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Tackle Real-World Business Decisions in the Classroom

Analyzing data, metrics, and consumer habits gets real—and really easy to understand—when students serve as actual business consultants.


Jeffrey Boichuk, PhD

Assistant Professor of Commerce, McIntire School of Commerce, University of Virginia

PhD and MS in Marketing, BA in Business Administration/Marketing

Walk down the health and beauty aisles of any grocery store, and the choices boggle the mind. Not only do countless brand-name products vie for attention, but “private label” imitators can usually be found right beside them. The question in many a consumer’s mind: Are those generic alternatives as appealing, reliable, and safe as the brand-name products we know and love?

Enter Brandefy, a smartphone app that helps consumers compare brand-name products to generic equivalents as they shop. Since its launch in 2016 by its creator Meg Pryde (a UVA business school graduate), this simple, intuitive, and free app has enjoyed rapid growth and success—so much so that Pryde wants to scale it to include the entire store. The question on her mind: As she scales up, which products should she include next?

Enter Jeffrey Boichuk, PhD, and students in his Customer Analytics course at UVA’s McIntire School of Commerce. Boichuk has made Brandefy’s business goal the center of a classroom case study, in which students use data analytics to help a real business owner (Pryde) decide where to focus next. This is a win for the students and for Brandefy.

“As a startup, you have limited resources, right?” says the assistant professor. “You can’t just review everything at once, [but] you want to make your app as useful as possible to your early adopters so that it can hopefully become viral and get some word of mouth behind it. [My] students use data to come up with category-level statistics in order to guide that decision for Meg.”

Challenge: Making data analytics relatable

For many of Boichuk’s students, Customer Analytics is their first look at how data can reveal customer preferences and sales trends. Their previous experience with consumer behavior is likely limited to their own shopping habits—or perhaps any observations they may have made if they worked as a retail cashier.

In many data analytics courses, educators present students with case studies that allow them to garner and analyze consumer-behavior information in a way that will simulate work they may do in their future career. However, Boichuk wondered if this simulated work truly prepared students for the real-world business challenges they will face. He wanted to be certain that they are prepared with the critical thinking skills—and experience—they need.

Innovation: A case study with real-world results

With Brandefy, Boichuk saw an opportunity for students to contribute to a real, living, breathing company—thereby gaining experience that is not only informative but also a resume builder. Using actual consumer data is the core of Boichuk’s innovation. By drawing on vast sets of data that compare real products, prices, and customer buying habits, Boichuk’s students help refine Brandefy’s growth strategy—and get a crash course in critical thinking as well.

Of course, Boichuk’s students learn the practical application of data analytics, but they also have an opportunity to hone their presentation skills, as they are asked to provide Brandefy with data-driven advice on what to target next.

“[This real-life case study] is meant to be a decision-making process that can be generalized to many different settings,” says Boichuk.


Course: GCOM 7140 Customer Analytics

Frequency: Two 2.75-hour class meetings per week for 7 weeks

Class size: 40

Course description: A research-oriented class that examines how firms can leverage customer analytics to successfully create, manage, and grow brands.

GCOM 7140 Customer Analytics

See materials

Lesson: Assuming the role of startup business consultants

“This class is about evidenced-based management in marketing—applying the principles of data science to the study of customers. How do you make decisions about how to go to market with the data available available to you as a marketer?”

— Jeffrey Boichuk, PhD

Drawing primarily on consumer sales data provided by 84.51°, a business development and data analytics company that collaborates with the Kroger grocery store chain, Boichuk’s class analyzes sales data from approximately 1,000 households that have bought a variety of products at Kroger over a period of two years.

Such data sets can fine-tune students’ analyses of products even further, notes Boichuk. He and his students use these vast troves of data to study and recommend what products and product categories Brandefy should focus on as they move out of the health and beauty aisle.

For example, if generic versions of brand-name cereals are selling particularly well, that might serve as a first indicator to the students that perhaps Brandefy should include name-versus-generic cereal comparisons in the app. “The students can look at that data set, and they can see the concentration of sales that go to private labels in a given category,” says Boichuk.

Detailed data sets let their thinking go even further. “We can go beyond concentration of sales to things like price differences,” he explains. “Maybe, if the price difference is greater [between generic and brand-name cereals], then people may be more likely to consider the private label.” This may, in turn, make them wonder about the comparative quality, which could drive them to look for an app like Brandefy.

Here are the steps Boichuk follows to bring students from the role of “unwitting consumer” to that of “savvy business consultant” in just a few weeks:

Boichuk’s Bookshelf

These two books form the bulk of the reading material for the Customer Analytics course. Both are available online for free.

Start with gut reactions

Working in groups of five, Boichuk’s students first approach the case study by taking an intuitive view of data gathering and interpretation.

“I give them the case study on the first day of class, and I ask them, ‘OK, what is the key decision that needs to be made here?’” says Boichuk. “Then I ask them for their initial gut instinct about how Meg should make this decision.”

