As marketing educators, we have all found ourselves laser-focused on our classes, each with its detailed theories and concepts; however, as the Einstein quote above demonstrates, it is important to continuously open ourselves up to new ideas to grow and learn. This is especially true for our college students, who often find themselves choosing (or avoiding!) a specific path for their studies and for their career paths. Dugan and Allen (2016) call for marketing professors to assuage fears of numbers and math anxiety and train students for the data analysis required in today's marketing careers. It is not uncommon to hear students openly dismiss analytics, given its reputation for quantitative skillsets. Yet, those familiar with analytics recognize that analytics entails much more than just the numbers. Marketing majors will face analytics in almost every career path they choose (He et al. 2022; Tesseras 2021), yet many dismiss it without a second thought (Killian 2023). Given the rapidly changing digital environment, it is imperative for marketing graduates to possess strong analytical skills (Kurtzke and Setkute 2021) and to understand how analytics can impact decision-making.

As university marketing programs seek to grow student interest in marketing analytics education to meet industry demands, we must first focus on student awareness and building interest and excitement before diving into the nitty–gritty details. Students may initially choose marketing (over accounting and finance) because it is seen as the major with less “math” (Graham et al., 2020). In addition, many marketing students may brush off analytics courses and programs as too numbers-focused. Still, all students should understand the role of analytics in their future careers and its impact on organizational decision-making (Spanjaard et al. 2018). Regardless of specific career paths, all students will encounter analytics in their jobs, even if only through setting objectives and performance evaluations (Kurtzke and Setkute 2021; Schlee and Karns 2017). The problem is when students opt into non-analytics marketing paths, they are connected with non-analytics faculty. Often, these faculty acknowledge the importance of analytics, yet if it fits outside their course theory and concepts, the idea of introducing analytics can be quite intimidating.

This paper aims to help marketing instructors spark interest and awareness of marketing analytics to a broad, novice-level audience. While extant literature in marketing education has demonstrated scholarly efforts to improve pedagogy at both the program and course level, the marketing education literature currently lacks evidence and ways to bridge the initial gap to marketing analytics from traditional marketing courses, especially for non-analytics-focused courses. Specifically, this paper presents a classroom activity focused on building interest in and awareness of marketing analytics, which can be completed in a single-class period in any marketing course by any instructor, regardless of expertise in analytics.

The objective of the classroom activity proposed in this paper is simple: to spark students' awareness of marketing analytics in their day-to-day lives and future marketing careers. This paper is not about how to teach marketing analytics in-depth, nor is it about the basics of analytics; this paper is about how marketing professors, who may have no background in analytics, can help students recognize the value and impact of analytics and spur interest for students to pursue future study of analytics. This paper makes valuable contributions to pedagogy in marketing and analytics by addressing students’ lack of interest in quantitative marketing courses and the intimidation associated with the word “analytics.” The proposed activity allows faculty without specific analytics expertise to engage in a marketing-focused dialogue and help students consider how marketing analytics is a critical component of data-driven decision-making.

We seek to make the world of marketing analytics less intimidating for marketing students and to create a more fun and friendly entry point. Although course and curriculum development are not the goal of this paper, we believe a broader audience can benefit from this research. This paper advances the pedagogical research such that the structure could be followed with the examples and questions in this module adapted to help with stimulating interest and increasing recruitment efforts for a variety of programs that often must overcome a stigma or misperception from students, such as sales, ethics, or cybersecurity. Through this new pedagogical avenue, students’ interests may shift, and more doors for career options may be opened.

Importance of analytics in the marketing curriculum

The concept of analytics, defined broadly, is “a process that employs various techniques to analyze and interpret different forms of data to enable better decisions and improve firm performance” (Delen and Zolbanin 2018, p. 188). The use of analytics has grown in popularity due to the prevalence of big data and the subsequent demand for skilled data-driven workers for core business functions, specifically, marketing (Mintu-Wimsatt and Lozada 2018; Houghton et al. 2018; Kurtzke and Setkute 2021). Marketing analytics is now recognized as its own entity under the broader analytics umbrella. For example, the use of data analytics to gain strategic recommendations that provide marketing insights to optimize firm performance can be referred to as marketing analytics (Liu and Levin 2018). Scholars have analyzed the advancement of marketing analytics across various topics, including using marketing analytics to understand customer experiences and preferences (Bozkurt et al. 2021; Simões and Nogueira 2022), building brand relationships and loyalty (Damberg et al. 2022; Yee et al. 2022), and the utilization of analytics for evaluating organizational capabilities, business performance, and marketing return-on-investment (ROI) (Harmath et al. 2021; Mathur et al. 2022).

Considering the rise of marketing analytics and how the vast use of technology and big data have continued to transform the field of marketing, academic research has recently highlighted a pressing need for marketing education to include training in analytics skills (Kurtzke and Setkute 2021). Many scholars advocate that analytics is an essential function within marketing practice and, as such, should be a part of the marketing curriculum in higher education (LeClair 2018; Moorthy et al. 2015; Spiller and Tuten 2015). Thus, calls to address the importance of including analytics in developing marketing students' skill sets and competencies exist in the marketing education literature (Iacobucci et al. 2019; Mintu-Wimsatt and Lozada 2018). A review by Iacobucci et al., (2019) concludes that, due to the increased demand and scope for marketing analytics required post-graduation, collegiate marketing departments need to include marketing analytics in required curricular offerings to provide students with essential career advantages.

In pedagogical research, many scholars advocate for and provide ways to implement marketing analytics into the collegiate curriculum. Specifically, a recent systematic review of the current state of analytics education in marketing within academia (Ye et al. 2023) highlights several academic publications focused on overall course and curriculum development (LeClair 2018; Houghton et al. 2018; Wilson et al. 2018; Liu and Burns 2018), as well as specific assignments for implementation into the marketing curriculum (Haywood and Mishra 2019; Kim 2019). Furthermore, extant literature explores the role and fit for analytics within marketing education, including the individual competencies students need to be proficient in skills used for marketing analytics (Weathers and Aragón 2019), technology integration in marketing courses (Crittenden and Crittenden 2016), and how to use analytics in course design and teaching tools (Kim 2019; Liu and Burns 2018; Wilson et al. 2018). Additionally, Liu and Levin (2018) introduce a program-wide curriculum mapping and design for teaching analytics across different marketing courses in a progressive approach within a university’s marketing program.

