The Impact of Consumer Search Cost on Assortment Planning and Pricing

Wang, Ruxian; Şahin, Özge

doi:10.1287/mnsc.2017.2790

Cited by 105 publications

(50 citation statements)

References 33 publications

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“…This heuristic is optimal in the case of the MNL, even with a capacity constraint. Wang and Sahin (2018) has studied the assortment optimization in a context in which consumer search costs are non-negligible. The authors showed that the strategy of revenue-ordered assortments is not optimal.…”

Section: Relevant Literaturementioning

confidence: 99%

Assortment optimization under the Sequential Multinomial Logit Model

Flores

Berbeglia

Hentenryck

2019

European Journal of Operational Research

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We study the assortment optimization problem under the Sequential Multinomial Logit (SML), a discrete choice model that generalizes the multinomial logit (MNL). Under the SML model, products are partitioned into two levels, to capture differences in attractiveness, brand awareness and, or visibility of the products in the market. When a consumer is presented with an assortment of products, she first considers products in the first level and, if none of them is purchased, products in the second level are considered. This model is a special case of the Perception-Adjusted Luce Model (PALM) recently proposed by Echenique, Saito, and Tserenjigmid (2018). It can explain many behavioral phenomena such as the attraction, compromise, similarity effects and choice overload which cannot be explained by the MNL model or any discrete choice model based on random utility. In particular, the SML model allows violations to regularity which states that the probability of choosing a product cannot increase if the offer set is enlarged.This paper shows that the seminal concept of revenue-ordered assortment sets, which contain an optimal assortment under the MNL model, can be generalized to the SML model. More precisely, the paper proves that all optimal assortments under the SML are revenue-ordered by level, a natural generalization of revenue-ordered assortments that contains, at most, a quadratic number of assortments. As a corollary, assortment optimization under the SML is polynomialtime solvable. This result is particularly interesting given that the SML model does not satisfy the regularity condition and, therefore, it can explain choice behaviours that cannot be explained by any choice model based on random utility.

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Section: Relevant Literaturementioning

confidence: 99%

Assortment optimization under the Sequential Multinomial Logit Model

Flores

Berbeglia

Hentenryck

2019

European Journal of Operational Research

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“…An assumption made in our paper is that the number of stages a customer is willing to interact is known when she arrives; this is also assumed in the papers above (and in some cases, assumed to always equal two). Other models have been proposed when a single customer views a variable number of pages; see (Gallego et al 2018), and also (Wang and Sahin 2017) who models the "search cost" paid by the customer to view more pages.…”

Section: Comparison Of Model To Related Literaturementioning

confidence: 99%

Multi-Stage and Multi-Customer Assortment Optimization With Inventory Constraints

Fata

Simchi‐Levi

2019

SSRN Journal

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We consider an assortment optimization problem where a customer chooses a single item from a sequence of sets shown to her, while limited inventories constrain the items offered to customers over time. In the special case where all of the assortments have size one, our problem captures the online stochastic matching with timeouts problem. For this problem, we derive a polynomial-time approximation algorithm which earns at least 1-ln(2-1/e), or 0.51, of the optimum. This improves upon the previous-best approximation ratio of 0.46, and furthermore, we show that it is tight. For the general assortment problem, we establish the first constant-factor approximation ratio of 0.09 for the case that different types of customers value items differently, and an approximation ratio of 0.15 for the case that different customers value each item the same. Our algorithms are based on rounding an LP relaxation for multi-stage assortment optimization, and improve upon previous randomized rounding schemes to derive the tight ratio of 1-ln(2-1/e).

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“…Therefore, the emphasis of our work is quite different from these papers. Wang and Sahin (2018) consider an assortment optimization problem under a consider-then-choose model. The consideration set in the consider-thenchoose model is formed by customers deciding to research a subset of the products to ascertain the valuations of those products.…”

Section: Literature Reviewmentioning

confidence: 99%

Information Provision and Pricing in the Presence of Consumer Search Costs

Liu

Cooper

Wang

2019

Production and Operations Management

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Should a seller make information about its products readily accessible to customers, so that customers do not have to incur any substantive cost—in terms of time and effort—to learn about those products? To help answer this question, we consider a monopolist selling two substitute products to a population of customers, who have differing tastes about the products. Each customer a priori has uncertainty about whether or not he will like each of the products. The seller may choose to make product information easily accessible, thereby allowing customers to resolve their uncertainties for free. Otherwise, customers may conduct research to resolve their uncertainties by incurring a search cost before making purchase decisions. We consider three “information structures” differing in whether the seller makes information about the products freely accessible or not. Our primary objective is to determine which structure gives the seller the highest revenue, while accounting for the seller’s pricing decisions as well as the induced customer responses to both the information structure and prices. We find that if each customer’s uncertainties are small in magnitude but highly positively correlated, then withholding both products’ information is the best for the seller. If the uncertainties are small in magnitude and negatively correlated, then providing one product’s information but not the other’s is the best. If the uncertainties are large in magnitude and positively correlated, then providing both products’ information is the best. We also show that when the correlation is negative, withholding both products’ information cannot be optimal. In addition, we also analyze various extensions of the model. These include a variant in which customers’ research is imperfect and may yield incorrect information to the customers, and a variant in which each customer’s uncertainty about a product can be decomposed into multiple uncertainties associated with individual attributes of the product.

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The Impact of Consumer Search Cost on Assortment Planning and Pricing

Cited by 105 publications

References 33 publications

Assortment optimization under the Sequential Multinomial Logit Model

Assortment optimization under the Sequential Multinomial Logit Model

Multi-Stage and Multi-Customer Assortment Optimization With Inventory Constraints

Information Provision and Pricing in the Presence of Consumer Search Costs

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