Koray is a researcher who passionately explores the intricate decision-making processes of consumers and firms, with a focus on enhancing firm-level performance outcomes.
His expertise lies in two dynamic areas: unraveling the complexities of consumer behavioral decision-making and its pivotal role in pricing, and strategizing firms’ non-pricing activities, including promotions, strategic placement, and product development. Koray’s research has been featured in prestigious business journals such as Marketing Science, the Journal of Marketing Research, Management Science, and the Journal of Retailing.
Koray’s investigative pursuits bridge the intersections of marketing, economics, and psychology. He delves into a variety of phenomena, from consumer switching costs to the decoy effect, reference pricing, and dynamic pricing strategies. He also examines non-pricing strategies that shape retail and marketing landscapes, including trade promotions, e-mail marketing, sponsorships, and product packaging, in both emerging and developed markets.
Methodologically, he employs a wide range of quantitative approaches, tailoring them to each unique research question and dataset. His expertise includes static and dynamic structural models, various reduced form models, optimization techniques, experimental designs, causal inference methods, and computational game theory.
As an award-winning educator, Koray has had the privilege of teaching Marketing Research, Direct Marketing, and a Structural Modeling Seminar at Georgia State University. At Indiana University, he taught Marketing Analytics and a seminar on Empirical Modeling of Consumer and Firm Decisions. Koray is scheduled to teach Pricing in Fall 2024.
Koray serves as an ad-hoc reviewer for several prestigious journals, including Marketing Science, Management Science, Journal of Marketing Research, Journal of Marketing, International Journal of Research in Marketing, Production and Operations Management, Information Systems Research, Journal of the American Statistical Association, and Journal of Business Research. Additionally, he chaired the "AI, Models, and Methods" and the "Big Data, AI, and ML Insights" tracks at the 2023 and 2024 Winter AMA Conferences.
Empirical Industrial Organization, Static and Dynamic Pricing, Switching and Staying Costs, Reference Price Effect, Decoy Effect, Emerging Markets, Diffusion of New Products, Retail Distribution, Trade Promotions, Product Packaging
Academic Degrees
Ph.D. in Marketing, Washington University in St. Louis, 2013
M.A. in Economics, Koc University, 2008
B.S. in Industrial Engineering, Bilkent University, 2004
Professional Experience
Associate Professor, Indiana University, 2024 – present
Assistant Professor, Indiana University, 2018 – 2024
Assistant Professor, Georgia State University, 2013 – 2018
Awards, Honors & Certificates
Winner of the IU Trustees Teaching Award, 2024
Best Paper Award, Digital Marketing Track at 2015 Summer AMA, Chicago, IL, 2015
AMA Sheth Foundation Doctoral Consortium Fellow, 2012
Hubert C. Moog Scholar, Washington University in St. Louis, 2012
Doctoral Fellowship, Ph.D. Program, Washington University in St. Louis, 2008-2013
Full Scholarship, M.A. Program, Koc University, 2006-2008
Full Scholarship, M.A. Program, Turkish National Science Foundation, 2006-2008
Full Scholarship, B.S. Program, Bilkent University, 1999-2004
Selected Publications
Cosguner, K., and Seetharaman, S. – Equal Authorship (2022). Dynamic Pricing for New Products Using a Utility-Based Generalization of the Bass Diffusion Model. Management Science, 68(3), 1904-1922.
Abstract
The Bass Model (BM) has an excellent track record in the realm of new product sales forecasting. However, its use for optimal dynamic pricing or advertising is relatively limited since the Generalized Bass Model (GBM), which extends the BM to handle marketing variables, uses only percentage changes in marketing variables rather than their actual values. This restricts the GBM's prescriptive use, for example, to derive the optimal price path for a new product, conditional on an assumed launch price, but not the launch price itself. In this paper, we employ a utility-based extension of the BM, which can yield normative prescriptions regarding both the introductory price, as well as the price path after launch, for the new product. We offer two versions of this utility-based diffusion model, namely the Bass-Gumbel Diffusion Model (BGDM) and the Bass-Logit Diffusion Model (BLDM), the latter of which has been previously used by Krishnan, Seetharaman and Vakratsas (2012). We show that both BGDM and BLDM handily outperform the GBM in forecasting new product sales using empirical data from four product categories. We discuss how to estimate BGDM and BLDM in the absence of past sales data. We compare the optimal pricing policy of the BLDM with the GBM and derive optimal pricing policies that are implied by the BLDM under various ranges of model parameters. We illustrate a dynamic pricing approach that allows managers to derive optimal marketing policies in a computationally convenient manner; and extend this approach to a competitive, multi-product case.
Sharma, A., Cosguner, K., Sharma, T., and Motiani, M. (2021). Channel Intermediaries and Manufacturer Performance: An Exploratory Investigation in an Emerging Market. Journal of Retailing, 97(4), 639-657.
Abstract
Channel intermediaries (hereafter, intermediaries) are among the most critical elements of any supply chain as the bulk of manufacturing output is transported through them. However, we have a limited understanding of the approach a manufacturer should take to achieve the dual goal of increasing profitability and intermediary satisfaction. To provide manufacturers with practical strategies to boost their performance and their intermediaries' satisfaction, we rely on three related studies. In Study 1, through interviews with managers and intermediaries, we find that distribution alignment across intermediary, market, and product types may be a practical strategy for both manufacturers and intermediaries. In Study 2, using a robust empirical methodology with data from a construction product manufacturer operating in an emerging market, we find that various combinations of intermediary, market and product types affect the manufacturer's performance differently. Accordingly, through a supply-side examination, we quantify the revenue impact of reallocating the studied manufacturer's retail distribution resources among different intermediary-, market-, and product-type combinations. In Study 3, through a field implementation, we find that our recommended intermediary alignment strategy from Study 2 substantially boosts both the manufacturer's performance (by 14% in revenue and 13% in profit) and intermediaries' satisfaction (by 7.71%). Thus, with our qualitative, empirical, and field studies, we contribute to the existing research in channel management, emerging market retailing, retail distribution, and marketing strategy.