Importance of analytics in marketing careers

According to industry reports, there has been a significant annual increase in the demand for marketing professionals who possess analytical skills (Tesseras 2021). Combining this need with the growing emphasis on data-informed marketing decisions (Johnson et al. 2019), modern marketing students must understand how analytics will be woven into their day-to-day marketing careers. In fact, Schlee and Karns (2017) found that marketing graduates with higher analytical acumen and abilities are more likely to receive higher starting salaries. Considering the ever-increasing amount of data available to organizations, marketing professionals will experience marketing analytics in their careers through social media and consumer brand preference (Chandrasekaran et al. 2017; Muñoz and Wood 2015), digital marketing and customer experience management (Bozkurt et al. 2021), targeted marketing (Cowley et al. 2021), and among many other data-driven marketing decisions (Petrescu and Krishen 2023). Furthermore, in a marketing career, utilizing marketing analytics and metrics is essential for obtaining market insights, optimizing performance, and remaining competitive (Krush et al. 2016; Wilson 2010). Given this, industry professionals and marketing educators have consistently recognized that possessing strong quantitative analytical skills is a key factor that influences a student's job prospects and success in the workplace (Finch et al. 2013; He et al. 2022).

According to extant research, despite the push from both industry and academia for the growing significance of quantitative skills in marketing, this increasing importance does not correlate with the level of interest marketing students have in these courses (He et al. 2022). Graham et al. (2020) find a stark contrast between the recommended courses for business professionals (high) and student interest (low) in analytics and advanced research courses. In this work, the authors find many students feel quantitative courses in the marketing program have a heavier workload compared to other options; however, choosing a non-quantitative marketing course for short-term relief may result in missing out on important skills that are applicable in the workforce in the long run. Industry practitioners, marketing educators, and business schools need to investigate the factors that influence this lack of enthusiasm among students. This is especially crucial considering the significant growth in hiring for quantitative marketing analytical jobs (Bureau of Labor Statistics, U.S. Department of Labor 2021).

While the increase in workplace demand forces business schools to adapt to the growing need for analytics education (LeClair 2018), it is critical to recognize that the demand for these skills points to the need for all students to be aware of how analytics will factor into their careers. By increasing students' awareness and interest in analytics, marketing programs have a broader audience to promote their marketing analytics offerings and improve their graduates’ career preparation and earning potential.

This paper makes a unique contribution to the existing academic literature focused on teaching marketing analytics. Specifically, existing research provides program-level recommendations, curricular sequencing recommendations, and projects that students can complete to advance their analytical skills. However, our extensive literature review found no articles that focus on stimulating interest in analytics among marketing students or building a heightened awareness of marketing analytics and how these data can drive decisions made by both consumers and organizations. The classroom activity proposed here serves to fill that void.

Analytics IRL (in real life): the classroom activity

As mentioned previously, the proposed activity aims to stimulate the interest and awareness of marketing analytics for marketing students. It is strategically designed so that no previous knowledge of analytics (in depth) is required for its facilitation. In fact, for the present study, the activity was delivered by faculty who do not claim any expertise in analytics but rather focus their efforts on sales and integrated marketing communications. Accordingly, this activity can be used for a one-off class session to strengthen awareness or recruit students to marketing analytics courses, or it could also be used as a first-day activity for an introductory analytics class to stimulate conversation and collaboration among students early on.

The proposed activity encompasses five specific examples of marketing analytics that are applied in ways most students regularly experience without considering what is happening behind the scenes. Following each example, individual and group discussions ensue. An outline of the course activity is provided in detailed steps below. The Appendix provides the slide deck that was used to accompany the class lesson and activities.

Learning objective

Recognizing that the aim is to spark the students' interest in discovering the power of marketing analytics, our class activity has the students consider marketing implications from multiple points of view. Specifically, students ponder questions from both the organizational and the consumer perspective on the impact of marketing analytics for each of the five examples. After going through specific examples, students will consider these same considerations more broadly. For instance, from the organization's perspective, questions like “How do you believe the organization is using analytics to drive consumer outcomes?,” “Why would they do this?,” and “How does this example drive ROI?” are discussed. Then, from the consumer's perspective, students will be encouraged to dig into questions such as “What individual consumer decisions might this influence, in both day-to-day life and future decisions?” or “How, or in what ways, might this impact consumer perceptions and attitudes? Could it also impact behaviors?” In the sections below, we provide several examples, followed by a debriefing guide with teaching notes to assist instructors with potential answers to these questions. As described below, this activity should be easily implementable for any instructor.

Step one: discuss and define marketing analytics

For this activity, we define marketing analytics as “the practice of managing and studying metrics data in order to determine the ROI of marketing efforts and identify opportunities for improvement” (Adobe 2022). Before beginning the activity, instructors should divide the class into groups of 5–8 students. To begin the lesson, students are asked to brainstorm what they think “analytics” and “marketing analytics” mean (Slide 2). Faculty should allow 2–3 min for the students to brainstorm, discuss as a group, and write down ideas. Then, the instructor should lead a class-wide discussion about the meaning of marketing analytics and how it falls under the umbrella of analytics. The critical part here is to avoid expecting the students to find the correct definition of analytics but instead to generate discussion among classmates to set the stage for the student's awareness of analytics (or lack of) and how marketing analytics exists broadly. Instructors might facilitate discussion with examples of analytics that are implemented into everyday life, such as Spotify recommendations, how long a crosswalk is programmed to last, or a geotargeted mobile coupon offering. After discussing, the instructor can use the accompanying slide deck to briefly discuss the definition of marketing analytics (Slides 3–4).

