Dr. Gokhan Yildirim is an associate professor of marketing at Imperial College Business School. His research is at the intersection of marketing effectiveness, metrics and models. Specifically, his research concerns the short- and long-term effectiveness of digital and non-digital marketing activities, cross-channel marketing resource allocation, and consumer attitudinal metrics for guiding marketing decisions. He uses applied time-series econometrics and dynamic programming tools to offer managerial insights in these areas.
Gokhan’s work has appeared in leading journals of the field such as the Journal of Marketing, Marketing Science and the International Journal of Research in Marketing. He is the recipient of the prestigious awards such as Amazon Research Awards in Advertising, ISMS-MSI Gary Lilien Practice Prize Competition award and Marketing Science Institute research award. His academic work was presented in the major conferences of the field such as INFORMS Marketing Science, Theory and Practice in Marketing (TPM), Marketing Dynamics and EMAC.
His research has been sponsored by several research grants from Amazon, Wharton Customer Analytics Initiative (WCAI), Marketing Science Institute, AiMark, and Spanish Ministry of Science and Innovation.
He received his Ph.D. in Business Administration and Quantitative Methods with a specialization in marketing from Carlos III University, Madrid. He holds a B.A. degree in Business Administration from Marmara University, Istanbul, Turkey.
At Imperial, Gokhan teaches on the executive MBA, MSc Business Analytics and MSc Strategic Marketing programmes.
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