Price Discounts and Personalized Product Assortment Under Multinomial Logit Choice Model PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Price Discounts and Personalized Product Assortment Under Multinomial Logit Choice Model PDF full book. Access full book title Price Discounts and Personalized Product Assortment Under Multinomial Logit Choice Model by Qingwei Jin. Download full books in PDF and EPUB format.
Author: Qingwei Jin
Publisher:
ISBN:
Category :
Languages : en
Pages : 54
Get Book Here
Book Description
With increasing availability of consumer data and rapid advancement and applications of technologies, online retailers are gaining better knowledge of shopping behavior and preferences of their customers. Thus more and more retailers are providing customized product assortment to better match the needs of customers and generate more sales. In this paper, we study a two-stage revenue management model where a retailer decides non-customized price discounts at stage one due to fairness consideration and customized product assortment at stage two (upon the arrival of customers) under the multinomial logit choice model. We employ a robust approach for the joint discounts and customized assortment optimization problem to handle data uncertainty for estimating customer preferences and distribution of different customer segments. We analyze the structural properties of the problems and propose efficient computational methods to solve the problems with/without cardinality constraint on the assortment. In certain cases, our algorithm converges at a superlinear rate. When there is a cardinality constraint on the assortment, we find the retailer should offer deeper discounts as the constraint becomes more restrictive. We also provide some discussion on the value of our robust solution and the extension when the customer discount sensitivity function is also uncertain. Finally, our extensive numerical study shows that the solutions under the robust approach perform very well compared to the one assuming accurate information and has robustness when there is uncertainty.
Author: Qingwei Jin
Publisher:
ISBN:
Category :
Languages : en
Pages : 54
Get Book Here
Book Description
With increasing availability of consumer data and rapid advancement and applications of technologies, online retailers are gaining better knowledge of shopping behavior and preferences of their customers. Thus more and more retailers are providing customized product assortment to better match the needs of customers and generate more sales. In this paper, we study a two-stage revenue management model where a retailer decides non-customized price discounts at stage one due to fairness consideration and customized product assortment at stage two (upon the arrival of customers) under the multinomial logit choice model. We employ a robust approach for the joint discounts and customized assortment optimization problem to handle data uncertainty for estimating customer preferences and distribution of different customer segments. We analyze the structural properties of the problems and propose efficient computational methods to solve the problems with/without cardinality constraint on the assortment. In certain cases, our algorithm converges at a superlinear rate. When there is a cardinality constraint on the assortment, we find the retailer should offer deeper discounts as the constraint becomes more restrictive. We also provide some discussion on the value of our robust solution and the extension when the customer discount sensitivity function is also uncertain. Finally, our extensive numerical study shows that the solutions under the robust approach perform very well compared to the one assuming accurate information and has robustness when there is uncertainty.
Author: Omar Besbes
Publisher:
ISBN:
Category :
Languages : en
Pages : 36
Get Book Here
Book Description
The role of assortment planning and pricing in shaping sales and profits of retailers is well documented and studied in monopolistic settings. However, such a role remains relatively unexplored in competitive environments. In this paper, we study equilibrium behavior of competing retailers in two settings: i.) when prices are exogenously fixed, and retailers compete in assortments only; and ii.) when retailers compete jointly in assortment and prices. For this, we model consumer choice using a multinomial Logit, and assume that each retailer selects products from a predefined set, and faces a display constraint. We show that when the sets of products available to retailers do not overlap, there always exists one equilibrium that pareto-dominates all others, and that such an outcome can be reached through an iterative process of best responses. A direct corollary of our results is that competition leads a firm to offer a broader set of products compared to when it is operating as a monopolist, and to broader offerings in the market compared to a centralized planner. When some products are available to all retailers, i.e., assortments might overlap, we show that display constraints drive equilibrium existence properties.