Step two: provide and discuss five examples

The classroom activity consists of five examples designed to get students excited and spark interest in marketing analytics. The accompanying slide deck (see full slide deck in the Appendix) is designed for instructors to “plug and play” each example presented in this section. Instructors can choose to present all five or a subset of the examples offered. The following section will provide an overview and instructions for each example, as well as a teaching note for instructors on the discussion questions. As of spring 2023, these examples were found to be relevant and interesting to students.

Example #1—smart watches

Overview and instructions

This discussion-based activity uses smartwatches (i.e., Apple Watch) to spark interest in analytics (Slide 6). First, students were asked to compare personalized fitness goals (e.g., daily move goals, Apple-generated monthly fitness goals) and their varying watch displays (e.g., what apps they use on their watch, what they display on the watch face, preferred layout). Groups without smartwatch experience also discussed their smart device's (i.e., smartphone’s) display, preferences, and user interface. This example generated lively discussion of how their daily behaviors are often impacted by the analytics provided by their smartwatch or device. For example, student groups discussed how their daily physical activity is often influenced by the tracking, notifications, and prompts provided daily on their smartwatches. Students also recognized the personalized nature of the analytics offered with smartwatches.

Teaching notes and discussion questions

While it is nice if at least one individual is wearing an Apple Watch in each group, this is not necessary. Several groups did not have one in our delivery of this classroom activity, yet they could still discuss how analytics is used with smartwatches, including other brands such as Fitbit, Garmin, and Samsung Galaxy. Interestingly, one student group who discussed the impact of the “steps tracking” feature on their Apple Watches had a student in a wheelchair due to a recent accident. He commented and showed the group his personalized feature offered by Apple to track his “pushes” rather than steps. This group shared with the class how analytics was personalized for physical activity due to varying circumstances.

Below are discussion questions and sample answers for the questions provided (Slide 7). In our experience, it is important to encourage students to closely compare and explore how they use their devices similarly and differently among their classmates before digging into the business application questions. Spending more time on this during the first example helps to streamline the activity experience for the next four examples.

Personal (consumer) application

  • How have you personalized or customized the face and apps you use on your smartwatch?

  • Which of your behaviors do your watch settings influence? How have you seen your behaviors evolve or change because of these settings?

  • For Apple Watch users: compare your monthly fitness goal with those of your classmates.

Business application

  • From a strategic perspective for this brand, why might watch faces be customized for Apple Watches (they aren't for Fitbit!)?

  • How does this apply to business in general? How does this apply to marketing specifically?

  • When might it be worth customizing/when is it not?

  • This technology was an enormous expense for Apple, and why was/is it worth it?

Responses to these questions varied and stimulated a lively in-class discussion. For the consumer-focused questions, many students will respond based on their own personal experiences. Instructors should encourage responses about app preferences and types of information consumed with this type of device, loyalty to smartwatch brand to keep fitness goals/streaks aligned, complementary integration of information with other devices (e.g., Apple Watch to iPhone), and ease of tracking information. Additional talking points may include how content can be more organized and convenient for lifestyle, that this component encourages people to make goals and reach them, consumers purchase the product more with incentives, the more data that a consumer puts into the product the more utility they get from it, and interactive programs with predictive analytics encourage motivation to reach goals. Additionally, other responses may dig into the more specific business- and marketing-based knowledge, such as how firms prioritize which technology/features to update, how output/fitness goals are only as good as the input data/activity of that use (i.e., how input data are necessary for predictive analytics such as fitness goals), how firms learn about their target market, how firms can encourage repeat usage, how firms see who the main users are, and how they are using the product to assess ROI of different features and to fuel research and development of future product iterations.

Example #2—Netflix

Overview and instructions

Most college students stream digital content; the authors chose Netflix as it is one of the most popular streaming services. For this activity, students were asked to open their Netflix accounts and profile and search for two different shows: “Survivor” and “Designated Survivor” (Slide 8). Upon searching for each show, students compared the thumbnail featured for each show on their profiles. Students with access to multiple Netflix profiles were encouraged to view the same shows to compare the thumbnails and who each could be targeting. After viewing the thumbnails, students were asked to discuss themes of the varying thumbnails they noticed versus those of their classmates.

Teaching note and discussion questions

During this portion of the class exercise, the student interest and excitement seemed to ramp up as many were not previously aware of the personalized targeting of Netflix thumbnails targeted due to individualized analytics. Students commented about their thumbnails and why they could see the specific thumbnail rather than those featured in their mom or dad's Netflix profile, even though they are promoting the same show. Possible themes of thumbnails include rom-com (e.g., pictures of couples) or adventure (e.g., an individual man usually doing something with speed or force), thrillers, or nature. Below are discussion questions and sample answers that can guide the discussion of this example of marketing analytics (Slide 9).

Personal (consumer) application

  • Have you ever thought that Netflix’s recommendations are highly tailored to your interests?

  • Are the thumbnails of your recommendations the same images as your classmates’ thumbnails, or do they have a different theme (e.g., romance, adventure, nature, animals)

  • What impression would I have of you if I just looked at your Netflix account?

  • How do the tailored recommendations influence what you watch?

  • How do your recommendations, and the thumbnails for the same shows, differ from those of the other people with a different profile in your same Netflix account?

Business application

  • Why does Netflix do this?

  • What behaviors might this influence? How could it change varying behaviors?

  • How does this apply to marketing?

  • From a strategic perspective for this brand, why might the content recommendations, as well as the thumbnails, be customized? When might it be worth customizing/when is it not? What is the ROI?

