on new product development found that the use of blogs can range from simply tracking a blogger’ community to advertising on influential blogs. Various studies have provided evidence that blogs have relational benefits in the eyes of the consumers (Kelleher & Miller, [ 8] ; Sweetser & Metzgar, [ 19] ). Additionally, previous research has found UGC can be also a measure of consumer interest and attention to determine future product demand (Liu, [ 12] ) and stock returns (Tirunillai and Tellis, [ 23] ). Obviously, the relevance of UGC in the business has come full circle and determining the effects within a business is crucial to success for the future.

UGC can also be a source for assessing brand personalities and brand evaluations (Nam & Kannan, [ 16] ), as it offers rich textual information. Past research has indicated that brand personalities and brand evaluations are indicators of brand equity, and thus firm valuation (Aaker & Jacobson, [ 1] ; Mizik & Jacobson, [ 14] ). Brand managers can monitor and adjust their brand image and brand positioning by tracking UGC to identify their points of parity and points of difference (Keller, Sternthal, & Tybout, [ 9] ).

Web 2.0 as a practitioner tool

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Web 2.0 is a second generation of interactive web-based tools such as social networks, blogs, content communities, forums, and content aggregators in which users are the primary content creators as well as consumers (Constantinides & Fountain, [ 3] ). Web 2.0 differs from the static Web 1.0 because users freely create, consume, and share content. There is a bottom-up structure that aims to harness collective intelligence, as in in the case of crowdsourcing. In this way, market power has shifted from producers to consumers and from traditional mass media to personalized fragmented media (Constantinides & Fountain, [ 3] ).

In the Web 2.0 age, this becomes increasingly difficult, as consumers as well as firms are content creators. Therefore, both firms and consumers are shaping the brand narrative. In this way, marketers no longer have full control over developing and maintaining the brand story. This can pose a threat to integrated marketing communications. Some UGC has begun to address the presence of the doppelganger brand modifications, which include the alteration of a company brand in a negative manner. For example, if the Intel logo wording changed to “Hacker Inside” from “Intel Inside” or McDonald's slogan was modified to “I'm Gaining It” from the traditional “I'm Lovin’ It.” Studies have found that these negative images that circulate throughout popular culture (Thompson, Rindfleisch, & Arsel, [ 22] ) were found to influence consumer beliefs and behavior (Giesler, [ 5] ). Through the usage of UGC, direct and personalized one-to-one marketing is possible and can aid in integrated marketing communications. As a result, UGC should be tracked and monitored to guide development of marketing communication strategies. As Schlee and Harich ([ 18] ) pointed out, “most students' current use of social networks for communicating with friends does not adequately prepare them for the use and evaluation of social media for business objectives” (p. 212). Our research objective was to address this gap and allow students to achieve marketing communication objectives by using social media.

Social media analytics Social media analytics is the practice of gathering data from blogs and social media websites and analyzing that data to make better business decisions (TechTarget, [ 21] ). Tools created by businesses help interrupt the big data; however, there is a significant learning advantage to understanding the companies' algorithms and deducing social media posts. Breaking material or concepts into parts and providing an interpretation of how the parts work together takes learning to a higher level (Anderson & Krathwohl, [ 2] ), and therefore a more abstract conceptualization. This assignment provides an important learning tool for comparing, organizing, and deconstructing information through an experiential learning process.

Students may come with preconceived notions and frames of reference for social media. They may believe their personal experience with social media gives them the knowledge they need to help an organization succeed. The process of the social media analytics assignment encourages learners to question their assumptions and frames of reference.

Social media analytics assignment Content analysis is a widely accepted methodology, which can produce valuable information on consumer attitudes and behaviors. Monitoring online communication is an important tool for gathering consumer information. Students learn the value of interpreting the information themselves instead of relying on the precalculated information from the analytic tool. By using data from consumer browsing behavior, one can directly examine search patterns rather than rely on self-reported data (P. Huang, Lurie, & Mitra, [ 7] ). Providing students hands-on experience with analytic tools can drive understanding and competence in the research topics.

