You can contact Aidin Namin at firstname.lastname@example.org.
Aidin Namin is assistant professor of marketing at the College of Business Administration at Loyola Marymount University. A modeler by training and passion, Dr. Namin received his Ph.D. from the University of Texas at Dallas. He received an MBA and a bachelor’s degree in industrial engineering from Sharif University of Technology. Applying quantitative and econometrics techniques, his research interests are marketing and data analytics, retailing, pricing, and digital marketing. Dr. Namin has received both research and teaching awards from different institutions. He is the recipient of the Best Paper Award for the Marketing Analytics track at 2018 SMA Conference, the 2018 Paul R. Lawrence Fellowship, LMU Hybrid Course Development Grant, LMU Internal Assessment Grant, LMU Academic Technology Grant, Junior Faculty Award, Fellowship Award for Established Record of Excellence in Research from the University of Idaho, and the Analytics for Purpose Grant. Dr. Namin has also received a Teaching Award as the Ph.D. Student Teacher of the Year from the University of Texas at Dallas. His research has been published in academic journals including the Journal of Retailing and Consumer Services, Journal of Marketing Analytics, and Journal of Marketing Communications. He is currently serving on the Editorial Board of Journal of Business Research and Journal of Marketing Analytics. He has presented his work in conferences such as Marketing Science and the American Marketing Association (AMA) in multiple occasions. Prior to joining LMU, Dr. Namin served as a tenure-track assistant professor of marketing at the University of Idaho. Before starting the PhD program, Namin worked in the industry as a data analyst and market researcher for some years. On the personal side, Aidin considers himself a family guy. He cares a lot about his family as well as his students. He enjoys traveling and outdoor activities. He also spends a big part of his free time cooking and trying new food recipes found on the Internet!
University of Texas at Dallas: Ph.D., Marketing 2015
Sharif University of Technology: MBA, Graduate Studies 2010
Sharif University of Technology: B.S., Industrial Engineering 2006
Areas of Expertise (6)
Industry Expertise (2)
ACME Teaching Innovation Award (professional)
Winner of the Teaching Innovation Award from ACME in 2020. This is a competitive national teaching award for marketing educators.
AMS and AFM Grant Awardee (professional)
Received a grant from the Academy of Marketing Science (AMS) & Association Française du Marketing (AFM), 2020.
Thought Leader in Retailing Research (professional)
Recognized as a Thought Leader in Retailing Research by the Retailing Thought Leadership Conference in 2019, sponsored by AMA Retailing and Pricing SIG.
LMU Ascending Scholar Award (professional)
Recipient of 2019 Ascending Scholar Award for excellence in research at Loyola Marymount University
Best Paper Award for Analytics and Big Data (professional)
The 2018 Palgrave Macmillan Best Paper Award for Analytics and Big Data from the Journal of Marketing Analytics.
Marketing EDGE Best Paper Winner in Analytics (professional)
Marketing EDGE Best Paper Winner in Analytics. 2018 Society for Marketing Advances Conference
2018 Paul R. Lawrence Award (professional)
2018 Paul R. Lawrence Award from the Case Research Foundation. This award is given to only five junior faculty across the globe every year.
LMU Online/Hybrid Course Development
LMU Internal Assessment Summer Grant
LMU Academic Technology Summer Grant
Editorial Board Member (professional)
Journal of Marketing Analytics
Editorial Board Member (professional)
Journal of Business Research
Marquis Who's Who biographical listee (professional)
Through direct invitation from the publisher
Excellence Junior Faculty Fellowship Award (professional)
Gary Michael Idaho Power award for Established Record of Excellence in Research
Junior Faculty Award (professional)
Winner at the 2016 Western Decision Sciences Institute Annual Meeting
Foster and Framing Excellence in Teaching Award (professional)
Invited by the Teaching & Advising Committee at the University of Idaho
Grant for Analytics with Purpose (professional)
Innovation, Impact and Outreach grant from the College of Business and Economics, University of Idaho
Outstanding Ph.D. Student Teacher of the Year (professional)
Winner of the Teaching Award at the University of Texas at Dallas for the 2014-2015 academic year
Teaching Award Nominations (professional)
Twice nominated for Teaching Award at the University of Texas at Dallas
Aidin Namina, Dinesh K.Gaurib, Robert J.Kwortnikc
The leisure cruise industry has enjoyed high levels of growth for nearly five decades due in part to traveler interest in the cruise experience, but also to relatively lower pricing. Although revenue management of cruise fares is now standard practice, there are untapped opportunities to improve yields through data-driven market segmentation and third-degree price discrimination. This paper uses a finite mixture modeling approach to develop, empirically validate, and compare pricing models. By unveiling segments of travelers based on individual attributes, third-degree price discrimination can improve target marketing, the timing and appeal of price discounts, and the matching of variable demand with fixed, though differentiated, room supply. Empirical results from running thousands of simulations with pricing data from one of the world’s largest cruise lines show that the segmentation analysis using third-degree price discrimination can increase fare revenue more than four percent. The modeling approach used in this research extends the emerging literature on revenue management in the cruise industry and offers meaningful managerial implications for advanced pricing tactics and revenue management.
