Teaching marketing analytics: a pricing case study for quantitative and substantive marketing skills

Skiera, Bernd; Jürgensmeier, Lukas Article — Published Version Teaching marketing analytics: a pricing case study for quantitative and substantive marketing skills Journal of Marketing Analytics Provided in Cooperation with: Springer Nature Suggested Citation: Skiera, Bernd; Jürgensmeier, Lukas (2024) : Teaching marketing analytics: a pricing case study for quantitative and substantive marketing skills, Journal of Marketing Analytics, ISSN 2050-3326, Palgrave Macmillan, London, Vol. 12, Iss. 2, pp. 209-226, https://doi.org/10.1057/s41270-024-00313-2 This Version is available at: https://hdl.handle.net/10419/316666 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ Vol.:(0123456789) Journal of Marketing Analytics (2024) 12:209–226 https://doi.org/10.1057/s41270-024-00313-2 CASE STUDY Teaching marketing analytics: apricing case study forquantitative andsubstantive marketing skills BerndSkiera1 · LukasJürgensmeier1 Revised: 27 March 2024 / Accepted: 28 March 2024 / Published online: 27 May 2024 © The Author(s) 2024 Abstract This article describes a data-driven case study for teaching and assessing students’ skills in marketing analytics, specifically in pricing. This case study combines teaching econometrics to analyze data and substantive marketing to derive managerial insights. The econometric challenge requires students to set up and implement a regression analysis to derive the demand function, detect multicollinearity, and select appropriate data visualizations. The substantive challenge requires deriving optimal pricing decisions and understanding how the parameters of the demand function impact optimal prices and the associated profit. We test the case study in a marketing analytics exam and discuss the performance of 134 students. Beyond assessing student performance in an exam, the case study facilitates teaching through in-class group work or assignments. Free of charge, under a liberal CC BY license, we encourage other educators to use the case study in their teaching. We provide the necessary data and a sample solution using the statistical programming language R. Keywords Marketing analytics· Pricing· Teaching· Education· Case study· Data science Introduction Marketing analytics, or data-driven marketing, enables managers to make decisions based on insights from analyzing data rather than relying upon their intuition or past experiences (Iacobucci etal. 2019). New technologies and increasing data availability in marketing (Petrescu and Krishen 2018; Van Auken 2015) enable the implementation of an evidence-based approach to marketing decisionmaking that promises better and less risky decisions (Wedel and Kannan 2016). For example, data-driven marketing uses data to estimate demand functions depending on prices and advertising budgets and derives optimal prices through profit maximization. Successful marketing analytics teaching requires that future decision-makers, i.e., today’s marketing students, learn (i) how to analyze data and (ii) how to turn the insights from this analysis into marketing decisions. Hence, such decisions require skills in econometrics and marketing. However, many multivariate data analysis textbooks primarily cover theoretical aspects of data analysis (e.g., Stock and Watson 2019; Wooldridge 2013), putting less emphasis on turning the econometric insights into (optimal) substantive decisions. In contrast, textbooks on substantive marketing topics focus on marketing management and less on data analysis (e.g., Kotler etal. 2019). In the vein of more specialized marketing analytics textbooks (e.g., Mizik and Hanssens 2018), this article aims to bridge this gap between econometrics and substantive marketing knowledge by suggesting a marketing analytics case study that connects both skills. This article aims to introduce and disseminate a marketing analytics case study that covers a fundamental task of marketing—deriving optimal prices—and sets this decision into context with several other typical marketing questions: measurement of advertising effects, marketing mix allocation, and performance-based compensation of sales representatives. By analyzing a simulated data set including sales, prices, and other marketing mix variables, students learn the theory and practical application of standard * Bernd Skiera [email protected] Lukas Jürgensmeier juerg[email protected]t.de 1 Department ofMarketing, Faculty ofEconomics andBusiness, Goethe University Frankfurt, Theodor-W.