Demand Estimation under Incomplete Product Availability

Conlon, Christopher T.; Mortimer, Julie Holland

doi:10.2139/ssrn.1394476

Cited by 32 publications

(44 citation statements)

References 41 publications

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“…This phenomenon is referred to as demand censoring. As discussed in the introduction, a large number of papers in the literature document this important phenomenon and study its implications (Braden and Freimer 1991, Anupindi et al 1998, Downs et al 2001, Ding et al 2002, Chen and Plambeck 2008, Lu et al 2008, Besbes and Muharremoglu 2012, Conlon and Mortimer 2013, Dai and Jerath 2013, Rudi and Drake 2014, Dai and Jerath 2016, Chen et al 2017). Because of demand censoring, the firm has to use the sales quantity rather than the actual demand as the basis for determining the salesforce compensation plan.…”

Section: Modelmentioning

confidence: 99%

“…This is a widely observed phenomenon commonly referred to as demand censoring. In recognition of its real-world importance, a growing economics, marketing and operations literature has studied the managerial implications of demand censoring (Braden and Freimer 1991, Anupindi et al 1998, Downs et al 2001, Ding et al 2002, Chen and Plambeck 2008, Lu et al 2008, Besbes and Muharremoglu 2012, Conlon and Mortimer 2013, Dai and Jerath 2013, Rudi and Drake 2014, Dai and Jerath 2016, Chen et al 2017. Demand 1 There is a recent literature on robust contracts in which the principal evaluates possible contracts by their worstcase performance over unknown actions that the agent may take (Antić 2014, Carroll 2015, Carroll and Meng 2016, Yu and Kong 2017.…”

Section: Introductionmentioning

confidence: 99%

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Salesforce Contracting Under Uncertain Demand and Supply: Double Moral Hazard and Optimality of Smooth Contracts

Dai

Jerath

2019

Marketing Science

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We consider the compensation design problem of a firm that hires a salesperson to exert effort to increase demand. We assume both demand and supply to be uncertain, with sales being the smaller of demand and supply, and assume that if demand exceeds supply then unmet demand is unobservable (demand censoring).Under single moral hazard (i.e., when the salesperson's effort is unobservable to the firm), we show that the optimal contract has an extreme convex form in which a bonus is provided only for achieving the highest sales outcome, even if low realized sales are due to low realized supply (on which the salesperson has no influence).Under double moral hazard (i.e., when the firm can also take supply-related actions that are unobservable to the salesperson), we show that the optimal contract is smoother as it involves positive compensation for intermediate sales outcomes to assure the salesperson that the firm does not have an incentive to deviate to an action that will hurt the agent; in fact, under certain conditions, the contract is concave in sales. We also determine conditions under which, if possible, the firm should postpone contracting until after supply is realized.

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Section: Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Salesforce Contracting Under Uncertain Demand and Supply: Double Moral Hazard and Optimality of Smooth Contracts

Dai

Jerath

2019

Marketing Science

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“…In a nutshell, stock-outs allow us to exploit temporal changes in the consumer choice set and hence provide a useful source of variation for demand estimation, particularly in the context where not all choice decisions are observed. Conlon and Mortimer (2013) also used stock-outs as an identification strategy but with complete information on choice decisions. As we have shown in this section, this source of variation is even more critical with only partially observed choice decisions.…”

Section: Extent Of Price Comparisonmentioning

confidence: 99%

Competition-Based Dynamic Pricing in Online Retailing: A Methodology Validated with Field Experiments

2018

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A retailer following a competition-based dynamic pricing strategy tracks competitors' price changes and then must answer the following questions: (1) Should the retailer respond? (2) If so, respond to whom?(3) How much of a response? (4) And on which products? The answers require unbiased measures of price elasticity as well as accurate knowledge of competitor significance and the extent to which consumers compare prices across retailers. To quantify these factors empirically, there are two key challenges: first, the endogeneity associated with almost any type of observational data, where prices are correlated with demand shocks observable to pricing managers but not to researchers; and second, the absence of competitor sales information, which prevents efficient estimation of a full consumer-choice model. We address the first issue by conducting a field experiment with randomized prices. We resolve the second issue by proposing an identification strategy that exploits the retailer's own and competitors' stock-outs as a valid source of variation to the consumer choice set. We estimate an empirical model capturing consumer choices among substitutable products from multiple retailers. Based on the estimates, we propose a best-response pricing strategy that takes into account consumer choice behavior, competitors' actions, and supply parameters (procurement costs, margin target, and manufacturer price restrictions). We test our algorithm through a carefully controlled live experiment that lasts for five weeks. The experiment documents an 11 percent revenue increase, while maintaining margin above a retailer specified target.

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“…In the inventory data, we track daily inventory for every shoe. 3 Importantly, this data allows us to infer the complete set of shoes in the consumer's choice set, even when the sale of a particular shoe is not observed (Conlon and Mortimer, 2013). Although the inventory data is size-specific, the sales data does not include size.…”

Section: Data Summarymentioning

confidence: 99%

Product Variety, Across-Market Demand Heterogeneity, and the Value of Online Retail

Quan¹,

Williams

2017

SSRN Journal

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Online retail gives consumers access to an astonishing variety of products. However, the additional value created by this variety depends on the extent to which local retailers already satisfy local demand. To quantify the gains and account for local demand, we use detailed data from an online retailer and propose methodology to address a common issue in such data-sparsity of local sales due to sampling and a significant number of local zeros. Our estimates indicate products face substantial demand heterogeneity across markets; as a result, we find gains from online variety that are 45% lower than previous studies.

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Demand Estimation under Incomplete Product Availability

Cited by 32 publications

References 41 publications

Salesforce Contracting Under Uncertain Demand and Supply: Double Moral Hazard and Optimality of Smooth Contracts

Salesforce Contracting Under Uncertain Demand and Supply: Double Moral Hazard and Optimality of Smooth Contracts

Competition-Based Dynamic Pricing in Online Retailing: A Methodology Validated with Field Experiments

Product Variety, Across-Market Demand Heterogeneity, and the Value of Online Retail

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