Assignment overview The social media analytics assignment is executed in five steps. The instructor may use the assignment evaluation following an easy five-step process on social media analytics implementation into the classroom.

Step 1: Brand selection For the best learning opportunity, it is important to choose well-known organizations for comparisons between group assignments. The most effective experience to date includes working with a monobrand. A monobrand firm is defined as a single brand that represents one primary business (Mizik & Jacobson, [ 14] ). Students explored differences among brands within a similar product category and under the same umbrella brand. A few examples of monofirms include Gap, Coca-Cola, LVMH, or Treasury Wine Estates. Social media analytics and the understanding of big data is fairly new; however, content is building every day. There is more content to download for large companies giving students a better experience of sorting, cleaning, and making the overall content analysis more valuable.

Step 2: Data download Big data can seem overwhelming to students in a social media project. Over a 30-min in-class discussion, students were directed on how to collect all available historical data on a brand category. For example, one tool students can explore is socialmention.com, a site where students enter in the brand name in the search bar and hit enter. The analysis covers over 100 media sources archived within a 12-month period. Another tool that could be used is qunitly (quintly LLC, San Francisco, CA); however, students will need to sign-up for a free trial in order to use all of its resources. Qunitly complies social media for up to five social media sites (i.e., Facebook, Instagram, YouTube, Twitter, and LinkedIn) into one location for the students to view. It is best if the students compile data over the same period (one week) for consistently in content. The total number of entries per brand can be beyond 1,000+; however, we recommend having student groups only code 50-100 sources.

Under socialmention.com, students will be quick to notice the precalculated category percentages on the left side of webpage. These categories include strength, sentiment, passion, and reach. Hovering over the calculations reveals a definition of each term. For example, strength is the likelihood that a brand is being discussed in social media. We encourage pointing this out before students start to assess the social media content. The students will also be quick to notice that not all the content relates to the product, which skews the system generated strength, sentiment, passion, and reach calculations. At this moment, students will begin to realize the importance of being able to analyze and interpret the data themselves to find the real customer conversation online.

Students are directed to scrub the data and delete duplicate entries as well as entries that do not apply to the brand. For example, when using the brand Patagonia, there were results for the place Patagonia in South American. Google Trends (http://www.google.com/trends/) is a tool that may help student narrow down the appropriate terms related to the company to narrow the search. Google Trends allows an individual to enter in a company name and find the top five keywords associated with the brand name. For example, when entering Patagonia into Google Trends an individual may wish to use the top keywords for a project (e.g., Patagonia Clothing Company or Patagonia Fleece Jacket). These top keywords can then be added into socialmention.com to refine the social media analytics results. Additionally, within socialmention.com it is possible to use the top keywords found on the left-hand side (e.g., Patagonia, jacket, fleece, North Face, sleeping bag). Students may simply click on the keywords to narrow the search. We are certain there are many systems that could be used in the classroom, such as quintly, and encourage educators to apply the same assessment guide to any tool that they find useful in their classroom. The programs continue to grow and populate resources for our students; however, step-by-step processes can help the students gain the most accuracy.

Step 3: Organizing and cleaning data Through this step, students genuinely start to understand the value of big data. Cleaning the data includes removing any data that are irrelevant to the research topic. Common data that are deleted include content in foreign languages, misinterpretation of brand keywords, and duplicate information. An additional tool available through

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quintly includes the option to sort information based on media sources (i.e., Twitter, Facebook, and LinkedIn) that enables the student to examine both volume and content within one specific source.

The coding criterion for the social media analytics project was derived from research conducted by Resnik and Stern ([ 17] ). Originally developed as a list of 14 evaluative criteria to analyze information content in television advertisements, it has been modified for use with UGC (Appendix A). We included criteria for sentiment and reach in the student project.

Step 4: Coding training The most rewarding component of the social media content analysis is training students on the value and importance of content analysis. Content analysis provides deep insight into online conversation; however, it is seen as tedious and repetitive. Training for coding is vital for proper comprehension of the data collection. As students start to code information, they will interpret the material slightly different based on their own past experiences. This type of interpretation is very important for critical thinking. This allows students to be able to build insight into their own data collected. Word count and sentiment algorithm programs may lead a company in the wrong direction while individuals interpreting the data can be a powerful tool for any organization.