Responses to these questions lead to a rich discussion about how Netflix’s recommendations influence students’ decisions to watch different shows and how consumers narrow down their choice of which show to watch due to the recommendations. Specifically, this targeted content influences what consumers see each time they open Netflix, and they are likely to find a show or movie they are interested in watching right away, which encourages repeat behavior (watching) and more usage (loyalty). Additionally, students are likely to express great surprise about the personalized thumbnail images and how they drive interest to watch something. From the perspective of Netflix, the conversation will likely focus on loyalty and the cultivation of customized accounts which can encourage subscribers to watch a wider variety of shows. Netflix can also track the success of their own shows produced, see what genre people are watching the most, and tailor new releases to audiences that enjoy the specific genre. Additionally, the data Netflix generate will help predict what shows will do well with whom and analyze what genres are watched most to help Netflix decide what content they want to produce or access.

Example #3—targeted advertising

Overview and instructions

For this activity, students were prompted to visit four websites on their personal computers or mobile phone (Slide 10) and compare the ads they saw. Students discussed and compared ads they saw across all four websites.

Teaching notes and discussion questions

While students are typically familiar with re-targeted ads, this activity opens their eyes to basic targeted ads. Based on ad spend, some websites will have more finely targeted ads than others. As of spring 2023, the websites recommended in the slides provided a wide variety for discussion. Students seem shocked at the differences in ads generated between peers on the same site and across sites for the same person. They also found it interesting to students that the time it took for the ad to load was not due to a slow website or connection but, rather, a result of real-time bidding. Below are discussion questions and sample answers for the questions provided (Slide 11).

Personal (consumer) application

  • Have you ever thought your phone was listening to you based on the ads you were served?

  • How well does the product/service advertised here match your interests?

  • Who is the likely target consumer of this product/service? Does that description fit you?

  • In what ways does the product/service advertised align (or does it not) with the website it is on?

  • How often do you notice these mobile ads? Do you think they influence your behaviors?

  • How finely targeted does an ad need to be for you to consciously pay attention to it? What happens when it is not highly targeted to you—do you even notice?

Business application

  • What is the value of marketing via digital ads?

  • From a strategic perspective, why might these ads be customized?

  • What types of products/services do you think have the best ROI for targeted digital ads?

  • Thinking of the brevity of these ads, what do you think their primary goal is?

  • How might a business track the success of its digital ads?

Responses to these questions will vary widely depending on students’ familiarity with digital advertising. Students should be guided to consider whether these ads are likely to build awareness and familiarity through repeat ads or whether they are likely to directly influence a purchase; in fact, if it fits with the course content, an opportunity will arise for a discussion of marketing “touch points” and how many “touches” are generally necessary to move a consumer to action. Students are likely to point out items they have been shopping for online and how these ads are of those items or similar ones; at this point, tracking and cookies may bridge an interesting discussion. Students tend to be surprised when they visit a website they have never even heard of, and the ad on it relates well to them. Consumer privacy, social listening, and brand alignment are other topics that are likely to arise in the discussion. To integrate marketing analytics, instructors should discuss how digital advertising allows for A/B testing and quantifiable measures of impact in deciphering ROI for these campaigns.

Example #4—social media analytics

Overview and instructions

For this activity, students were asked if they managed any social media pages for student organizations or internships (Slide 12). They were prompted to discuss what analytics they could see and how they used those analytics. Further, students were then encouraged to discuss personal social media and how/if analytics influence their actions on social media. Some students in the group had managed social media pages and could share the analytics provided to them related to the most popular viewing periods for followers. Discussions surrounded these types of analytics and how changes to posts and interactions with followers are impacted by the analytics provided.

Teaching notes and discussion questions

For this activity, each class had at least a handful of students who currently managed social media pages at a business/organization level or had managed one in the past. They logged in to their pages and then viewed the page metrics available for a behind-the-scenes discussion of page impact. In this case, students discussed the different metrics, their aims, and the ways they are used to inform the leadership team and drive future decisions related to content. Depending on how many students have this experience in their class, the instructor may wish to discuss this one as a class versus in small groups. Instructors for this exercise were surprised at the experience students were able to share with peers regarding the best days/times to post on different social media websites and their tips for generating conversations among followers. Below are discussion questions and sample answers for the questions provided (Slide 13).

Personal (consumer) application

  • What analytics can you see on the social media accounts you manage or that you are viewing?

  • Which analytics help you identify depth, and which help you identify breadth with respect to your audience?

  • Which metrics do you pay the most attention to, and how do you use those metrics to make decisions about what content you post in the future? (Dig in here!)

  • For personal social media, how do different interactions across different media influence your content choices and habits for interacting with others? (For example, how do you use LinkedIn vs. Instagram? Do you ever hide likes? Do you respond to influencers?)

Business application

  • What metrics should organizations pay attention to as a way of shaping future content for the organization?

  • How might these metrics help a marketing team refine their organization’s “voice” and “tone” on their social channels?

  • What metrics should organizations pay attention to as a way of deciding when to post content and what kind of content to post when?

  • How might a social media manager choose which metrics to prioritize, and how might they “sell” that rationale to their leadership team?

Students’ responses to these questions will vary greatly depending on their personal experiences with social media. The questions above allow for a surface-level discussion as well as a deep, immersive discussion if time permits. Students are very intrigued by this because they like to see firsthand which times/days are “best” to post (and to debate over what makes it a “best” time, which leads into a discussion of whether to display or hide “likes”). All students can relate to lying in bed scrolling through social media late at night, but many will say this is a time when they are also less likely to interact with posts; so, again, a discussion about the right type of content at the right time is an evolving dialogue. Students also may discuss how hiding “likes” on their personal social media accounts leads people to post more, how they use social to see what is trending, and that most of them have made a purchase related to something they saw on social. Regarding the business angle, instructors should encourage students to discuss how content effectiveness can be tracked, how to identify what demographics are being reached, and how to tailor the content delivered to consumers. Additionally, in many classes, a conversation may arise about the responsibilities (and related stress) of a social media manager, from analyzing what posts get the most interaction and overseeing page mentions stemming from other sources to answering direct messages at all hours of the day and night.