Author: Sari Rida Kalakesh
Publisher:
ISBN:
Category :
Languages : en
Pages : 76
Get Book Here
Book Description
Retail represents the second largest industry in the United States with nearly $ 4 trillion in annual revenues and a large capital tied-up in retail inventories. However, the Marketing and Economics literature typically study pricing and var iety (assortment) decisions while ignoring inventory costs. This project incorpo rates the important effects of inventory on pricing and variety decisions for a retailer's product line composed of substitutable items. The retailer's custome rs make their buying decisions for items in the product line based on a random u tility function. A popular model for consumer utility (choice) in this situation is the Multinomial Logit Choice (MNL) model. However, MNL suffers from the inde pendence from irrelevant alternative (IIA) property, which basically implies tha t items in the product line are broadly similar. A remedy to this limitation is to adopt a Nested Multinomial Logit (NMNL) choice model, an enhancement of the M NL, which has received little attention in the retail literature. In this project, we study pricing, variety and inventory decisions under NMNL co nsumer choice and within a single-period newsvendor-type inventory setting. We d erive a close-form expression for the retailers expected profit function of pric es, assortment, and inventory levels. We then numerically analyze the structure of a retailer's optimal assortment, prices and inventory levels. To enhance our understanding of the problem, we develop models of different flavors involving d eciding on two out of three basic pricing, variety and inventory decisions befor e considering all three decisions jointly. Specifically, we start by analyzing p ricing and variety decisions while assuming ample supply (i.e., infinite invento ry levels). We then study a second situation where prices are exogenously fixed and the retailer decides on variety and inventory levels. We finally study a thi rd, and perhaps most important, situation where the retailer jointly decides on pricing, variety and inventory. We observe that the optimal prices, assortment and inventory levels have simple structure in all three situations. This sugges ts that "good" practical solutions for the complex problem of joint pricing, var iety and inventory decisions within the realistic setting we consider could be o btained with ease.
Author: Chenhao Du
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
We consider a seller's problem of determining revenue-maximizing prices for an assortment of products that exhibit network effects. Customers make purchase decisions according to a multinomial logit (MNL) customer choice model, modified -- to incorporate network effects -- so that the utility each individual customer gains from purchasing a particular product depends on the market's total consumption of that product. In the setting of homogeneous products, we show that if the network effect is comparatively weak, then the optimal pricing decision of the seller is to set identical prices for all products. However, if the network effect is strong, then the optimal pricing decision is to set the price of one product low, and to set the prices of all other products to a single high value. This boosts the sales of the single low-price product in comparison to the sales of all other products. These results can be compared to the optimal pricing policy for the classical MNL model (without network effects) in which it is optimal to set identical prices and obtain identical sales quantities for homogeneous products. We obtain comparative statics results that describe how optimal prices and sales levels vary with a parameter that determines the strength of the network effects. We extend our analysis of settings with heterogeneous products as well as to settings with inter-product network effects, and we describe efficient computational methods.
Author: Ruxian Wang
Publisher:
ISBN:
Category :
Languages : en
Pages : 7
Get Book Here
Book Description
We consider an assortment and price optimization problem where a retailer chooses an assortment of competing products and determines their prices to maximize the total expected profit subject to a capacity constraint. Customers' purchase behavior follows the multinomial logit choice model with general utility functions. This paper simplifies it to a problem of finding a unique fixed point of a single-dimensional function and visualizes the assortment optimization process. An efficient algorithm to find the optimal assortment and prices is provided.
Author: Yuhang Ma
Publisher:
ISBN:
Category :
Languages : en
Pages : 163
Get Book Here
Book Description
In most E-commerce scenarios such as hotel booking and online shopping, products are not offered to customers simultaneously. Instead, they are divided into different webpages and presented to customers sequentially. In this thesis, we focus on solving a common problem faced by online retailers: when products are revealed to customers sequentially, which products should the retailers display at each stage and what prices should the retailers charge for each product so that the expected revenue can be maximized? To solve those problems, we generalize the classical multinomial logit model to capture the customer's choice behavior under the sequential setting and present efficient algorithms for different generalized choice models and different operational constraints.