Wu, C., and Cosguner, K. (2020). Profiting from the Decoy Effect: A Case Study of an Online Diamond Retailer. Marketing Science, 39(5), 974–995
Abstract
The decoy effect (DE), first introduced by Huber et al. (1982), has been robustly documented across dozens of product categories and choice settings using lab experiments. However, it has never been verified in a real product market in the literature. In this paper, we empirically test and quantify the DE in the diamond sales of a leading online jewelry retailer. We develop a diamond-level proportional hazard framework by jointly modeling market-level decoy-dominant detection probabilities and the boost in sales upon detection of dominants. Results suggest that decoy-dominant detection probabilities are low (11%-25%) in the diamond market; however, upon detection, the DE increases dominant diamonds' sale hazards significantly (1.8-3.2 times). In terms of the managerial significance, we find that the DE substantially increases the diamond retailer's gross profit by 14.3%. We further conduct simulation studies to understand the DE's profit impact under various dominance scenarios.
Sharma A., Kumar, V., and Cosguner, K. – Equal Authorship (2019). Modeling Emerging Market Firms’ Competitive Retail Distribution Strategies. Journal of Marketing Research, 56(3), 439–458.
Abstract
In emerging markets, the effective implementation of distribution strategies is challenged by the underdeveloped infrastructure of roads, and low penetration of retail stores that are insufficient in meeting customer needs. In addition, products are typically distributed in multiple forms through multiple retail channels. Further, due to the competitive landscape, manufacturers’ distribution strategies should be based on the anticipation of competitor reactions. Accordingly, we develop a manufacturer-level competition model to study the distribution and price decisions of insecticide manufacturers competing across multiple product forms and retail channels. Our study shows that both consumer preferences and estimated production and distribution costs vary across brands, product forms, and retail channels; that ignoring distribution and solely focusing on price competition results in up to a 55% overestimation of manufacturer profit margins; and that observed pricing and distribution patterns support competition rather than collusion among manufacturers. Through counterfactual studies, we show that manufacturers respond to decreases in distribution costs and to the exclusive distribution of more preferred manufacturer by asymmetrically changing their price and distribution decisions across different retail channels.
Cosguner K., Chan, T. Y., and Seetharaman, S. (2018). Dynamic Pricing in a Distribution Channel in the Presence of Switching Costs. Management Science, 64(3), 1212-1229.
Abstract
We advance the literature on dynamic oligopoly pricing models in the presence of switching costs by additionally modeling the strategic pricing role of the retailer within the distribution channel. In doing this, we study the relative dynamic pricing implications of how current retail and wholesale prices for a brand must optimally take into account past and future demand, respectively, for the brand. Using scanner data from the cola market, we find that while the retailer exploits the benefit of inertial demand by appropriately increasing the retail profit margin, the cost of investing is borne entirely by the manufacturers. We use simulation studies to show how the retailer will lose its ability to leverage the benefits of inertial demand as consumers become more price sensitive. We also show that when inertia of the more price-sensitive customer segment increases, the aggregate welfare of consumers, the retailer, and manufacturers may increase.
Cosguner K., Chan, T. Y., and Seetharaman, S. (2017). Behavioral Price Discrimination in the Presence of Switching Costs. Marketing Science, 36(3), 426-435.
Abstract
We study the strategic impacts of behavioral price discrimination (BPD) on manufacturers and retailers in a distribution channel when there are switching costs in consumer demand. Unlike previous empirical studies of behavioral price discrimination, which rely only on differences in price elasticity across customers, our pricing model allows the firm strategies to additionally account for differences in price elasticity across time (due to switching costs). We estimate a dynamic pricing model using empirical data from the cola category and, through a series of counterfactuals, we find that the retailer should simply outsource the data analytics and customization of coupons to manufacturers and improve its profit beyond what it can achieve by proactively couponing on its own. We further find that serving as an information broker to sell its customer database to manufacturers can be a vital source of profit to the retailer. By contrast, manufacturers end up worse off, illustrating that customer information is a potent source of channel power to the retailer. Finally, we show that simply using customers’ most recent purchase information can significantly impact firms’ profits. BPD based on this information is easy to implement and of low cost to manufacturers and retailers.
Zhang, X., Kumar, V., and Cosguner, K. – Equal Authorship (2017). Dynamically Managing a Profitable Email Marketing Program. Journal of Marketing Research, 54(6), 851-866.
Abstract
Although email marketing is highly profitable and widely used by marketers, it has received limited attention in the marketing literature. Extant research has focused on either customers’ email responses or the “average” effect of emails on purchases. In this article, the authors use data from a U.S. home improvement retailer to study customers’ email open and purchase behaviors by using a unified hidden Markov and copula framework. Contrary to conventional wisdom, the authors find that email-active customers are not necessarily active in purchases, and vice versa. Furthermore, the number of emails sent by the retailer has a nonlinear effect on both the retailer’s short- and long-term profitability. Through a counterfactual study, the authors provide a decision support system to guide retailers in making optimal email contact decisions. This study shows that sending the right number of emails is vital for long-term profitability. For example, sending four (ten) emails instead of the optimal number of seven emails can cause the retailer to lose 32% (16%) of its lifetime profit per customer.