Example #5—the Photos app

Overview and instructions

Photos and videos on smartphones are a huge part of all college students' lives. They are used to relive fun experiences, recall important information, and provide seemingly endless entertainment. It is not uncommon for students to spend an hour scrolling through photos and videos on their phones. However, many students do not realize the amount of data that is stored within each image or the analytics that goes into these photos and compiling them. Smartphones allow photos to be searched by people, locations, moments, categories, dates, text, and other metadata. Phones now autogenerate and recommend videos made up of images based on location, timing, or contacts. After prompting students to open the Photo app on their phones, students are encouraged to discuss and share how their phones have used analytics with their photos (Slide 14). Students were asked to complete different searches within their photo app and to look at the information contained on each photo.

Teaching notes and discussion questions

As mentioned above, the most common way a phone uses analytics is through data embedded within each photo, including faces, locations, and dates. Students also discussed the ability to use the search function on their phone to find pictures of various places, things, and people; interestingly, most students were not as experienced with the “search” function as we expected, so instructors should come up with some examples of what they might search for (e.g., pizza, Chicago, mom). Instructors should be mindful that, while analytics are heavily involved, this example is also mixed with Artificial Intelligence (AI). In this example, the analytics are driving the AI. Below are some relevant questions and sample responses to aid in the discussion (Slide 15).

Personal (consumer) application

  • How many photos and videos do you have on your phone? How do you interact with them?

  • What type of data are your photo memories and videos based on?

  • Try using the search function for an animal, location, word, or person; what do you find?

  • How has the search function and the data embedded into photos changed the way consumers interact with their photos now that it is more than simply a generic photo album?

Business applications

  • Advancing the Photos app was a large investment. How do artificial intelligence and marketing analytics combine in a powerful way here, and how do they influence customer experience?

  • From a strategic view, why has Apple made the Photos app have these search features and the ability to generate a memory video of just about anything? What is the ROI?

  • From the perspective of a content creator, how might the “search” function assist in your role?

  • What might Apple (or another phone manufacturer) do with all this information they now have about their consumers through their photos/videos?

  • How might the analytics within the Photos app drive future research and development efforts of the firm?

The discussion here can go in many directions depending on the course content and focus an instructor wants to have, but a main point for instructors to reiterate is how these analytics can influence user experience. Additionally, identifying factors that may have led Apple (and others) to make this investment in the Photos app will help students dig into a discussion about capturing data and ROI. From a consumer perspective, these features encourage users to keep taking pictures, make consumers feel good, and give personally pleasing memory videos to further attract the use of the Photos app. From a business perspective, these features can increase brand loyalty, keep users actively using their camera roll, encourage consumers to use their phone camera rather than a separate camera, track where consumers take the most pictures, understand which photos are kept and which are deleted as a way of improving future AI efforts (for example, compiling that data likely have led to how AI automatically chooses the “best” moment to display of a photo taken in “live” mode).

Choose your own example

To wrap up this portion of the classroom activity, students were also encouraged to brainstorm other places where analytics may be woven into their day-to-day lives (Slide 16). Sample answers included mobile ordering, TikTok-tailored content, Amazon recommendations for purchase, and the Starbucks App for mobile ordering. In classes with more time for this activity, students can be asked to create their own discussion questions from both a consumer perspective and a business perspective. Again, the focus is on how consumers are influenced by these examples of marketing analytics, how businesses can benefit from such usage of analytics, and how firms determine the ROI related to these uses of analytics. Additionally, for educators who wish to expand this lesson and activity across two class periods or would like to have additional resources for more specific data examples, we have provided some optional youtube.com video links that can be accessed. by students or faculty.Footnote 1

Step three: marketing analytics recap and relevance to career

After lots of discussion and energy around the various classroom activities, instructors bring the class back together to review the definition of analytics (Slide 17). While facilitating the class discussions and activities, instructors can be flexible to discuss the marketing implications with each activity or at the end. It is important to let the students see that all the data “inputted” for the company to analyze can then be used to help improve customer loyalty and retention. Further, the instructor should then bring the discussion to career relevance (Slide 18). Conversations around this should focus on how analytics could be used in their first job, regardless of specialty. For example, many hiring agencies will use social media and analytics to screen applicants. Further, job goals and outcomes are typically set using analytics. The critical part here is to help students that regardless of their career path, analytics can and will be used to set goals, determine compensation, and perform many other daily job functions.

Step four: key takeaways and promote college/department offerings in marketing analytics

As the final step to this activity, instructors should summarize the key takeaways from the activity (Slide 19). Specifically, we recommend emphasizing how marketing analytics is part of daily life and integral in nearly every decision made by consumers. As marketers and future business leaders, students will be hard-pressed to ignore analytics.

At this point, the students will likely express an increased excitement and interest in marketing analytics, making it an ideal time to promote the university offerings. The final slide (Slide 20) is designed to be customized with offerings from the instructor’s university and/or department. For example, does the corresponding university have options for students to explore marketing analytics further? (e.g., specific classes, programs, majors, or student organizations related to analytics). The post-activity is an optimal time to promote interest in those areas, if applicable.

Methodology and design

This activity has been used to introduce the topic of marketing analytics across seven sections of four different undergraduate marketing courses offered at three separate public institutions. The activity and discussion were facilitated exactly as prescribed previously, and identical materials (the slides from the Appendix) were used. Specifically, this project was incorporated into one section of Foundations of Integrated Marketing Communications, two sections of Creative Strategy and Design, three sections of Personal Selling, and one section of Advanced Selling Techniques. Data were collected in these classes during the same 15-week semester, and the exact instructions, slide deck, and discussion questions were provided for all students. All classes were offered in a traditional face-to-face format, and 45–50 min of class time was spent on this activity in each section. Across all seven sections, 156 students completed the project.

Survey respondents were undergraduate students enrolled in the courses mentioned above who were aware that this exercise was being used as a research study, and they voluntarily consented to participate in the study and complete the related surveys. Specifically, these students shared input about the class activity through an online pre-assessment survey and an online post-assessment survey which were adapted versions of the surveys used in pedagogical work by Dingus and Milovic (2019) and Dingus and Black (2021). The post-test survey repeated pre-test items and included additional questions about the student's experience with the project. Qualtrics was used to administer both online surveys. Of the 156 students completing the in-class exercise, 153 completed both the accompanying pre-assessment and the post-activity assessment, resulting in a 98.08% response rate. The pre- and post-test results were matched, and a within-subjects design was employed for t-tests to assess changes between the pre- and post-test.