-Adorno-Platz 4, 60629Frankfurt, Germany 210 B.Skiera, L.Jürgensmeier econometric methods, such as the multivariate linear regression, and use its results to determine optimal prices, discuss advertising allocations to different marketing channels, and estimate the impact of those marketing decisions on sales and profit. We assess the exercise’s efficacy as a 60-min final exam by analyzing the performance of 134 students in an undergraduate marketing analytics class. The results show that the exercise results in approximately normally distributed student performance across all sub-exercises with a mean close to half of the achievable points. Although the exercise is very challenging to solve in 60min, the almost perfect solutions of a few students show that its difficulty is well-calibrated. On an individual sub-exercise level, our empirical analysis shows that the sub-exercises vary in difficulty, as reflected by the distribution of student performance. These results indicate which sub-exercises confront students with the biggest challenge and suggest where educators could focus their teaching efforts. Our article first introduces the didactic setting, including the case study’s pedagogical vision, learning goals, structure, and theoretical background. After introducing the context, we provide the full case study and detailed solutions with a rubric that enables educators to use the exercise as a case study, a (graded) assignment, or an exam. The article proceeds with a discussion of the case study’s implementation. Before concluding, we assess and discuss the case study’s efficacy by presenting the graded results from 134 exam submissions. We provide data, simulation codes, exercise templates, and solutions in the accompanying repository, https:// github. com/ lukasjue/ marke tinganaly ticsexerc ise. Background ofthecase study Description ofpedagogical vision We pursue the following two pedagogical aims with this case study: (a) equip students and future marketing managers with the quantitative intuition and econometric toolkit to analyze marketing data, and (b) enable students to use those quantitative skills to derive better marketing decisions. Fundamentally, we want students to understand in which marketing setting which econometric method fits best. For example, this case study does not ask students to “estimate a linear regression and interpret its parameters” but instead asks them to derive optimal prices based on the available data. Hence, students need to identify the appropriate method themselves. Through this didactic approach, we aim to prepare the students for their future work by simulating a realistic working environment where part of the challenge is identifying the path toward a solution. Description oflearning goals Table1 describes the learning goals of our case study, which correspond to the two pedagogical aims stated above. In the first column, we list the marketing learning goals, which equip students with substantive marketing skills. The second column describes the analytical or econometric techniques students need to fulfill the corresponding marketing learning goal. We structure the case study to achieve those stated learning goals through the following seven parts: Table 1 Link between the Case Study’s Learning Goals in Marketing and Analytics Marketing Analytics (Econometrics) Understanding typical marketing data (here: sales and price data) Using a programming language to read, visualize, and model data Estimating and interpreting parameters of a demand function from sales and price data Estimating and interpreting results of the (multivariate) linear regression Understanding the relationship between a demand and a profit function Using the parameter estimates from the linear regression to describe the demand function and set up the respective profit function Setting the optimal price based on the demand function estimated from sales and price data Taking the first derivative of the profit function to derive its maximum for identifying the optimal price Understanding the impact of marketing mix variables (e.g., advertising) on the demand function and the corresponding optimal price In linear regression, understanding how changing the value of an independent variable impacts the dependent variable Interpreting elasticities of demand and assessing their plausibility Understanding and computing the (price) elasticity of demand Identifying and avoiding the impact of multicollinearity in marketing variables on managerial decisions Understanding the econometric problem and consequences of multicollinearity 211 Teaching marketing analytics: apricing case study forquantitative andsubstantive marketing… 1. Starting with simple descriptive statistics and visualizations to understand the data and potential problems, 2. continuing with specifying the correct demand function and modeling it through an appropriate multivariate linear regression, 3. using the regression results to compute price and advertising budget elasticities of demand, 4. using the estimated demand function to derive optimal prices, 5. estimating the impact of changes in the advertising budget allocation on optimal prices, 6. using the econometric results to evaluate proposed marketing budget allocations, and 7. assessing which performance characteristics of sales representatives could justify different compensations for individual salespersons based on their expected profit (derived from the demand function). This ordering follows a typical analytics workflow—from descriptively analyzing the data to modeling and deciding based on those results. Considerations fromtheliterature forsuccessful case study development Because our case study should be suitable for both exams and teaching, we consult the literature