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Valeria Stourm and Xi (Alan) Zhang win the 2016 Don Lehmann Award.

The Don Lehmann award is given for the best dissertation based paper published in the past two years in Journal of Marketing or Journal of Marketing Research. A committee comprised of past award winners, current and past JM/JMR editors selected the winner. There were several high quality nominations this year. The committee selected 2 winners Valeria Stourm and Xi Zhang as winners of the Don Lehmann Award in 2016.

The dissertation based paper of Valeria Stourm co-authored with her dissertation chairs, Eric Bradlow and Peter Fader, titled, “Stockpiling Points in Linear Loyalty Programs,” was published in the Journal of Marketing Research in 2015. This paper is based on an interesting but previously undocumented empirical observation that people earn points in linear loyalty programs but chose not to redeem them as frequently as possible. According to the nomination of this paper, “…it is rare to see a paper that examines a novel real-world phenomenon (with significant dollars at stake), lays out competing economic and psychological theories to explain it, uses state-of-the art Bayesian computation to sort out these , and offers meaningful managerial implications that can have a substantial impact on practice. In other words, these are exactly the kinds of contributions that the Don Lehmann Award seeks to acknowledge.”

The dissertation based paper of Xi (Alan) Zhang coauthored with his dissertation chair, V Kumar, and another faculty, Anita Luo, titled, “Modeling Customer Opt-In and Opt-Out in a Permission-Based Marketing Context” was published in the Journal of Marketing Research in 2014. This study shows6. the monetary value of staying opted in to a marketing program for each time period. According to the nominator of this paper, "This paper makes numerous contributions to marketing research. First, it introduces to marketing a multivariate copula model using a pair-copula construction method to jointly model three decisions: opt-in time (from a customer’s first purchase to opt in), opt-out time (from customer opt-in to opt-out) and average transaction amount. Second, it demonstrates the significant improvement in predicting the opt-out time in comparison with several benchmark models through the use of multivariate dependences. Third, it shows the usefulness of applying the variables of marketing intensity and customer characteristics to explain customer opt-in and opt-out decisions. Finally, the study simulates the possibility to extend customer opt-out time and increase customer spending level by strategically allocating resources.”