Class sizes ranged from 10 to 29 students. Of the 153 students, 15.00% of the respondents were first-year students, 17.60% were sophomores, 21.60% were juniors, and 45.80% were seniors; all were traditional-aged undergraduate students. When asked about gender identity, of the 153 students, 51.60% identified as a woman, 47.10% as a man, 0.07% as non-binary/non-conforming, and 0.07% preferred not to respond. When asked about their academic interests, 73.20% were marketing majors, 11.80% were marketing minors, and 15.00% were neither.

Only 34.60% reported having marketing analytics as a topic introduced in a previous course, and only 7.20% of the 153 students reported current enrollment in their university's marketing analytics offering (e.g., major, minor, certificate). As such, the 7.20% of the original sample who were enrolled in an analytics program were removed from further analysis to avoid a potential bias in favor of analytics and to get a more accurate view of the impact of this exercise for students in marketing courses who are not formally studying analytics.

Administered in advance of this lesson, the online pre-test asked students to define the term “marketing analytics” in their own words and then to rate their confidence that the definition was correct on a sliding scale from zero to 100 (0 = not at all confident and 100 = positively confident). Students were also asked to identify their awareness, interest in, and excitement for marketing analytics using a slider scale from zero to 100 (0 = not at all aware/interested/excited and 100 = extremely aware/interested/excited). Additionally, respondents identified their agreement with the statement “I find analytics to be intimidating” on a Likert-style scale, where 1 = strongly disagree and 7 = strongly agree. Finally, students answered various questions about any experience they have had with analytics or marketing analytics and identified any fears, reservations, or hesitations related to analytics through an open-response question.

Following the pre-test, about 45 min of one in-person class meeting was spent introducing the idea of marketing analytics through the four steps mentioned previously. Following the class discussion, students were asked to complete the online post-test to evaluate the activity's effectiveness as a pedagogical tool. In the post-test, student respondents answered the questions repeated from the pre-test. Students also identified how effective the lesson was in impacting five factors (excitement for analytics in general, excitement for marketing analytics, learning about marketing analytics, ability to recognize marketing analytics in everyday life, and learning the relevance of marketing analytics for a future career), using a 7-point Likert-style scale, where 1 = not at all effective and 7 = extremely effective. Additionally, students used a Likert-style scale of agreement (where 1 = strongly disagree and 7 = strongly agree) to indicate their agreement with these four statements as a result of the lesson: I have a better understanding of what may be involved in marketing analytics, I learned the benefits of marketing analytics, Marketing analytics will be relevant in my future career, and I would recommend this lesson for future semesters. Finally, students were asked five open-ended questions about whether any fears or hesitations related to analytics have been lessened as a result of this lesson, what their favorite thing was about this lesson, what was something they would change about this lesson, what was one eye-opening fact they learned, and what else they would like to learn about related to analytics.

All data analysis was completed using SPSS, Version 29. All pre- and post-test responses were matched and, as mentioned earlier, the 7.2% of respondents who were enrolled in an analytics program offering at their institution were removed from further analysis. Then, means were calculated for each class section and for the overall group. Paired-sample t tests were performed to identify significant differences in pre- and post-test results for students’ self-assessed measures of confidence in their definition of marketing analytics as well as their awareness of, interest in, and excitement for marketing analytics. An additional t test compared levels of intimidation related to the topic of marketing analytics during the pre- and post-tests.

The student perceptions of learning reported above, paired with their increased enthusiasm for marketing analytics, demonstrate that students did benefit from this exercise. However, students’ enjoyment and perceived benefits should never be regarded the same as objectively measuring actual learning in educational research (Bacon 2016). To assess actual learning, each definition of marketing analytics written by respondents in the pre-test and the post-test was assessed by an outside scorer using a rubric scale from one to five. This grader is not related to the current project but has previously served as a teaching assistant for an analytics course and is well qualified to score all respondents’ pre- and post-test definitions of marketing analytics using a rubric scale from one to five. The final t test assessed objective evidence of learning by comparing the average ratings of students’ marketing analytics definitions in the pre-test and the post-test.

Results and discussion

Several interesting conclusions can be made from the data attained through pre- and post-assessments of the students who participated in this single-class activity. In particular, there is evidence that students' confidence in the idea of marketing analytics increased. Growing up as a generation intimidated by numbers and words like “analytics” and “quantitative,” this exercise was effective in reducing students' hesitations even to approach these concepts. Thus, the main goal of this project was accomplished, as students are now more intrigued by the idea of studying marketing analytics, and, even without knowing the nitty–gritty details, they understand analytics will influence their careers.

Students in the seven course sections experienced a significant increase from the pre-test (M = 46.53, SD = 28.39) to the post-test (71.27, SD = 21.80) in their confidence in defining “marketing analytics,” t(135) = 12.004, p < 0.001. They also experienced a significant increase in their Awareness of, Interest in, and Excitement for Marketing Analytics (see Table 1 for full statistical evidence). Beyond defining the concept with more confidence, they also experienced a significant decrease from the pre-test (M = 4.38, SD = 1.49) to the post-test (M = 3.56, SD = 1.41) in how intimidating they perceive analytics to be, as evidenced both by the quantitative evidence where t(137) = -6.560, p < 0.001, as well as the qualitative comments. Evidence of such is provided in Tables 1 and 2.