to derive characteristics of a successful exam exercise and teaching case study, respectively. Characteristics ofasuccessful exam exercise One of the prominent theories in psychometrics and student testing is the Item Response Theory. This framework aids in constructing exams that accurately measure the latent student ability. To achieve this aim, Item Response Theory models students’ probability of correctly solving a sub-exercise as a function of the exercise’s difficulty, among other parameters (e.g., DeMars 2010). Constructing an exam with sub-exercises of varying difficulty enables the educator to differentiate between different levels of student ability. Hence, in line with the requirements of Item Response Theory, our exam aims to feature sub-exercises of varying difficulty such that they can discriminate well between different levels of student performance. Although assessing the exercise’s capability of measuring the latent ability remains beyond this manuscript’s scope, we posit that the full exam should be sufficiently difficult to distinguish between degrees of student performance granularly but not too difficult such that the exercise lacks differential power. Hence, the results of the exam should. (a) yield an approximately normal distribution of achieved points, thereby reflecting the assumed normal distribution of students’ abilities, (b) with students’ performances covering the entire range of possible outcomes. While these two empirical characteristics refer to the full set of sub-exercises, the exam should feature sub-exercises from a broad range of difficulties. Hence, the exam results should feature. (c) sub-exercises with a comparatively high degree of correct submissions (i.e., relatively easy sub-exercises), and (d) exercises with a comparatively low degree of correct submissions (i.e., relatively challenging sub-exercises). Eventually, we test whether those requirements hold in an exam setting in our empirical section. There, we present and discuss the student performance distribution to our exercise in an exam setting. Additionally, we designed the exercise to feature high construct validity (e.g., Peter 1981). Transferring the role of construct validity in marketing research to an educational setting, a successful marketing analytics exercise should mirror the substantive marketing and analytics requirements demanded in students’ future careers. Hence, the exercise should reflect a typical marketing analytics task commonly faced in practice and academia—thereby correctly assessing students’ abilities in this field. We ensure this requirement through the topic choice (pricing) and the required analytics techniques (data visualization, regressions, and optimization). Characteristics ofasuccessful teaching case study While the previous theoretical considerations apply primarily to the exam setting, we also aim to provide a useful case study for teaching purposes. Hence, the following theoretical considerations inspired the development of the case study. First, and congruent to exhibiting a high construct validity, a successful case study for teaching purposes should exhibit constructive alignment (Biggs 1996). Hence, the case study should mirror the course’s intended learning outcomes. We designed the case study to feature high constructive alignment with a typical marketing analytics course, as reflected by the marketing and analytics learning goals in Table1. Second, besides the case study mirroring the academic course’s learning goals, it should also mirror what students expect in the real world. Hence, the case study should be an “authentic assessment,” meaning that students can expect 212 B.Skiera, L.Jürgensmeier a similar case once they graduate and practice marketing analytics (Montano etal., forthcoming). Third, the case study should be sufficiently difficult to provide ample learning opportunities. Hence, some of the case study’s sub-exercises should be relatively challenging even for exceptional students, enabling discussions in which a teacher explains the underlying non-trivial approach to solve the case study correctly. After introducing the exercise’s pedagogical vision, learning goals, and theoretical background, we now introduce the case study and provide detailed solutions, including a grading rubric. Description andsolution ofthecase study Your B2B firm sells an energy-efficient heater fan and has divided the country into 100 comparable sales territories, each staffed exclusively by a female sales representative. The sales representatives differ according to their professional experience (measured by the number of years they have been in sales) and whether they have an engineering degree. The management recently stated that 50% of the sales representatives have at least five years of professional experience in sales, and at least one-third of the sales representatives also have an engineering degree. The firm’s management randomly varied the prices across the sales territories. In addition, the management commissioned an advertising agency to support the sales representatives through marketing and provided a dedicated budget. The agency should use this