Author: Aydin Alptekinoglu
Publisher:
ISBN:
Category :
Languages : en
Pages : 50
Get Book Here
Book Description
We investigate the use of a canonical version of a discrete choice model due to Daganzo (1979) in optimal pricing and assortment planning. In contrast to multinomial and nested logit (the prevailing choice models used for optimizing prices and assortments), this model assumes a negatively skewed distribution of consumer utilities, an assumption we motivate by conceptual arguments as well as published work. The choice probabilities in this model can be derived in closed-form as an exponomial (a linear function of exponential terms). The pricing and assortment planning insights we obtain from the Exponomial Choice (EC) model differ from the literature in two important ways. First, the EC model allows variable markups in optimal prices that increase with expected utilities. Second, when prices are exogenous, the optimal assortment may exhibit leapfrogging in prices, i.e., a product can be skipped in favor of a lower-priced one depending on the utility positions of neighboring products. These two plausible pricing and assortment patterns are ruled out by multinomial logit (and by nested logit within each nest). We provide structural results on optimal pricing for monopoly and oligopoly cases, and on the optimal assortments for both exogenous and endogenous prices. We also demonstrate how the EC model can be easily estimated--by establishing that the loglikelihood function is concave in model parameters and detailing an estimation example using real data.
Author: Wang Chi Cheung
Publisher:
ISBN:
Category :
Languages : en
Pages : 37
Get Book Here
Book Description
Motivated by online retail applications, we study the online personalized assortment optimization problem. A seller conducts sales by offering assortments of products to a stream of arriving customers. The customers' purchase behavior follows their respective personalized Multinomial Logit choice models, which vary according to their individual attributes. The seller aims to maximize his revenue by offering personalized assortments to the customers, notwithstanding his uncertainty about the customers' choice models. We propose a Thompson Sampling based policy, policy Pao-Ts, where surrogate models for the latent choice models are constructed using samples from a progressively updated posterior distribution. We derive bounds on the revenue loss, namely Bayesian regret, incurred by policy Pao-Ts, in comparison to the optimal policy which is provided with the latent models. The regret bounds hold even when the customers' attributes vary arbitrarily, but not independently and identically distributed.
Author: Mohan Gopalakrishnan
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
Motivated by direct interactions with practitioners and real-world data, we study a monopoly firm selling multiple substitute products to customers characterized by their different social network degrees. Under the multinomial logit model framework, we assume that the utility a customer with a larger network degree derives from the seller's products is subject to more impact from her neighbors and describe the customers' choice behavior by a Bayesian Nash game. We show that a unique equilibrium exists as long as these network effects are not too large. Furthermore, we study how the seller should optimally set the prices of the products in this setting. Under the homogeneous product-related parameter assumption, we show that if the seller optimally price-discriminates all customers based on their network degrees, the products' markups are the same for each customer type. Building on this, we characterize the sufficient and necessary condition for the concavity of the pricing problem, and show that when the problem is not concave, we can convert it to a single-dimensional search and solve it efficiently. We provide several further insights about the structure of optimal prices, both theoretically and numerically. Furthermore, we show that we can simultaneously relax the multinomial logit model and homogeneous product-related parameter assumptions and allow customer in- and out-degrees to be arbitrarily distributed whilemaintaining most of our conclusions robust.
Author: Yicheng Bai
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
Motivated by empirical evidence that the utility of each product depends on the assortment of products offered along with it, we propose an endogenous context-dependent multinomial logit model (Context-MNL) under which the utility of each product depends on both the product's intrinsic value and the deviation of the intrinsic value from the expected maximum utility among all the products in the offered assortment. Under the Context-MNL model, an assortment provides a context in which customers evaluate the utility of each product. Our model generalizes the standard multinomial logit model and allows the utility of each product to depend on the offered assortment. The model is parsimonious, requires only one parameter more than the standard multinomial logit model, captures the assortment-dependent effect endogenously, and does~not require the decision-maker to determine in advance the relevant attributes of the assortment that might affect the product utility. The Context-MNL model also admits tractable maximum likelihood estimation and is operationally tractable, with efficient solution methods for solving assortment and price optimization problems. Our numerical study, which is based on data from Expedia, shows that compared to the standard multinomial logit model, the Context-MNL model substantially improves out-of-sample goodness of fit and prediction accuracy.
