Dr. Gokhan Yildirim is an Associate Professor of Marketing at Imperial College Business School. He is also the author of the book Applied Marketing Analytics Using R, published by SAGE. His research is at the intersection of marketing effectiveness, metrics, and models. Specifically, his work addresses the short- and long-term effectiveness of digital and non-digital marketing activities, cross-channel marketing resource allocation, and the use of consumer attitudinal metrics to guide marketing decisions. He employs applied time-series econometrics, machine learning, and dynamic programming tools to provide managerial insights in these areas.
Gokhan's research has been published in leading journals in the field, including the Journal of Marketing, Marketing Science, the International Journal of Research in Marketing, and the Journal of the Academy of Marketing Science. He has received prestigious awards, such as the Amazon Research Awards in Advertising, the ISMS-MSI Gary Lilien Practice Prize Competition award, and the Marketing Science Institute research award. He has also presented his academic work at major conferences in the field, such as INFORMS Marketing Science, Theory and Practice in Marketing (TPM), Marketing Dynamics, and EMAC.
His research has been supported by several research grants from organizations including Amazon, the Wharton Customer Analytics Initiative (WCAI), the Marketing Science Institute, AiMark, and the Spanish Ministry of Science and Innovation.
Gokhan earned his Ph.D. in Business Administration and Quantitative Methods with a specialization in marketing from Carlos III University in Madrid. He holds a B.A. degree in Business Administration from Marmara University in Istanbul, Turkey.
At Imperial College, Gokhan teaches in the Executive MBA, MSc Business Analytics, and MSc Strategic Marketing programs.
et al., 2018, App popularity: where in the world are consumers most sensitive to price and user ratings?, Journal of Marketing, Vol:82, ISSN:1547-7185, Pages:20-44
et al., 2017, Can retail sales volatility be curbed through marketing actions?, Marketing Science, Vol:36, ISSN:1526-548X, Pages:232-253
Esteban-Bravo M, Vidal-Sanz JM, Yildirim G, 2014, Valuing customer portfolios with endogenous mass and direct marketing interventions using a stochastic dynamic programming decomposition, Marketing Science, Vol:33, ISSN:0732-2399, Pages:621-640
et al., 2014, Consumer attitude metrics for guiding marketing mix decisions, Marketing Science, Vol:33, ISSN:0732-2399, Pages:534-550