Encourage conceptualizing

Boichuk uses group discussion to help students develop conceptualizations. “I’ll put someone’s name up on the board and say, ‘What are your thoughts?’” says Boichuk. “And then I’ll ask the students to discuss that, and I’ll ask them what they might do differently.”

“What this conversation does is get them thinking about that decision-making process and how they might [examine these product] categories from a conceptual level,” says Boichuk. “What’s important from a conceptual level is different from how you actually measure it with data.”

Now dive into the data

Once students have a bird’s-eye view of how Brandefy might expand into new categories, Boichuk exposes them to sequential steps of data analysis—data transformation, data visualization, and exploratory data analysis. This helps foster the kind of dynamic, out-of-the-box thinking that succeeds in the real world.

First, Boichuk teaches the students about “data transformation” by familiarizing them with computer programming languages that organize vast data sets into more manageable—and more interesting—chunks.

“Let’s say you have all the sales at Kroger for this panel of customers over two years,” says Boichuk. “What if you only want to look at the first year for one reason or another? Or what if you only want to look at households with four people in them? Now what if you only want to look at singles? How do you do that?”

Next, Boichuk emphasizes “data visualization”—ways of making data visible and graphical through line charts, bar charts, and histograms. “[Students] can create all kinds of visualizations in order to show visually in a slide what their insights are from the data,” says Boichuk.

“Exploratory data analysis” drills down even further into the data, allowing the students to discern product price fluctuations over time and in relation to other variables that might appear elsewhere in the data. “How do you look for relationships between variables?” says Boichuk. “And how do you look for variation within a variable?”

Pull it all into a (competitive) presentation

Groups make their final Brandefy recommendations in seven-to-eight-minute presentations given at the end of the semester. Boichuk organizes the presentations in the mode of a friendly competition, with the winning group, as judged by Pryde, receiving a free dinner out with her and the professor.

Boichuk rates each presentation by assessing whether the group:

  • Discussed the data they analyzed in a simple, easy-to-understand way
  • Built an argument with a breadth and depth of analysis that motivated him to accept their claims
  • “Edutained” by fusing scientific rigor with canny attention-grabbing
  • Responded to questions and reservations appropriately
  • Presented as a team with comparable and complementary contributions

“They need to build an argument with a breadth and depth of analysis that motivates people to accept their claims and go along with them. As consultants, that’s really their task: Thinking deeply about a problem or decision a business is facing, and then coming up with a recommendation.”

Make them consider their process, too

In their final presentation, Boichuk wants students to move beyond making a pitch on what categories Brandefy should go into. “They’re really meant to be taking us through the decision-making process they went through over the course of the semester,” he says.

“If you don’t outline the sausage-making process, you can easily lose your audience,” adds Boichuk. “So I make sure that they know: ‘You’re not just selling the end product. You’re first and foremost selling how you got there.’”

Finish with a lesson in constructive critique

Following each presentation, Boichuk has his students peer-evaluate each other using a Google form, and he allows for two to three minutes of question-and-answer follow-up.

“I tell [students] that, for the university to succeed over time, it has to be a place where ideas are combatted. So one idea and another idea can go to battle in a class, and that may have one student on one side and one student on the other. It’s all constructive.”

— Jeffrey Boichuk, PhD

“I tell them that, for the university to succeed over time, it has to be a place where ideas are combatted. So one idea and another idea can go to battle in a class, and that may have one student on one side and one student on the other,” he says. “It’s all constructive.”

It also offers students an opportunity to practice having a growth mindset: to deliver feedback diplomatically; to receive feedback with gratitude; and to filter, analyze, and apply all of the data they have accumulated so they can become the best possible version of themselves.


The efforts Boichuk’s students have put in to Brandefy as part of their classwork have had real-world results, says the app’s creator, Meg Pryde.

The startup is ramping up: Brandefy’s users—now numbering several thousand—have opened the app to make comparisons more than 30,000 times collectively, and Brandefy was recently accepted into the Lighthouse Labs accelerator program, which provides seed money and connections to startups. (Version 2.0 is scheduled to roll out in September 2018.)

“At this stage, it’s a balance between thinking about the big picture and executing on very practical suggestions,” says Pryde. “I most appreciated the student groups who provided the most specific, practical advice and backed it up with their strategy.”

For example, the app recently added frozen foods (including pizza) to their categories as a result of student recommendations.

The students’ input has inspired the Brandefy team to think more critically about the assumptions they were making about their users, too. “[We wondered how we] could pair user search data with what [students] were providing,” says Pryde. “Recently, we [tested] if marketing to ‘Aldi shoppers’ is easier than marketing to people who like pizza, for example. Testing what [students] suggested has opened us up to new hypotheses.”

Student feedback

Boichuk’s efforts have paid off in the compliments he receives from students who have moved on to new challenges in both the academic and business worlds.

“I hear from them months after the course,” says Boichuk. “They are applying [team collaboration concepts] that we talked about in class to the teams they are on now, and it’s going well.”

Overall, he says, students express gratitude more than anything else. “They talk about how I introduced them to the world of data science, and they’re so thankful,” says Boichuk. “I have these wonderful students who write to me out of the blue, and it’s really, really, rewarding.”