Table 1 Comparison of self-assessed pre- and post-test measures about marketing analytics
Full size table
Table 2 Perceived intimidation of analytics, before and after
Full size table

Measures from the post-test indicate that this class activity was an eye-opening activity that was influential in students' perceptions of analytics and interest in learning more in this area. Specific results are shared by class and overall in Table 3. Students report that this activity increased their excitement for analytics in general as well as marketing analytics (overall mean = 5.65 and 5.44, respectively). In addition to increasing their perception of learning about marketing analytics (mean = 5.78), students also report an increased ability to recognize marketing analytics in their everyday lives (mean = 5.97) and the relevance of marketing analytics for their future careers (mean = 6.05). Sharing specific feedback about the activity's learnings, students overall felt they had a better understanding of what marketing analytics entails (mean = 6.06), the benefits of marketing analytics (mean = 6.12), and its relevance for their future careers (mean = 6.21). Interest in taking a marketing analytics course was also strong (mean = 5.51), in addition to a strong recommendation from students for their instructors to use this activity again in future semesters (mean = 6.18).

Table 3 Mean responses to survey administered at conclusion of project
Full size table

The accuracy of definitions students wrote for marketing analytics before and after the class activity were compared to assess whether students experienced an increase in actual learning of what marketing analytics is. As detailed in Table 4, students’ knowledge of the definition of marketing analytics increased as a result of this exercise and they were able to write better definitions in their own words. While the self-perceptions of learning and the increased attitudinal variables regarding marketing analytics make this exercise seem strong, evidence of actual learning bolsters the impact of this exercise.

Table 4 Objective evidence of learning showing before and after ratings of definitions of marketing analytics
Full size table

Students were enthusiastic about this class session, and in addition to the strong empirical support, qualitative feedback and class discussions were richly supportive of this activity. They expressed an awareness of how to recognize undercover analytics, differentiate between analytics and marketing analytics, and view this from both a consumer's and an organizational perspective. These outcomes indicate a strong foundation from which recruitment for marketing analytics courses and programs can occur. Qualitative feedback shared in the post-test was overwhelmingly positive. The following students' comments are reported verbatim, summarizing common sentiments in three core areas:

In response to “Have any of your fears or hesitations related to analytics been resolved, or lessened, as a result of this lesson?”

  • They have been slightly lessened. I am less intimidated by applications of analytics now that I've seen more relevant examples of it in action.

  • Yes. I think I've learned that it is more straightforward and less complicated than previously thought…feels less daunting because I also thought of it as just numbers and mathematical, but there's more to it.

  • Yes. Prior to this lesson I had no clue what marketing analytics were. After the lesson I recognize the importance of marketing analytics and how they impact my daily life.

  • Yes, the understanding of marketing analytics is calming. It's more interesting than anticipated.

  • Yes, I knew almost nothing about it before but now I feel confident explaining it to someone else!

Favorite aspects of this lesson and key takeaways

  • Analytics are everywhere, and even if you don't think so, you are using analytics in your everyday life.

  • Seeing how the profile for the same Netflix shows change per person.

  • The differences you saw from you and your group members.

  • I liked learning about what it actually means to use and how it works with marketing analytics.

  • How much analytics will impact my future career

Recommendations for future iterations of the lesson

  • Send it to early-level marketing classes, analytics sounds cool, but it's too late for me to make any changes to learn analytics.

  • Teach more about graphs.

  • Go deeper into it, more in-depth examples.

Throughout qualitative responses, quite a few students allude to their year in school and how they would like to study more about this but are unable as they graduate. But the enthusiasm for this topic and the realization is evident that marketing analytics' real-world impact is far more interesting than its mathematical modeling is scary. In fact, the last question of the post-test, “Take it a step further… What else would you like to learn related to analytics?” Overwhelmingly, the students answered this optional question with thought-provoking responses. While these are not direct outcomes of the exercise, the sincerity with which this question was responded indicates the students' genuine interest in the topic and evidence that this class activity stimulated deep thoughts about the use of marketing analytics. Responses varied greatly for this question, and we share an assortment below as they may be helpful for faculty considering ways to attract and recruit students to their programs based on actual student input, as well as build future lessons in marketing analytics:

  • Look at more examples and understand more of the behind the scenes of implementing analytics from a business standpoint.

  • The cost of maintaining marketing analytics.

  • Career in analytics.

  • How does data pinpoint optimal information?

  • How to track actual decisions being made.

  • I would like to learn how to use specific platforms and measurement strategies I may use in my future career.

  • I would love to see an actual example of what a company looks at in terms of a data application or something along those lines.

  • How it works with YouTube.

  • How will it apply in product management.

  • I would like to see the more technical side.

  • How to do sports analytics.

  • I would like to learn how my data impacts these marketing analytics and what companies do to ensure that my experience is personalized and tailored to my interests.

  • How it would relate in detail to my future job.

  • How we can implicate improvements for a business based on data.

  • I'm interested in how they calculate the results behind the scenes.

  • How they find this sort of information and if it can sometimes be illegal or unethical.

  • How to gather my data to use it in my life.

While this class activity specifically does not claim to teach analytics, it invites students to consider a new topic and explore how they have seen evidence of marketing analytics in their lives and some examples of how companies utilize data the way they do. In fact, for an instructor pressed for time who may want to quickly generate interest in marketing analytics, a shortened version of this activity using just the smartwatch and Netflix examples will generate a lively conversation. These first two examples generated the most discussion and engagement across all classes in the sample. Considering consumer and organizational implications encourages them to stretch their minds and apply their coursework, using strategic thinking skills that do not directly require math but analyzing actions that companies have made based on many algorithms and computations. For students struggling to think of business applications, instructors can guide students to focus on personal implications and then challenge students to flip perspectives on how the business would use this personal data to benefit the organization.

Students enjoyed this exercise, especially as they saw differences between themselves and their classmates using such seemingly simple examples that were, in fact, quite complex…thanks to the incredible power of marketing analytics! We encourage faculty with minimal expertise in analytics to consider adding class discussions and activities such as this one into their marketing courses as a way of whetting students' appetite and broadening students' view of what lies ahead in the marketing world and as a recruitment tool for marketing analytics programs.