marketing budget for telephone marketing and social media marketing, with an approximately equal allocation towards the two channels (i.e., 50% phone calls and 50% social media marketing). So far, the firm’s management has randomly varied the agency’s budget across the sales territories. The variable cost per unit of a heater fan is $200. In the file data_sales.csv, you receive information that helps you answer the questions. The data set includes 100 rows, each describing one of the 100 sales territories with the following variables: Table: Description of the data set data_sales.csv Variable Description quantity Number of sold heater fans in the respective sales territory price Price of the heater fan sold in the respective sales territory experience Number of years the sales representative has been working in sales Variable Description engineer Binary variable indicating whether the sales representative has an engineering degree (= 1) or not (= 0) gender Gender of the sales representative, female (= 1) or other (= 0) location Number of letters of the sales representative’s birthplace (e.g., 7 if the birthplace is “Chicago”) budget_agency Budget used by the advertising agency, which is either spent for phone or social media marketing, in $ budget_phone Budget used for telephone marketing, in $ budget_social_media Budget used for social media marketing, in $ (1):Exploratory data analysis For each part of this first exercise, please answer the question by analyzing the data set through one visualization and one non-visual descriptive analysis. We do not require custom labels for the visualizations. (a) Does the firm only employ female sales representatives? (2 points). Solution df <- read.csv("data_sales.csv") summary(df$gender) ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 1 1 1 1 1 1 barplot(table(df$gender), main ="Barplot of Gender" b ="Number of Sales Territories") Yes, because the summary statistics and the barplot show that the data set only includes gender = 1, corresponding to females. (b) Do the prices differ across sales territories? (2 points). 213 Teaching marketing analytics: apricing case study forquantitative andsubstantive marketing… Solution summary(df$price) ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 209.4 271.1 301.4 300.4 329.6 369.4 hist(df$price, main ="Histogram of Price", xlab ="Price (in $)", ylab = "Number of Sales Territories") Yes, prices differ across sales territories. For example, the summary statistics exhibit different values for the minimum ($209.4) and maximum ($369.4). Additionally, the histogram shows that prices vary across sales territories. (c) Is the management’s statement regarding the professional experience of their sales representatives correct? (2 points). Solution Statement to check: Median experience is 5 years median(df$experience ) ## [1] 4 boxplot(df$experience, main = "Boxplot of Experience", ylab = "Years") No, because the summary statistics and the boxplot show that the median of experience is 4. If management’s statement (“50% of the sales representatives have at least five years of professional experience in sales”) were true, then the median of experience should be 5. An alternative and equally correct solution is to count the observations that have less than 5years of experience: table(df$experience) ## ## 0 1 2 3 4 5 6 7 8 9 10 ## 2 9 12 20 19 20 7 6 3 1 1 sum(df$experience >= 5) / nrow(df) ## [1] 0.18 barplot(table(df$experience), main ="Barplot of Experience", xlab ="Year s", ylab ="Number of Sales Territories") (d) Is the management’s statement regarding the background of their sales representatives correct? (2 points). Solution Statement to check: At least one-third of sales representatives hold an engineering degree. table(df$engineer) ## ## No Yes ## 72 28 barplot(table(df$engineer), main ="Barplot of Engineering", ylab ="Numbe r of Sales Territories") Management’s statement is incorrect because we only observe that 28% of sales representatives hold an engineering degree, not at least 33%. (e) Does the advertising agency’s budget differ across the sales territories? (2 points). 28 72 +28 =0.28 < 1 3 ≈ 33% . 214 B.Skiera, L.Jürgensmeier Solution summary(df$budget_agency) ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 329 838 1030 1028 1199 2056 hist(df$budget_agency, main = "Histogram of Budget Agency", xlab = "Budget in $", ylab = "Number of Sales Territories") Yes, the budgets differ. The summary statistics outline that the minimum budget across all sales territories is 329, and the maximum is 2,056. Additionally, the histogram shows that the budget varies strongly across sales territories. (f) Did the advertising agency achieve the aim of approximately equally allocating the budget to the two channels (telephone and social media marketing)? (2 points). Solution cor(df$budget_phone, df$budget_social_media) ## [1] 0.9910698 plot(df$budget_social_media, df$budget_phone, main ="Scatterplot of Budge ts" , xlab ="Social Media ($)", ylab ="Telephone ($)") Yes, all sales territories received almost equal telephone and social media marketing budgets. This insight is easiest to observe in the scatterplot. The