Luckily, Kanuri, an associate professor of marketing, is able to regularly draw new content and decision models into his class via his prolific research. Using econometric, optimization and machine-learning techniques, he evaluates the performance and consumer welfare implications of marketing strategies within the domains of social media, services and multichannel marketing. Each time he publishes a paper, Kanuri converts the dataset into a case, allowing students the opportunity to tackle real-world problems directly.
“I've only taken on projects that have managerial implications and can be readily applied in practice,” he said. “Most of my students list these projects on their résumés, which I encourage them to do, because in some cases they're accomplishing more than they would during a summer internship. Sometimes they come back from an interview and tell me that's all the interviewer was interested in.”
Kanuri’s practical approach to marketing decision models began when he was working toward his Ph.D. and attended a conference at the Reynolds Journalism Institute at the University of Missouri, which convened a number of national newspapers. “When I sat in that meeting, I was just baffled to hear that every single problem they discussed was actually a marketing problem,” he said.
He became fascinated with pricing-related issues at major papers and eventually collaborated with a major West Coast newspaper, writing his dissertation on a subscription algorithm he developed that aligned both customer and advertiser preferences. Within six months of implementing a variant of Kanuri’s algorithm, the paper reported nearly $1 million in profits. Suddenly, other departments at the newspaper were eager to work with him.
Kanuri’s interest in the area corresponded with the industry’s increasing reliance on social media platforms, which were proving to be as unpredictable as they were influential. This time, the newspaper solicited his help in determining how to use social media to drive content to the news site. The resulting paper, Scheduling Content on Social Media: Theory, Evidence, and Application, was published in the Journal of Marketing and also formed the backbone for one of his course’s group projects.
“How do you go from a model to making a decision for an actual problem? That is the part we cover in class,” said Kanuri. In this case, his students reviewed data comprising the newspaper’s Facebook posts to observe aspects such as the time stamp, title, description, whether the post was boosted and how many link clicks it received. After analyzing the data, the students built a regression model to forecast link clicks and then created a scheduling tool that could demonstrate a correlation to a profit function.
“They've really enjoyed the practical aspects of this,” he said. “They get to see both the theoretical relevance and the managerial application of the content we cover in class.”
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