Customer Behavior Modeling in Revenue Management and Auctions: A Review and New Research Opportunities

Shen, Zuo‐Jun Max; Su, Xuanming

doi:10.1111/j.1937-5956.2007.tb00291.x

Cited by 309 publications

(148 citation statements)

References 107 publications

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“…In line with the recent interest in modeling consumer behavior (e.g., Shen and Su 2007;Netessine and Tang 2009), there are several papers that study how comparative effects between consumers can drive operational practices. Tereyagoglu and Veeraraghavan (2012) analyze production decisions when consumers engaging in conspicuous consumption value exclusivity (i.e., when others are excluded from consumption), while Veeraraghavan and Debo (2011) study the queueing implications of herding behavior (i.e., when others also join in consumption).…”

Section: Figurementioning

confidence: 98%

Optimal Design of Social Comparison Effects: Setting Reference Groups and Reference Points

Roels

2014

Management Science

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107

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In this paper, we study how social planners should exploit social comparisons to pursue their objectives. We consider two modes of social comparison, referred to as behind-averse and ahead-seeking behaviors, depending on whether individuals experience a utility loss from underperforming or a utility gain from overperforming relative to their peers. Modeling social comparison as a game between players, we find that ahead-seeking behavior leads to output polarization, whereas behind-averse behavior leads to output clustering. A social planner can mitigate these effects in two ways: (i) by providing the full reference distribution of outputs instead of an aggregate reference point based on the average output and (ii) by assigning players into uniform rather than diverse reference groups. Social planners may thus need to tailor the reference structure to the predominant mode of social comparison and their objective. A performance-focused social planner may set the reference structure so as to maximize the output of either the top or the bottom player depending on whether she puts greater marginal weight to larger or smaller outputs. When the social planner also cares about utility, she faces a dilemma because performance optimization may not be aligned with utility maximization. Inevitably, the social planner will have to confront equity issues because better performance may not reflect greater effort or greater ability. Xuanming SuThe Wharton School, University of Pennsylvania, 3730 Walnut Street, Philadelphia, PA 19104, USA, xuanming@wharton.upenn.edu In this paper, we study how social planners should exploit social comparisons to pursue their objectives.We consider two modes of social comparison, referred to as behind-averse and ahead-seeking behaviors, depending on whether individuals experience a utility loss from under-performing or a utility gain from overperforming relative to their peers. Modeling social comparison as a game between players, we find that aheadseeking behavior leads to output polarization whereas behind-averse behavior leads to output clustering.A social planner can mitigate these effects in two ways, (i) by providing the full reference distribution of outputs instead of an aggregate reference point based on the average output, and (ii) by assigning players into uniform rather than diverse reference groups. Social planners may thus need to tailor the reference structure to the predominant mode of social comparison and their objective. A performance-focused social planner may set the reference structure so as to maximize the output of either the top or the bottom player depending on whether she puts greater marginal weight to larger or smaller outputs. When the social planner also cares about utility, she faces a dilemma because performance-optimization may not be aligned with utility-maximization. Inevitably, the social planner will have to confront equity issues because better performance may not reflect greater effort or greater ability.

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Section: Figurementioning

confidence: 98%

Optimal Design of Social Comparison Effects: Setting Reference Groups and Reference Points

Roels

2014

Management Science

Self Cite

107

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“…(14) and (15) constraints provide the demand of each product are positive. (16) provides that the sales of the product for each week cannot be greater than the demand of that period. (17)(18)(19) constraints are related to stock constraints.…”

Section: Mathematical Modelmentioning

confidence: 99%

“…Therefore consumer behaviors are important to analyse the substitution effects and they should be considered in the model. Customer behavior modeling has been gaining increasing attention in the operations management community 16 . We use Multinomial Logit Model which is one of the discrete choice models and used mostly in marketing literatures to estimate the substitute demand.…”

Section: Introductionmentioning

confidence: 99%

Markdown Optimization via Approximate Dynamic Programming

Coşgun¹,

Kula²,

Kahraman³

2013

IJCIS

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We consider the markdown optimization problem faced by the leading apparel retail chain. Because of substitution among products the markdown policy of one product affects the sales of other products. Therefore, markdown policies for product groups having a significant crossprice elasticity among each other should be jointly determined. Since the state space of the problem is very huge, we use Approximate Dynamic Programming. Finally, we provide insights on the behavior of how each product price affects the markdown policy.

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“…Secondly, it is from how to reduce strategic customer waiting behavior. Shen & Su (2007) pointed out that retailers need to introduce new inventory and price mechanism to reduce strategic customer waiting behavior [5] . Thirdly, it is from the symmetry and asymmetry information.…”

Section: Introductionmentioning

confidence: 99%

Research on Retailer's Financial and Operation Decisions Based on Strategic Customer

Liu¹

2012

Proceedings of 2012 National Conference on Information Technology and Computer Science

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Abstract-This paper studies retailer's decision and financial institution's decision based on the strategic customer when retailer accepts financial service from financial institutions. Research shows that financing rate increases with the wholesale price and the average rate of return on investment increasing and decreases with the retailer's own funds increasing, and retailer's ordering quantity with finance service is higher than that of without finance service, and retailer and financial institution both get more the expected profit, and strategic customers get more benefits. Finally, a numerical example is validated.

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Customer Behavior Modeling in Revenue Management and Auctions: A Review and New Research Opportunities

Abstract: Customer behavior modeling has been gaining increasing attention in the operations management community. In this paper we review current models of customer behavior in the revenue management and auction literatures and suggest several interesting research directions in this area.

Cited by 309 publications

References 107 publications

Optimal Design of Social Comparison Effects: Setting Reference Groups and Reference Points

Optimal Design of Social Comparison Effects: Setting Reference Groups and Reference Points

Markdown Optimization via Approximate Dynamic Programming

Research on Retailer's Financial and Operation Decisions Based on Strategic Customer

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