The Dick Wittink Prize for the best paper published in the QME was established to honor the memory of professor Dick Wittink, George Rogers Clark Professor of Management and Marketing at the Yale School of Management, who died in June 2005. He was a member of QME’s advisory board.
Wittink was a true academic—curious and ready to embrace new ideas and methods—making significant contributions to marketing research and marketing practice. He played an important role in applying econometric methods to marketing problems, such as measuring the impact of advertising, sales promotions, and completion. Wittink also pushed the boundaries of methods like conjoint analysis. He was known for his fair mindedness and ability to look beyond the superficial to evaluate research based on its true merit. Wittink was a mentor and guide to many doctoral students and junior faculty members who benefited tremendously from his input and support.
The Dick Wittink prize is awarded annually to the best paper published in the preceding volume of the QME.
2020 Dick Wittink Prize
The Dick Wittink Prize Committee is pleased to announce the 2020 winners of the 14th Annual Dick Wittink prize for the best paper published in the Quantitative Marketing and Economics journal.
Watch the recording of the 2020 Wittink Prize announcement
Dynamic effects of price promotions: field evidence, consumer search, and supply-side implications
by Andrés Elberg, Pedro M. Gardete, Rosario Macera and Carlos Noton
This paper investigates the dynamic effects of price promotions in a retail setting through the use of a large-scale field experiment varying the promotion depths of 170 products across 17 categories in 10 supermarkets of a major retailer in Chile. In the intervention phase of the experiment, treated customers were exposed to deep discounts (approximately 30%), whereas control customers were exposed to shallow discounts (approximately 10%). In the subsequent measurement phase, the promotion schedule held discount levels constant across groups. We find that treated customers were 22.4% more likely to buy promoted items than their control counterparts, despite facing the same promotional deals. Strikingly, the magnitude of the dynamic effects of price promotions (when promotional depths are equal across conditions) is 61% of the promotional effects induced by offering shallow vs. deep discounts during the intervention phase. The result is robust to other concurrent dynamic forces, including consumer stockpiling behavior and state dependence. We use the experimental variation and historical promotional activities to inform a demand-side model in which consumers search for deals, and a supply-side model in which firms compete for those consumers. We find that small manufacturers can benefit from heightened promotion sensitivity by using promotions to induce future consideration. However, when unit margins are high, heightened promotion sensitivity leads to fierce competition, making all firms worse off.
A study into mechanisms of attitudinal scale conversion: A randomized stochastic ordering approach
by Zvi Gilula, Robert E. McCulloch, Yaacov Ritov & Oleg Urminsky
This paper considers the methodological challenge of how to convert categorical attitudinal scores (like satisfaction) measured on one scale to a categorical attitudinal score measured on another scale with a different range. This is becoming a growing issue in marketing consulting and the common available solutions seem too few and too superficial. A new methodology for scale conversion is proposed, and tested in a comprehensive study. This methodology is shown to be both relevant and optimal in fundamental aspects. The new methodology is based on a novel algorithm named minimum conditional entropy, that uses the marginal distributions of the responses on each of the two scales to produce a unique joint bivariate distribution. In this joint distribution, the conditional distributions follow a stochastic order that is monotone in the categories and has the relevant optimal property of maximizing the correlation between the two underlying marginal scales. We show how such a joint distribution can be used to build a mechanism for scale conversion. We use both a frequentist and a Bayesian approach to derive mixture models for conversion mechanisms, and discuss some inferential aspects associated with the underlying models. These models can incorporate background variables of the respondents. A unique observational experiment is conducted that empirically validates the proposed modeling approach. Strong evidence of validation is obtained.
Firms’ reactions to public information on business practices: The case of search advertising
by Justin M. Rao and Andrey Simonov
We use five years of bidding data to examine the reaction of advertisers to widely disseminated press on the lack of effectiveness of brand search advertising (queries that contain the firm’s name) found in a large experiment run by eBay (Blake et al. 2015). We estimate that 11% of firms that did not face competing ads on their brand name keywords, matching the case of eBay, discontinued the practice of brand search advertising. In contrast, firms did not react to the information pertaining to the high value and ease of running experiments—we observe no change in the experiment-like variation in advertising levels. Further, while 72% of firms had sharp changes in advertising suitable for estimating causal effects, we find no correlation between firm-level advertising effects and the propensity to advertise in the future. We discuss how a principal-agent problem within the firm would lead to these learning dynamics.