Dr. Ofer Mintz is Associate Head (External Engagement) of the Marketing Department and a Senior Lecturer in Marketing at the University of Technology Sydney (UTS). His research focuses on marketing strategy/analytics, digital marketing, the role of marketing in startups, and firm and marketing strategies during the COVID-19 era, which he investigates by combining econometric techniques and theories from across business school disciplines.
Ofer has given talks on 5 continents, his research has been published (or forthcoming) in leading marketing journals such as the Journal of Marketing, Marketing Science, Journal of International Business Studies, Harvard Business School Working Knowledge Series, and International Journal of Research in Marketing, and he has been awarded grants from several academic-practitioner organizations. His research on marketing metric use by managers in 16 different countries was as a finalist for the Buzzell Best Paper Award from the Marketing Science Institute's for its impact to marketing practice and thought over the last two years.
In addition, Ofer was a delegate on an Australian Trade Mission on Innovation to the USA, led by Robyn Denholm and Maureen Dougherty, Tesla Chairwoman and Boeing APAC President, respectively, that led to an opportunity to visit and talk with executives from leading tech firms in the Silicon Valley and Seattle regions. Currently, Ofer is quite passionate about helping firms employ customer centric growth strategies and principals to navigate through the COVID-19 new normal, and is writing a book on the topic.
Ofer is also quite passionate on teaching his digital marketing subject (with the subject winning awards in the past), trying to get students quality jobs in this area or up-skill their current ones, and keeping contact and relationships with former students.
Can supervise: YES
Digital Marketing, Marketing Stategy, Marketing Analytics, Applied Marketing Projects
Mintz, O, Gilbride, T, Lenk, P & Currim, I 2021, 'The Right Metrics for Marketing-Mix Decisions', International Journal of Research in Marketing.
This study addresses the following question: For a given managerial, firm, and industry setting, which individual metrics are effective for making marketing-mix decisions that improve perceived performance outcomes? We articulate the key managerial takeaways based on testing a multi-stage behavioral framework that links decision context, metrics selection, and performance outcomes. Our statistical model adjusts for potential endogeneity bias in estimating metric effectiveness due to selection effects and differs from past literature in that managers can strategically choose metrics based on their ex-ante expected effectiveness. The key findings of our analysis of 439 managers making 1,287 decisions are that customer-mindset marketing metrics such as awareness and willingness to recommend are the most effective metrics for managers to employ while financial metrics such as target volume and net present value are the least effective. However, relative to financial metrics, managers are more uncertain about the ex-ante effectiveness of customer-mindset marketing metrics, which attenuates their use. A second study on 142 managers helps provide detailed underlying rationale for these key results. The implications of metric effectiveness for dashboards and automated decision systems based on machine learning systems are discussed.
Deshpandé, R, Mintz, O & Currim, I 2020, 'Your Customers Have Changed. Here's How to Engage Them Again', Harvard Business School Working Knowledge Series.
Mintz, O, Currim, IS, Steenkamp, J-BEM & de Jong, M 2020, 'Managerial metric use in marketing decisions across 16 countries: A cultural perspective', Journal of International Business Studies.View/Download from: Publisher's site
Mintz, O, Bart, Y, Lenk, P & Reibstein, D 2019, 'Drowning in Metrics: How Managers Select and Trade-Off Metrics for Making Marketing Budgetary Decisions'.
Choudhary, V, Currim, I, Dewan, S, Jeliazkov, I, Mintz, O & Turner, J 2017, 'Evaluation Set Size and Purchase: Evidence from a Product Search Engine', JOURNAL OF INTERACTIVE MARKETING, vol. 37, pp. 16-31.View/Download from: Publisher's site
Gilbride, TJ, Currim, IS, Mintz, O & Siddarth, S 2016, 'A Model for Inferring Market Preferences from Online Retail Product Information Matrices', Journal of Retailing, vol. 92, no. 4, pp. 470-485.View/Download from: Publisher's site
© 2016 New York University This research extends information display board methods, currently employed to study information processing patterns in laboratory settings, to a field based setting that also yields managerially useful estimates of market preferences. A new model is proposed based on statistical, behavioral, and economic theories, which integrates three decisions consumers must make in this context: which product-attribute to inspect next, when to stop processing, and which, if any, product to purchase. Several theoretical options are considered on how to model product attribute selection and how to treat uninspected attributes. The modeling options are empirically tested employing datasets collected at a popular e-tailer's website, while customers were making product evaluation and purchase decisions. Subsequent to identifying the best model, we show how the resulting attribute preference estimates can be managerially employed to improve customer targeting of abandoned shopping carts for follow up communications aimed at improving sales conversions.