Pedagogical implications and broader applications

Within marketing, using experiential activities to enhance a student's engagement toward knowledge and skills that are in demand by today's employers has been an important consideration in the goals of effective marketing pedagogy (Civi & Persinger 2011; Lemken & Siguaw 2021). For educators, having easily accessible options to introduce topics to students with examples that provide engaging learning activities can help assist in educating marketing students and, in this case, drive awareness and interest in new topics. Considering the pedagogical implications for such activities, cultivating interest is crucial to the success of student learning because, ultimately, the more interested a student is in a topic, the more progress they will make in their learning due to heightened attention concentration (Krapp et al. 1999). Further, the relationship between student interest and student engagement and motivation has been shown to drive student success (Mazer 2013). For this current study, students increased their interest and awareness of marketing analytics in each of the seven sections of the course. Additionally, their actual knowledge of marketing analytics increased, as reflected by their increased ability to define the term. This answers Bacon’s (2016) call for actual assessment of student learning rather than relying on students’ perceptions of their learning.

Moreover, considering that university programs are vying for students (Hemsley-Brown and Oplatka, 2006) coupled with industry requiring students to have analytical acumen (Kurtzke and Setkute, 2021), it is important to have pedagogical activities that are easy for faculty to use to create awareness for marketing students who may not have considered the broad application of analytics in their careers and beyond. The broader importance of integrating more complex topics into non-specialist curricula and the positive impact of hands-on, interactive teaching methods in marketing are worth considering. These approaches enhance student engagement and understanding, especially in fields perceived as challenging or outside a student's primary area of study (Spanjaard et al. 2018). Given this, the underlying goal of this paper's outlined activity is not to teach marketing analytics specifically but to enhance student interest and awareness of marketing analytics, using the broader application of marketing analytics as shown in the student's personal experience with organizations or tools that use marketing analytics in ways that the student can imagine and relate to in their daily lives. Through increased interest and awareness of marketing analytics, we suggest that students may be more inclined to consider a marketing analytics-focused course or degree in the future.

Interestingly, classroom activities such as ours that help to increase student awareness and interest will ultimately impact educational decisions and potential action for course registrations or students considering analytics programs. Though this is beyond the scope of this paper, the more students that become interested and aware of important career-ready topics and options, the greater the results to have students in courses, major and minor programs. Though our paper deals only with awareness and interest, this is a potentially broader application to the pedagogical implications of this activity.

Limitations and future research

No study is without limitations. In the present study, one such limitation is that this in-class exercise introducing marketing analytics was included and assessed in four different intermediate-level marketing courses. One limitation to the generalizability of this paper could be the varied awareness, knowledge, and experience levels with both marketing and marketing analytics that the responding students had as a result of varied class standing and/or marketing courses completed. Ideally, this activity would be included in introductory-level marketing courses, so students become intrigued by this program offering even earlier in their collegiate career and have more flexibility and time to allow them to enroll in analytics courses/programs. However, we believe the results experienced in the intermediate-level courses might even be amplified at the introductory level, so this limitation is also an avenue for future research.

Future research projects can build on this paper’s proposed introduction to marketing analytics exercise by providing more in-depth examples and integrating interesting datasets. Of course, this will require additional time. However, it would be interesting to see what types of activities might be most influential to increase the number of students committing to analytics program offerings. An additional limitation of this study is that the only objective measure of learning is through the assessment of definitions where respondents wrote, in their own words, a definition of marketing analytics as part of the pre-and post-tests. This single measure of actual learning answers the call from Bacon (2016) to avoid making large claims about learning based on students’ self-perceptions of learning; however, it would be ideal to have additional objective measures of the impact of this class exercise.

When administering this activity in their classes, the authors were impressed by the quality and depth of discussion among their students. Further, one unintended outcome of this exercise is the student discussing how analytics would be used in their day-to-day careers (Slide 18). Anecdotally, they noticed that, as career opportunities were discussed throughout the remainder of the semester, the relevance of analytics was frequently a topic of conversation. Future research could focus on how analytics is relevant to daily careers, regardless of the job description (e.g., setting goals, evaluation, etc.). Marketing pedagogy would benefit from a review of common entry-level marketing roles that identify the different aspects of marketing analytics that impact that role and what analytical skills are mastered by the top performers in the respective roles. Additionally, this information could be organized into a systematic structure where a hierarchy is created based on common career progressions for marketers, again aligned with the importance of data analytics. We were surprised to see that students who claimed to not be very interested personally in analytics were actually curious about the use of analytics in non-analytic roles. Structured the right way, such a paper could benefit students exploring marketing careers, faculty who lack extensive skills in analytics but teach in marketing programs, and hiring firms alike.

Conclusion

This paper presents a highly engaging class activity that can be integrated as a plug-and-play style, using the slides included in the Appendix, into any marketing course to spark students' interest in marketing analytics. Using this activity as a recruiting tool allows faculty without specific analytics expertise to engage in a marketing-focused dialogue that stimulates a fun discussion while introducing students to the importance of analytics in their everyday lives and their future careers. In essence, this paper aims to create a more friendly entry (for faculty and students alike) into the sometimes-intimidating world of marketing analytics.

Examples within the activity can easily be changed or adapted, as can the discussion questions, though the combination described in this paper was able to be completed in one 45–50 min setting and these examples were reported as interesting to a wide variety of undergraduate students in four different marketing courses at three different institutions. The average recommendation to include this lesson in future semesters was notable at 6.18 on a 7-point scale. Additionally, for faculty wanting to go more in-depth, this activity could be used as a fun activity on the first day of a marketing analytics—or general analytics—course to garner a deeper interest. At the same time, the learning environment would be enhanced for the rest of the semester as students would get to know each other as they collaborate in small groups and learn about the scope of the class.

As marketing faculty prepare students for the modern business world, it is imperative that analytics are emphasized and the stigma that marketing does not involve math is debunked. The first step in this may be to reduce the perception that analytics is intimidating. The qualitative and quantitative evidence supports that students found this simple activity exciting and effectively shifted their perceptions of analytics and their interest in pursuing further study of marketing analytics.