Step 5: Data analysis For the final step, we recommend using a variety of quantitative and qualitative options to analyze students' organic findings. Quantitative options include running descriptive statistics such as sum, median, and mean as well as a simple correlation analysis for each of the coded categories. In the past we have used Excel as well as SPSS to run the data analysis. In a more advanced marketing research class we recommend having the students upload the data into SPSS to run test out the system; however, these same results can be calculated in Excel. Qualitative analysis will include the students group discussion about the social media platforms and most reach. For example, if a post on Facebook was shared 10,000 times, it is a more powerful post than one that included many of the coded categories but did not have as many shares. It is important for students to see the effects of the posts and interpret their own critical thinking into the process.

Sample grading assignment We provide some recommendations for assessing the social media analytics assignment.

Selection & Cleaning (25%): It is our recommendation to give students a portion of points for selection of company and scrubbing the data. It is often seen as tedious, but students soon realize the importance and value in misinterpreting data analysis.

Coding Training (30%): Training students on the Resnik and Stern (1977) coding scheme should not take more than a 30-min class period training session. It helps to run through a couple of examples in front of the class then let the students work on it in their groups to get a hands-on experience. This enables students to ask questions related to their specific brand as well as reduce fear of misunderstanding the analytic systems.

Interpretation (25%): This is an opportunity to dig into the data and look for statistical results. Qualitative and quantitative approaches both work well. Instructors can require students to find statistical results such as correlations and t tests. However, if the course does not require statistical analysis, students could assess mean, median, and mode looking at the word counts and common themes throughout the data assessed.

Conclusion (20%): Students present their findings and results to the class or have the students use the social media analysis findings to build their social media marketing campaigns.

Learning outcomes Students were very receptive to the assignment as shown from the survey results. A total of 84 students have participated in the assignment from two universities and 83% completed the learning outcome questionnaire (n = 70). There was no prevalent differences across the universities, and therefore they are combined in the final analysis found in Table 1. Students were asked to compare the social media analytics assignment to traditional lecture over social media analysis. A one-sample t test was analyzed to assess the differences with the null (M = 5.0). The average scale calculations find that the students had higher levels of effectiveness with the social media analytics assignment than a traditional lecture method across 10 questions (M = 4.16, SD = 0.91, t(69), p <.00). Students also conveyed the assignment was challenging, addressing that the assignment took a great deal of effort; however, they also expressed confidence in their ability to apply this type of project to another company. Overall, students experienced a positive learning experience. The students really grew to know the company and the data. This active experimentation with the data led students to reflect on their own interpretation of the data.

Comparing social media analytics to traditional social media lecture.

Experiential learning compared with lecture (n = 70) M ± SD t df 1. More challenging than listening to a lecture 4.14 ± 0.7011.1969 2. More enjoyable than listening to a lecture 4.00 ± 1.26 7.20 69 3. More productive than listening to a lecture 4.33 ± 1.02 5.98 69 4. Worth the effort 4.17 ± 1.12 6.75 69 5. Should be assigned in future courses 4.19 ± 0.85 8.68 69 6. Understand the importance of brand image 4.19 ± 0.79 8.68 69 7. How branding enables competitive advantage 4.19 ± 0.5912.5169 8. Preparation of business presentation 4.14 ± 0.89 8.80 69 9. Ability to work in groups 4.19 ± 1.04 7.09 69 10. Ability to apply to another company 4.10 ± 0.8110.1269

Note. Responses were rated on a 5-point Likert-type scale ranging from 1 (disagree) to 5 (agree). All t values are significant at p <.00.

Conclusion Consumers consume, create, and share content among each other and marketers should monitor and shape the conversation. In today's market,

Despite the numerous benefits of the social media analytics assignment, there are limitations associated with the assignment. First, the

The selective experiential learning project answers the call to blend conceptual knowledge with technical skills. Although others may app

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By Sarah Fischbach and Jennifer Zarzosa

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