Currim, IS, Mintz, O & Siddarth, S 2015, 'Information Accessed or Information Available? The Impact on Consumer Preferences Inferred at a Durable Product E-commerce Website', Journal of Interactive Marketing, vol. 29, no. C, pp. 11-25.View/Download from: Publisher's site
© 2014 Direct Marketing Educational Foundation, Inc., dba Marketing EDGE. Most previous choice modeling research infers preferences by assuming that consumers consider all the information available at the point-of-purchase. Because e-commerce sites increasingly incorporate tracking technologies that can monitor consumer behavior on their site, our research studies how incorporating the information accessed by consumers into a choice model impacts model performance and inferred preferences. We use data from an electronic goods manufacturer that monitored the attribute information accessed by 582 shoppers while they made Customize and Buy decisions at the firm's website. We find that incorporating the information accessed by consumers into the choice model provides more valid estimates of attribute preferences and better fitting choice models than models based on information available. Because firms can easily obtain this type of information as a by-product of their online operations, we propose that managers who monitor information acquisition and apply the information accessed model will have a useful methodology to gain a better understanding of consumer preferences.
Mintz, O & Currim, IS 2015, 'When does metric use matter less?: How firm and managerial characteristics moderate the relationship between metric use and marketing mix performance', European Journal of Marketing, vol. 49, no. 11-12, pp. 1809-1856.View/Download from: Publisher's site
© 2015, Emerald Group Publishing Limited. Purpose – This paper aims to develop a conceptual framework, in an effort toward building a contingent theory of drivers and consequences of managerial metric use in marketing mix decisions, this paper develops a conceptual framework to test whether the relationship between metric use and marketing mix performance is moderated by firm and managerial characteristics. Design/methodology/approach – Based on reviews of the marketing, finance, management and accounting literatures, and homophily, firm resource- and decision-maker-based theories and 22 managerial interviews, a conceptual model is proposed. It is tested via generalized least squares – seemingly unrelated regression estimation of 1,287 managerial decisions. Findings – Results suggest that the impact of metric use on marketing mix performance is lower in firms which are more market oriented, larger and with worse recent business performance and for marketing and higher-level managers, while organizational involvement has a lesser nuanced effect. Research limitations/implications – While much is written on the importance of metric use to improve performance, this work is a first step toward understanding which settings are more difficult than others to accomplish this. Practical implications – Results allow identification of several conditional managerial strategies to improve marketing mix performance based on metric use. Originality/value – This paper contributes to the metric literature, as prior research has generally focused on the development of metrics or the linking of marketing efforts with performance metrics, but paid little attention to understanding the relationship between managerial metric use and performance of the marketing mix decision and has not considered how the relationship is moderated by firm and managerial characteristics.
Mintz, O & Currim, IS 2013, 'What Drives Managerial Use of Marketing and Financial Metrics and Does Metric Use Affect Performance of Marketing-Mix Activities?', JOURNAL OF MARKETING, vol. 77, no. 2, pp. 17-40.View/Download from: Publisher's site
Mintz, O, Currim, IS & Jeliazkov, I 2013, 'Information processing pattern and propensity to buy: An investigation of online point-of-purchase behavior', Marketing Science, vol. 32, no. 5, pp. 716-732.View/Download from: Publisher's site
The information processing literature provides a wealth of laboratory evidence on the effects that the choice task and individual characteristics have on the extent to which consumers engage in alternative-based versus attribute-based information processing. Less attention has been paid to studying how the processing pattern at the point of purchase is associated with a consumer's propensity to buy in shopping settings. To understand this relationship, we formulate a discrete choice model and perform formal model comparisons to distinguish among several possible dependence structures. We consider models involving an existing measure of information processing, PATTERN; a latent variable version of this measure; and several new refinements and generalizations. Analysis of a unique data set of 895 shoppers on a popular electronics website supports the latent variable specification and provides validation for several hypotheses and modeling components. We find a positive relationship between alternative-based processing and purchase, as well as a tendency of shoppers in the lower price category to engage in alternative-based processing. The results also support the case for joint modeling and estimation. These findings can be useful for future work in information processing and suggest that likely buyers can be identified while engaged in information processing prior to purchase commitment, an important first step in targeting decisions. © 2013 INFORMS.
Mintz, O, Lenk, P & Wang, Y Marketing Science Institute 2020, Start-up Funding Decisions in the Eyes of Investors and Entrepreneurs: Effects of Co-Founders' Functional Background, Marketing Science Institute Working Paper Series.
Mintz, O 2020, 'Australia re-opens: Seven ways to increase the likelihood customers will return to your store', Smart Company.
Mintz, O, Gilbride, T, Lenk, P & Currim, I 2019, 'Right Metric for the Right Decision: A Behavioral Model to Infer Metric Effectiveness in Managerial Marketing-Mix Decision-Making', Marketing Science Institute (MSI) Working Paper Series..