Professor Mitra’s research encompasses innovation and new product management, their antecedents and consequences on organizations, customers, and economies, and the development of analytics in these areas. He uses both analytical and econometric methods to understand these effects over time as well as across brands, firms, industries, and countries.
Areas of Expertise
University of Pittsburgh
Indian Institute of Management
Indian Institute of Technology
Four Insights on How the Great Brands Fail
During the last 15 years, we’ve studied when and why leading brands persist as leaders over periods ranging from five to 89 years. Four insights have been the most surprising to executives and investors.
Casino marketing wars heat up over bragging rights to claim "first"
Hartford Courant online
An advertising campaign that slaps back keying in on a single word — in this case, “first” — can be tricky to pull off, marketing experts say. “You’re paying too much credence to their use of the word ‘first’ and you’re, in some ways … ending up helping them,” said Debanjan Mitra, professor of marketing and Voya Financial Chair at the University of Connecticut School of Business in Storrs. “MGM is the one who came up with ‘first,’ and now if you keep talking first, chances are the customers who see all the ads, the only thing that they will remember is MGM.”
Soapbox: Value of research
Financial Times online
We have found that when a business school generates more research, its graduating students’ salaries go up. In fact, an increase of three single authored articles per year across the school leads to a graduate’s starting salary going up by $750. This amount is above the salary graduates would normally receive based on their quality upon entering an MBA programme (as reflected by a high GMAT score or low school acceptance rate) and a business school’s resources.
A Theory for Market Growth or DeclineMarketing Science
2013 Our theory suggests that market participants repeatedly take successful and unsuccessful actions that cause them to change or to mutate in myriad and often unpredictable ways. The environment sorts these mutations, determining winners and losers. Abundant mutations often cause different market participants to become winners, displacing past winners.
What is Quality? An Integrative Framework of Processes and States,Journal of Marketing
2012 Quality is a central element in business strategy and academic research. Despite important research on quality, an opportunity for an integrative framework remains. The authors present an integrative framework of quality that captures how firms and customers produce quality (the quality production process), how firms deliver and customers experience quality (the quality experience process), and how customers evaluate quality (the quality evaluation process).
Customer Portfolio Composition and Customer Equity Feedback Effects: Student Diversity and Acquisition in Educational CommunitiesMarketing Letters
2012 Researchers in marketing have long recognized that current populations of customers can influence the behavior of prospective customers. This paper draws on existing marketing theories to empirically examine how changes in student body demographic segments influence future demand for MBA programs. Using a longitudinal analysis of data spanning 18 years, we find that higher proportion of female students leads to significant increases in future applications.
Managing Service Expectations in Online Markets: A Signaling Theory of E-tailer Pricing and Empirical TestsJournal of Retailing
2010 Expectations play a significant role in determining customer perceptions and satisfaction. Accordingly, retailers seek to manage customers’ service expectations. However, the tangible signals of service quality that are available to brick-and-mortar retailers (such as location, store appearance, and salespersons’ behavior) may not be available in online markets.
Metrics – When and Why Non-Averaging Statistics WorkManagement Science
2008 Good metrics are well-defined formulae (often involving averaging) that transmute multiple measures of raw numerical performance (e.g., dollar sales, referrals, number of customers) to create informative summary statistics (e.g., average share of wallet, average customer tenure). Despite myriad uses (benchmarking, monitoring, allocating resources, diagnosing problems, explanatory variables), most uses require metrics that contain information summarizing multiple observations.