visualization displays the almost perfect linear relationship and shows that the social media budget approximately equals the telephone marketing budget. The correlation coefficient is almost 1, and both budgets’ average values are also almost equal. Note: Displaying the individual distributions of the two variables, e.g., via a histogram, is insufficient because a similar distribution of two variables is a necessary but not a sufficient condition for similar budgets in all sales territories. (g) Were the sales representatives born in at least 8 different cities? (2 points). barplot(table(df$location), main = "Barplot of Location", xlab = "Number o f Characters in City Name" , ylab = "Number of Sales Territories") Solution nrow(table(df$location)) ## [1] 11 Yes, because the data set includes 11 different values of location, which implies that the sales representatives were born in at least 11 cities. Step Task Max. Points Achieved Points 1-a Correct visualization (1 point), correct non-visual descriptive statistic (1 point) 2 1-b see above 2 1-c see above 2 1-d see above 2 1-e see above 2 1-f see above 2 1-g see above 2 215 Teaching marketing analytics: apricing case study forquantitative andsubstantive marketing… Exercise(2): Estimating ademand function Estimate a linear demand function. Justify which variables you include and which ones you ignore. Discuss the influence of price and professional experience on the quantity and how confident you are about their influence. (14 points). Solution Step 1: Identify the variables to include in the linear regression equation. Include all variables except: • gender, because it only has the value 1 and, thus, no variation. • location, because there is no plausible reason why the character length of the sales representative’s city of birth should impact sales. • budget_phone and budget_social_media, because – the two variables are almost perfectly collinear (50% share each and correlation is almost 1), and – the sum of both variables is a linear combination corresponding to the third variable budget_agency, leading to perfect multicollinearity: budget_agency = budget_ phone + budget_social_media. Step 2: Use a linear regression to estimate the linear demand function. Most Suitable Model reg_model_1 <- lm(quantity ~ price + experience + engineer + budget_agency , data = df) Acceptable Models reg_model_2 <- lm(quantity ~ price + experience + engineer + budget_phone, data = df) reg_model_3 <- lm(quantity ~ price + experience + engineer + budget_social _media, data = df) library(stargazer) stargazer(reg_model_1, reg_model_2, reg_model_3, type = "text", header = F ALSE) ## ## ==================================================================== ## Dependent variable: ## -------------------------------------- ## quantity ## (1) (2) (3) ## -------------------------------------------------------------------- ## price -2.953*** -2.951*** -2.955*** ## (0.038) (0.038) (0.038) ## ## experience 9.039*** 9.061*** 9.023*** ## (0.663) (0.659) (0.669) ## ## engineerYes 0.954 1.012 0.860 ## (2.972) (2.956) (2.990) ## ## budget_agency 0.023*** ## (0.004) ## ## budget_phone 0.046*** ## (0.009) ## ## budget_social_media 0.044*** ## (0.009) ## ## Constant 1,215.966*** 1,215.042*** 1,217.175*** ## (13.124) (13.090) (13.161) ## ## -------------------------------------------------------------------- ## Observations 100 100 100 ## R2 0.985 0.986 0.985 ## Adjusted R2 0.985 0.985 0.985 ## Residual Std. Error (df = 95) 12.868 12.803 12.948 ## F Statistic (df = 4; 95) 1,609.366*** 1,626.051*** 1,589.121*** ## ==================================================================== ## Note: *p<0.1; **p<0.05; ***p<0.01 Incorrect Models # Including two out of three variables `budget_agency`, `budget_phone`, an d `budget_social_media` reg_model_4 <- lm(quantity ~ price + experience + engineer + budget_phone + budget_social_media, data = df) # Including all three multicollinear independent variables `budget_agency ` , `budget_phone`, and `budget_social_media ` reg_model_5 <- lm(quantity ~ price + experience + engineer + budget_agency + budget_phone + budget_social_media, data = df) stargazer(reg_model_4, reg_model_5, type ="text", header = FALSE, column. labels = c("(4)", "(5)"), model.numbers = FALSE) ## ## ========================================================= = ## Dependent variable: ## --------------------------- - ## quantity ## (4) (5) ## --------------------------------------------------------- - ## price -2.948*** -2.948*** ## (0.038) (0.038) ## ## experience 9.177*** 9.177*** ## (0.665) (0.665) ## ## engineerYes 0.943 0.943 ## (2.950) (2.950) ## ## budget_agency -0.076 ## (0.064) ## ## budget_phone 0.120* 0.196 ## (0.063) (0.127) ## ## budget_social_media -0.076 ## (0.064) ## ## Constant 1,214.119*** 1,214.119*** ## (13.085) (13.085) ## ## --------------------------------------------------------- - ## Observations 100 100 ## R2 0.986 0.986 ## Adjusted R2 0.985 0.985 ## Residual Std. Error (df = 94) 12.775 12.775 ## F Statistic (df = 5; 94) 1,306.717*** 1,306.717*** ## ========================================================= = ## Note: *p<0.1; **p<0.05; ***p<0.01 216 B.Skiera, L.Jürgensmeier