The purpose of this paper is to demonstrate how consumers choose among three different options offered by a firm in a monopolistic setting, namely, to buy a standard product with a non-customizable design, to ask the firm to customize a product using the consumer’s ideal design or to do the entire design task by themselves. The authors also investigate how social preference intensity and the possibility of reselling a product influence a consumer’s decision.
A crowdsourcing contest is the process of inviting the general public or a targeted group of individuals to submit their ideas or solutions to a specific problem or challenge within a predefined period of time. In this study, utilizing nearly 500 participants, we design and run a field experiment to model two types of feedback: rated and ranked. We also measure the effect of each on the likelihood of revising the generated ideas and their subsequent quality based on novelty, feasibility, and value criteria in a crowdsourcing contest. Our major findings indicate that providing any type of feedback, compared to no feedback, improves the quality of ideas generated.
Students, in general, get into undesirable eating habits, partly due to the decrease in consumption of unhealthy, prepared food items (e.g., take-out). This research applies a multi-method approach to modeling the motivations behind cooking behavior for this cohort of young-adult consumers. Focus groups are conducted and findings are incorporated into an integrative framework to develop and estimate three quantitative choice models for predicting millennials’ cooking behavior.
When judging the expensiveness of a product or service, consumers often make comparisons to similar offerings that serve as reference points. Extant pricing literature shows that reference items in the consideration set may trigger a “contrast effect,” where higher-priced items make the target item seem less expensive. Two studies show that the effect of reference price depends on the consumer’s level of abstract thinking—or “construal level” —at the time of judgment. Concrete construal leads to the standard contrast effect, but abstract construal leads to an assimilation effect, where higher-priced reference items make the target seem more expensive.
This study identifies hidden classes of grocery shoppers and their choice of different items on different days of the week. Following the literature on consumer grocery shopping, three major groups of products are considered: food/drink, cleaning, and personal care. Applying Finite Mixture Modeling to a rich scanner dataset, latent classes of customers and their choice of grocery items on different days of the week are discovered and empirically validated. The model controls for consumer unobserved heterogeneity and demographic characteristics through mixing probabilities. Results uncover latent classes of grocery shoppers and their day of the week shopping day, their sizes, their product choices, mixing probabilities, and demographics. Findings offer retail promotion targeting guidelines for the identified latent classes in the food/drink, cleaning, and personal care groups. Analysis outcome provides marketing and managerial implications in identifying grocery store segments, handling store traffic, managing store promotion and pricing, and improving store layout.
In this research, we address an important gap in the literature as to the search behavior of new car buyers. While the effect of the Internet on this process is known, the literature still lacks a comprehensive study which (1) concurrently covers time periods before and after the launch of the Internet, and (2) compares trends of consumer search across those combined years. Our unique survey dataset, which spans 22 years and includes consumer search information for new cars from both the pre- and post-Internet eras, enables us to investigate this important gap. Using a latent class model, we classify respondents according to variables that measure consumer search for new automobiles. We unveil changes in characteristics of the six latent segments of car shoppers. Our main findings show that, over the years since the advent of the Internet, the segment of car buyers who mainly search through car dealers/stores has been shrinking drastically. We also find evidence that, over time, the heavy Internet user segment has become less likely to have decided on the manufacturer/dealer prior to searching. Our findings benefit researchers, practitioners, car manufacturers, dealers, and buyers.
Following past research examining online advertising design and effectiveness, this research studies the impact of variations in the design of online banner advertisements on advertising involvement and effectiveness. Advertisement involvement and effectiveness are measured as response to changes in message design and are determined by the number of clicks on the banner ad (involvement) as well as the click-through rate, or CTR (effectiveness). The latter is the ratio of ad clicks to its total impressions. Related to the message design, the type (static or dynamic), size (pixel ratios), and the format of a banner advertisement are studied employing behavioral response data from a single apparel retailer. Results suggest that the type of banner advertisement significantly influences advertising involvement and effectiveness. Results also suggest that banner ad size in terms of pixel ratios significantly increases advertising involvement through total number of clicks but does not affect effectiveness through CTR. Our findings also identify and empirically validate the important role of the Golden Ratio in banner ad message design and its effectiveness.
In this paper, using data from a leading specialty apparel retailer, we empirically examine the determinants of a retailer's dynamic pricing policy and investigate consumer response to price changes (markdowns) throughout a fashion product's selling season using a product diffusion setting.
This research is an extension to previous work in fast food restaurant marketing. The population of this research consists of actual fast food restaurant customers. Results indicate that there is no direct way of increasing behavioral intentions through improving service quality for fast food restaurants. Rather, behavioral intentions can be improved through customer satisfaction as an intermediary. Further, this work finds evidence that customer satisfaction can be improved through service quality, food quality, and price-value ratio, which in turn would pave an indirect path toward improvement in behavioral intentions in this industry.
The focus of this research is on assessing the quality of services of Tehran’s Saman bank and the available gap between customer’s expectation and perception. Also the relationship between customer’s satisfaction and each dimension of service quality (ie: reliability, tangibility, responsiveness, assurance and empathy) and ranking them accordingly, is investigated.