Modeling Multiple Sources of Heterogeneity in Multinomial Logit Models: Methodological and Managerial Issues

Gönül, Füsun F.; Srinivasan, Kannan

doi:10.1287/mksc.12.3.213

Cited by 184 publications

(34 citation statements)

References 35 publications

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“…Specifically, if menu m = q 1 q 2 q n is offered with prices p = p 1 p 2 p n , the probabilities of buying variant j = 1 n, or the outside good are Gönül and Srinivasan (1993), who give a general discussion of Bayesian logit models and random brand effects, describe the randomness of b 0 and b 1 as capturing "variations on the intrinsic brand utility across households" (p. 216). We further assume that E e − < + , which is satisfied by the normal distribution and any distribution with bounded support.…”

Section: Assumption 5 Demand For Offered Variants and The Outside Gomentioning

confidence: 99%

Product Line Selection and Pricing with Modularity in Design

Hopp

2005

M&SOM

126

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T his paper addresses the strategic impact of modular design on the optimal length and price of a differentiated product line. We represent consumer demand with a Bayesian logit model. We also break operations costs into product design and production components. Our analysis shows that reducing product development costs via modular design always makes it attractive to offer greater product variety. However, reducing production costs can sometimes motivate a reduction in variety for a risk-averse producer in a multiple-segment market. We also characterize the impacts of degree of modularity and production cost on price markup and market share. Finally, we show that the optimal product line length is monotonic in risk attitude and the monotonic weak majorization, partial order on product assortment.

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Section: Assumption 5 Demand For Offered Variants and The Outside Gomentioning

confidence: 99%

Product Line Selection and Pricing with Modularity in Design

Hopp

2005

M&SOM

126

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“…a careless driver has a higher probability of being involved in a car accident). Kamakura and Russell [10] show how to segment a market into loyal and nonloyal customers, where the loyal customers are less price sensitive and keep buying the same brand (see the works of [6] and [20] who show that even a very short purchase history data can have a huge impact, and that of [16] which shows that consuming one product from a particular company increases the probability of consuming another product from the same company).…”

Section: Related Workmentioning

confidence: 99%

Movie recommender system for profit maximization

Azaria

Hassidim

Kraus

et al. 2013

Proceedings of the 7th ACM Conference on Recommender Systems

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Traditional recommender systems minimize prediction error with respect to users' choices. Recent studies have shown that recommender systems have a positive effect on the provider's revenue.In this paper we show that by providing a set of recommendations different than the one perceived best according to user acceptance rate, the recommendation system can further increase the business' utility (e.g. revenue), without any significant drop in user satisfaction. Indeed, the recommendation system designer should have in mind both the user, whose taste we need to reveal, and the business, which wants to promote specific content.We performed a large body of experiments comparing a commercial state-of-the-art recommendation engine with a modified recommendation list, which takes into account the utility (or revenue) which the business obtains from each suggestion that is accepted by the user. We show that the modified recommendation list is more desirable for the business, as the end result gives the business a higher utility (or revenue). To study possible reduce in satisfaction by providing the user worse suggestions, we asked the users how they perceive the list of recommendation that they received. Differences in user satisfaction between the lists is negligible, and not statistically significant.We also uncover a phenomenon where movie consumers prefer watching and even paying for movies that they have already seen in the past than movies that are new to them.

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“…The mixed multinomial logit (MNL) model specified above and its variant have been well studied and widely applied in economics and marketing (e.g., Guadagni and Little 1983, Kamakura and Russell 1989, Chintagunta et al 1991, Gönül and Srinivasan 1993. 7 The Bayesian approach and the MCMC algorithm for the mixed MNL estimation are well established Allenby 1993, Allenby andLenk 1994).…”

Section: Ketchup Data Setmentioning

confidence: 99%

No Customer Left Behind: A Distribution-Free Bayesian Approach to Accounting for Missing Xs in Marketing Models

Qian

Xie

2011

Marketing Science

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In marketing applications, it is common that some key covariates in a regression model, such as marketing mix variables or consumer profiles, are subject to missingness. The convenient method that excludes the consumers with missingness in any covariate can result in a substantial loss of efficiency and may lead to strong selection bias in the estimation of consumer preferences and sensitivities. To solve these problems, we propose a new Bayesian distribution-free approach, which can ensure that no customer is left behind in the analysis as a result of missing covariates. In this way, all customers are being considered in devising managerial policies. The proposed approach allows for flexible modeling of a joint distribution of multidimensional interrelated covariates that can contain both continuous and discrete variables. At the same time, it minimizes the impact of distributional assumptions involved in covariate modeling because the method does not require researchers to specify parametric distributions for covariates and can automatically generate suitable distributions for missing covariates. We have developed an efficient Markov chain Monte Carlo algorithm for inference. Besides robustness and flexibility, the proposed approach reduces modeling and computational efforts associated with missing covariates and therefore makes the missing covariate problems easier to handle. We evaluate the performance of the proposed method using extensive simulation studies. We then illustrate the method in two real data examples in which missing covariates occur: a mixed multinomial logit discrete-choice model in a ketchup data set and a hierarchical probit purchase incidence model in a retail store data set. These analyses demonstrate that the proposed method overcomes several important limitations of existing approaches for solving missing covariate problems and offers opportunities to make better managerial decisions with the current available marketing databases. Although our applications focus on consumer-level data, the proposed method is general and can be applied to other marketing applications where other types of marketing players are the units of analysis.

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Modeling Multiple Sources of Heterogeneity in Multinomial Logit Models: Methodological and Managerial Issues

Cited by 184 publications

References 35 publications

Product Line Selection and Pricing with Modularity in Design

Product Line Selection and Pricing with Modularity in Design

Movie recommender system for profit maximization

No Customer Left Behind: A Distribution-Free Bayesian Approach to Accounting for Missing Xs in Marketing Models

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