Conceptual notes concerning model selection: When deciding on the appropriate model for this exercise, students need to consider three crucial factors to ensure a correct estimation: • Correctly defining a demand function: the defined regression equation must feature quantity as the dependent variable. Additionally, price and experience must serve as independent variables to determine how they impact the quantity. • Omitted variable bias: failing to control for a variable that correlates with the dependent and other independent variable(s) leads to biased coefficient estimates. For example, omitting experience or one of the advertising budget variables could bias the coefficient estimate of price if the omitted variable(s) correlate with price and the dependent variable. However, this exercise introduces the simplifying assumption of randomly set prices, thereby ruling out endogenous prices through omitted variable bias. Randomizing prices might be challenging to implement in firms outside of narrow A/B tests. If the educator decides to omit randomization of prices as a simplifying element of the exercise, students must discuss the impact of endogeneity on the coefficient estimation and potential remedies. • Multicollinearity: Including at least two independent variables that strongly correlate with each other can bias those variables’ coefficient estimates. If this correlation is very high, estimated coefficients might exhibit opposing signs, even though the actual effects would be in the same direction. In an extreme case, including at least two perfectly collinear variables leads to perfect collinearity, which makes the regression estimation impossible due to a less-than-full rank of the design matrix of all independent variables. Model selection: Model 1 is the preferred model according to the justification from Step 1. In the incorrect model 4, we see the effects of collinearity between the two variables budget_phone and budget_social_media (as observed through their correlation, which is very close to 1) on the estimated coefficients. The coefficients in model 4 have different signs, even though the overall effect (model 1) and the effect of each variable alone in models 2 and 3 are positive. Additionally, the sum of the coefficients of the variables budget_phone and budget_social_media in model 4 is similar in magnitude to the total effect, i.e., the coefficient of the variable budget_agency in model 1. Step 3: Interpretation of Model 1. If the price increases by $1, the (expected) quantity decreases by 2.954 units. For each additional year of work experience, the (expected) quantity increases by 9.034 units. Both coefficients display p-values smaller than 1%. Hence, we are very confident for both coefficients that we can reject the null hypothesis that the coefficient is not different from zero. Formulated differently, we are confident that the estimated effect differs from zero. Step Task Max. Points Achieved Points 2–1 Correct justification of which dependent and independent variables to include in the linear regression 6 (6 Points for the correct model. 1 point deduction for each incorrectly included or excluded variable. 0.5 points deduction if the correct decision but not justified.) 2–2 Estimate regression with lm() and print summary() 2 2–3 Interpret the estimated coefficient of price correctly 3 (1.5 points for magnitude and statistical significance, each) 2–4 Interpret the estimated coefficient of experience correctly 3 (1.5 points for magnitude and statistical significance, each) Exercise(3): Compute advertising andprice elasticities ofdemand Use your model to compute the price elasticity of demand and the (advertising) budget elasticity of demand. Interpret these elasticities and assess whether your computed elasticities are consistent with economic intuition. (12 Points). Solution First, note that elasticities in a linear demand model are not constant. Hence, the elasticity depends on the independent variable’s value. Students must pick a sensible value for both variables to compute the elasticities (advertising budget and price). A good choice would be to select the average values. elasticity_budget <- coefficients(reg_model_1)["budget_agency"] * mean(df$ budget_agency) / mean(df$quantity) elasticity_budge t ## budget_agency ## 0.05974684 elasticity_price <- coefficients(reg_model_1)["price"] * mean(df$price) / mean(df$quantity ) elasticity_price ## price ## -2.286785 223 Teaching marketing analytics: apricing case study forquantitative andsubstantive marketing… Discussion oftheapplicability asanexam andimplications forother settings The significant variation in Fig.2’s distributions shows that our case study’s sub-exercises exhibit various degrees of difficulty, enabling the educator to assess students’ performance very granularly. Nevertheless, the comparatively low average number of achieved points (27.2/60 = 45.3%) shows that it is challenging for the given student population to solve the full exercise correctly in 60min. Still, the occasional almost perfect scores show that very strong students can solve the full exercise correctly within 60min. Hence, we conclude that