The Brand Choice Model of Wine Consumers: A Multinomial Logit Model

Recent developments of information technology have increased market’s competitive pressure and products’ prices turned to be paramount factor for customers’ choices. These challenges influence traditional revenue management models and force them to shift from quantity-based to price-based techniques and incorporate individuals’ decisions within optimization models during pricing process. Multinomial logit model is the simplest and most popular discrete choice model, which suffers from an independence of irrelevant alternatives limitation. Empirical results demonstrate inadequacy of this model for capturing choice probability in the itinerary share models. The nested logit model, which appeared a few years after the multinomial logit, incorporates more realistic substitution pattern by relaxing this limitation. In this paper, a model of game theory is developed for two firms which customers choose according to the nested logit model. It is assumed that the real-time inventory levels of all firms are public information and the existence of Nash equilibrium is demonstrated. The firms adapt their prices by market conditions in this competition. The numerical experiments indicate decreasing firm’s price level simultaneously with increasing correlation among alternatives’ utilities error terms in the nests.

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A Mode Choice Model for a Public Transport Transfer Center in Istanbul

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.97-98.606 ◽

2011 ◽

Vol 97-98 ◽

pp. 606-610

Author(s):

Huseyın Onur Tezcan ◽

Fatih Yonar ◽

Sabahat Topuz Kiremitci

Keyword(s):

Public Transport ◽

Logit Model ◽

Choice Model ◽

Mode Choice ◽

Multinomial Logit Model ◽

Multinomial Logit ◽

Questionnaire Data ◽

Mode Choice Model

The aim of this study is to understand the reasons behind the mode choice preferences of passengers using a public transport transfer center. For this aim, a questionnaire data obtained at an interim transfer center in Istanbul is utilized. This interim center hosts stops for paratransit, bus and metro modes. A multinomial logit model of modal preferences is estimated and the coefficient results of this model are used to analyze and compare modes.

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Assortment Optimization and Pricing Under the Multinomial Logit Model with Impatient Customers: Sequential Recommendation and Selection

Operations Research ◽

10.1287/opre.2021.2127 ◽

2021 ◽

Author(s):

Pin Gao ◽

Yuhang Ma ◽

Ningyuan Chen ◽

Guillermo Gallego ◽

Anran Li ◽

...

Keyword(s):

Logit Model ◽

Choice Model ◽

Multinomial Logit Model ◽

Polynomial Time Algorithm ◽

Time Algorithm ◽

Multinomial Logit ◽

Impatient Customers ◽

Purchasing Decisions ◽

Assortment Optimization ◽

Decision Variables

Sequential Recommendation Under the Multinomial Logit Model with Impatient Customers In many applications, customers incrementally view a subset of offered products and make purchasing decisions before observing all the offered products. In this case, the decision faced by a firm is not only what assortment of products to offer, but also in what sequence to offer the products. In “Assortment Optimization and Pricing Under the Multinomial Logit Model with Impatient Customers: Sequential Recommendation and Selection”, Gao, Ma, Chen, Gallego, Li, Rusmevichientong, and Topaloglu propose a choice model where each customer incrementally view the assortment of products in multiple stages, and their patience level determines the maximum number of stages. Under this choice model, the authors develop a polynomial-time algorithm that finds a revenue-maximizing sequence of assortments. If the sequence of assortments is fixed, the problem of finding revenue-maximizing prices can be transformed to a convex program. They combine these results to develop an effective approximation algorithm when both the sequence of assortments and prices are decision variables.

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Modelling brand choice with the multinomial logit model and artificial neural networks: A hybrid approach

International Journal of Research in Marketing ◽

10.1016/s0167-8116(97)88038-x ◽

1997 ◽

Vol 14 (2) ◽

pp. 205

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Logit Model ◽

Hybrid Approach ◽

Brand Choice ◽

Multinomial Logit Model ◽

Multinomial Logit ◽

Artificial Neural

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Treating Zero Brand Sales Observations in Choice Model Estimation: Consequences and Potential Remedies

Journal of Marketing Research ◽

10.1509/jmkr.45.5.618 ◽

2008 ◽

Vol 45 (5) ◽

pp. 618-632 ◽

Cited By ~ 2

Author(s):

Richard A. Briesch ◽

William R. Dillon ◽

Robert C. Blattberg

Keyword(s):

Panel Data ◽

Logit Model ◽

Choice Model ◽

Simulated Data ◽

Multinomial Logit Model ◽

Multinomial Logit ◽

Relative Performance ◽

Data Sets ◽

Model Estimation ◽

Household Panel Data

This research focuses on the consequences of and potential remedies for incorporating zero brand sales observations when calibrating choice models on the basis of household panel data. As the authors show both analytically and empirically, including or excluding zero brand sales observations can bias price elasticities, even in cases in which the data are generated by a multinomial logit model. The authors show that an appropriate choice of a model form (i.e., specification) is influenced by the incidence of “missingness” and the mechanism presumed to be causing a brand to have no purchases in a given store/week. The authors propose two model forms that may perform well in the presence of zero brand sales observations and investigate their relative performance on the basis of simulated data and two household panel data sets.

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