09-22-2026
As businesses navigate an economy increasingly shaped by digital platforms and artificial intelligence, understanding how customers make complex choices and how firms should make strategic choices on product design and monetization using rich sources of data have become more important than ever.
Vineet Kumar brings to Purdue University a research portfolio focused squarely on these questions.
Kumar joins the Mitch Daniels School of Business as a professor in the Marketing Department after serving on the faculties of Harvard University and Yale University. He was hired through Purdue’s Moveable Dream Hires program, a program designed to attract high-performing, top-caliber faculty to the university.
Kumar earned his doctorate from Carnegie Mellon University, where his research received the William Cooper Dissertation Award. His research has also earned recognition from organizations including the American Statistical Association, INFORMS and the Marketing Science Institute. He serves as a senior editor at Production and Operations Management and as an associate editor at Management Science.
His interest in marketing grew from an experience outside academia. Trained as an engineer, Kumar worked on a startup and saw firsthand that building a good product was only part of the challenge.
“The engineering mindset is very different,” he says. “You think that the product is the primary thing that matters. But I think when you have a product that goes to market, what’s equally important or perhaps even more important is how the consumer experiences it.”
That experience led him to examine the “consumer lens” — how customers perceive products, evaluate value and make decisions in complex digital settings. It also helped establish a theme that continues to run through his research: combining an understanding of individual human behavior with sophisticated analytical methods.
Digital technologies have fundamentally changed both sides of the marketplace. Consumers have more choices, while companies have access to more detailed information about what customers purchase and how and when they use product features.
One of Kumar’s research projects, for example, examines the additional value of usage data alongside traditional purchase data. In a physical marketplace, researchers and companies might know that a customer purchased a product. Digital technologies can also reveal what happens after that purchase, providing new opportunities to understand behavior.
Platforms such as Airbnb, Uber and eBay represent another important area of interest for Kumar. These businesses use digital technology to connect different groups of participants and create value across both sides of the marketplace. Digital technology also allows companies to pursue business models that would have been difficult or impossible in traditional markets, including monetization models such as freemium versioning that offer a basic product or service for free while charging money for advanced features or enhanced functionality.
“Digital enables a lot of strategic options for firms that were not possible without it in the pre-digital or physical economy,” Kumar says.
His broader research program examines how firms can use these new technologies to transform themselves. Netflix, for example, evolved from a DVD-by-mail company into a streaming business and continues to reinvent its approach as technology and consumer behavior changes.
Much of Kumar’s current research sits at the intersection of digital business and artificial intelligence, particularly machine learning.
He wants to bring human and domain knowledge into machine learning models. “Almost all of my research essentially starts with a theoretical lens,” Kumar says. “What do we know about how individual consumers make choices? What’s our source of knowledge from different academic fields?”
Drawing on economics for instance, his research combines structural models of consumer and firm behavior with theory-based machine learning to produce effective, interpretable and trustworthy models that provide critical insight to decision-makers.
His approach seeks to translate complex information so it is not locked in a black box. “I focus on explainable and interpretable machine learning, where we can actually understand how the model works,” he says.
That focus is particularly relevant as machine learning makes it possible to analyze previously difficult-to-use sources of unstructured data, including text, audio and video. Kumar has studied how such information can help explain consumer decisions. In one example, visual characteristics of a vehicle can provide insights into consumer preferences that conventional numerical data such as horsepower or fuel efficiency cannot fully capture.
Kumar sees Purdue’s strength in STEM disciplines as an important foundation for expanding his research agenda.
“I think it’s a great opportunity to develop my research and collaborate with others at Purdue,” he says, particularly around systems that bring together “human domain knowledge and machine learning.”
That collaborative vision is already taking shape. Kumar is organizing an AI roundtable lunch in collaboration with the Krenicki Center for Business Analytics and Machine Learning. The gathering will bring together faculty from different disciplines to explore how established domain knowledge can be incorporated into machine learning to improve performance, interpretability and other characteristics.
“Marketing is primarily about the consumer,” he says, “but I’m sure there’s a lot of domain knowledge in other fields and I look forward to learning more.”
He also is organizing a conference on AI and the customer interface, scheduled for fall 2027, to bring together scholars and practitioners to examine the state-of-the-art thinking and practice in the field.
By connecting that knowledge with advances in AI and machine learning, Kumar aims to develop research that positions the Daniels School at the forefront of understanding how AI, humans and businesses will interact in the digital economy.