the exercise is well-calibrated to measure varying degrees of student performance. These results from using the exercise as an exam hold implications for other settings earlier in the student’s learning journey, such as take-home or in-class case study assignments. First, students need sufficient time to work on the exercises. Assuming a lower familiarity with the subject than students writing an exam, we recommend that students in a take-home case study or in-class group work have at least 120min to solve the case study as part of a learning experience. Second, the exam results show in which sub-exercises students struggle to find the correct solution. Exercise 6 (marketing budget allocation under high multicollinearity) and Exercise 4 (determining the optimal price) were the most challenging parts of the case study. Hence, it might be sensible for educators to spend most of their time discussing those sub-exercises and the underlying concepts. While our analysis shows the promise of the case study in teaching and assessing marketing analytics, this analysis cannot comment on whether this case study is optimal such that it maximizes student learning. This article aims to make this case study freely available to educators as a first step in improving learning outcomes in marketing analytics. Hence, we encourage further research comparing our case study’s efficacy to other teaching and assessment approaches in marketing analytics. Fig. 2 Distribution of student performance by sub-exercise. Note: Due to the exam’s time constraints, the implemented exam consisted of all discussed sub-exercises except for Exercise 3 (Price Elasticity). Hence, we present the empirical results from all but this sub-exercise 224 B.Skiera, L.Jürgensmeier Summary This article described a case study to teach or assess marketing analytics skills. The presented case study combines substantive marketing skills with econometric methods, conveying how data-driven marketing can create value for firms and enable better decision-making. Through accessing our open-source repository, marketing educators can use this case study, its solution, and the accompanying data(accessible viahttps:// github. com/ lukasjue/ marke tinganaly ticsexerc ise) as a template for in-class group work, homework, or computer-based exams. Appendix: Description ofthecase study (excluding thesolution) Your B2B firm sells an energy-efficient heater fan and has divided the country into 100 comparable sales territories, each staffed exclusively by a female sales representative. The sales representatives differ according to their professional experience (measured by the number of years they have been in sales) and whether they have an engineering degree. The management recently stated that 50% of the sales representatives have at least five years of professional experience in sales, and at least one-third of the sales representatives also have an engineering degree. The firm’s management randomly varied the prices across the sales territories. In addition, the management commissioned an advertising agency to support the sales representatives through marketing and provided a dedicated budget. The agency should use this marketing budget for telephone marketing and social media marketing, with an approximately equal allocation towards the two channels (i.e., 50% phone calls and 50% social media marketing). So far, the firm’s management has randomly varied the agency’s budget across the sales territories. The variable cost per unit of a heater fan is $200. In the file data_sales.csv, you receive information that helps you answer the questions. The data set includes 100 rows, each describing one of the 100 sales territories with the following variables: Table: Description of the data set data_sales.csv Variable Description quantity Number of sold heater fans in the respective sales territory price Price of the heater fan sold in the respective sales territory Variable Description experience Number of years the sales representative has been working in sales engineer Binary variable indicating whether the sales representative has an engineering degree (= 1) or not (= 0) gender Gender of the sales representative, female (= 1) or other (= 0) location Number of letters of the sales representative’s birthplace (e.g., 7 if the birthplace is “Chicago”) budget_agency Budget used by the advertising agency, which is either spent for phone or social media marketing, in $ budget_phone Budget used for telephone marketing, in $ budget_social_media Budget used for social media marketing, in $ (1) Exploratory data analysis For each part of this first exercise, please answer the question by analyzing the data set through one visualization and one non-visual descriptive analysis. We do not require custom labels for the visualizations. (a) Does the firm only employ female sales representatives? (2 points). (b) Do the prices differ across sales territories? (2 points). (c) Is the management’s statement regarding the professional experience of their sales representatives correct? (2 points). (d) Is the management’s statement regarding the background of their sales representatives correct? (2 points). (e) Does the advertising agency’s budget differ across the sales territories? (2 points). (f) Did the advertising agency achieve the aim of approximately equally allocating the budget to the two channels (telephone and social media marketing)? (2 points). (g) Were the sales representatives born in at least 8 different cities? (2 points). (2) Estimating a demand function Estimate a linear demand function. Justify which variables you include and which ones you ignore. Discuss the influence of price and professional experience on the quantity and how confident you are about their influence. (14 points). 225 Teaching marketing analytics: apricing case study forquantitative andsubstantive marketing… (3) Compute advertising and price elasticities of demand Use your model to compute the price elasticity of demand and the (advertising) budget elasticity of demand. Interpret these elasticities and assess whether your computed elasticities are consistent with economic intuition. (12 Points). (4) Determine the optimal price A sales representative with three years of professional experience and an engineering degree operates in a sales territory. In addition, the advertising agency spends a budget of $2,000, allocating 50% to telephone and 50% to social media marketing. Determine the optimal price in this setting and explain the steps you take to arrive at your result. Assume fixed costs of zero. What is the contribution margin per unit and the firm’s profit? (20 points). (5) Impact of changes on optimal prices Answer the following two sub-questions by ticking the correct answer. (a) How does the optimal price change if a sales representative without an engineering degree works in the sales territory instead of a sales representative with an engineering degree? (2 points). The optimal price… • increases • decreases • remains about the same • no statement possible (b) How does the optimal price change if a sales representative with only one year of professional experience works in the sales territory instead of a sales representative with three years of professional experience? (2 points). The optimal price… • increases • decreases • remains about the same • no statement possible (6) Marketing budget allocation The management wants to know whether it should spend the marketing budget on telephone marketing or social media marketing. Which recommendation can you give based on your analyses? (2 points). (7) Performance and compensation of sales representatives (a) Do female sales representatives sell more than non-female sales representatives? (2 points). Answer this question based on your previous results. (b) Do business outcomes justify paying sales representatives with an engineering degree a higher salary than those without an engineering degree? (2 points). Answer this question based on your previous results. (c) Do business outcomes justify paying sales representatives with more professional experience a higher salary than those with less professional experience? (2 points). Answer this question based on your previous results. Acknowledgements We thank Timo Müller-Tribbensee for valuable comments on this case study. Funding Open Access funding enabled and organized by Projekt DEAL. Data availability The authors provide the case study’s data, simulation codes, exercise templates, and solutions in the accompanying GitHub repository: https:// github. com/ lukasjue/ marke tinganaly ticsexerc ise Declarations Conflict of interest On behalf of all authors, the corresponding author states that there is no conflict of interest. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. 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Introductory Econometrics: A Modern Approach. Boston: Cengage Learning. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Bernd Skiera is a chaired professor of electronic commerce at Goethe University Frankfurt, Germanyand a Professorial Research Fellow at Deakin University, Australia. His research interests are Marketing Analytics, Data-driven Marketing, Electronic Commerce, Online Advertising, Marketing Automation, and Consumer Privacy. Bernd Skiera’s publications appeared in Marketing Science, Journal of Marketing Research, Journal of Marketing, and Management Science. His articles have received numerous awards, including the Sheth Foundation/Journal of Marketing Award for the best Journal of Marketing article with long-term contributions and the AMA/Marketing Science Institute/H. Paul Root Award. Through an ERC Advanced Grant, he and his group currently conduct research on the economic consequences of online consumer privacy. Lukas Jürgensmeier is a Ph.D. student in Quantitative Marketing at Goethe University Frankfurt. His research focuses on the interface between Data Science and Marketing and applies econometric methods to novel data sources in the digital economy. Lukas Jürgensmeier is the winner of the 2023 ISMS Dissertation Award for his dissertation proposal “Measuring Fair Competition.” He currently serves as a supervisory board member (previously executive board member) of “TechAcademy,” an award-winning non-profit organization teaching coding to students.