Markov chain Monte Carlo and models of consideration set and parameter heterogeneity

Chiang, Jeongwen; Chib, Siddhartha; Narasimhan, Chakravarthi

doi:10.1016/s0304-4076(98)00062-1

Cited by 137 publications

(94 citation statements)

References 21 publications

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“…Chib and Greenberg (1998) and Wang et al (2001) developed methods for interrelated discrete responses. Chiang et al (1999) examined situations where the choice set that the decision maker considers is unknown to the researcher. Train (2001) extended the Bayesian procedure for mixed logit to nonnormal distributions of coefficients, including lognormal, uniform, and triangular distributions.…”

Section: Introductionmentioning

confidence: 99%

Discrete Choice Methods with Simulation

Train¹

2003

3,812

2,450

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Section: Introductionmentioning

confidence: 99%

Discrete Choice Methods with Simulation

Train¹

2003

3,812

2,450

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“…Research in Monte Carlo simulation can be found in the areas of consumer behaviour models, 8,9 consideration set-brand choice models and the impact of marketing mix, 10 risk reduction in marketing strategy building, 11 and the impact of network effects on market share. 12 While Alan Rosenspan in a 1999 article emphasises the need for risk reduction as a major objective of direct mail testing, 13 no publications were found that discuss the use of simulation as a risk reduction tool.…”

Section: Simulation In Direct Marketingmentioning

confidence: 99%

Simulation and optimisation in direct marketing. Part 1: Using simulation models to develop forecasts

McDoniel¹,

Monteleone

2001

J Database Mark Cust Strategy Manag

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and Stanislaw Ulam, attempted to model a complex situation that still adhered to the laws of physics in order to forecast neutron behaviour. 2,3 P. Bruce McDonielBruce is the Vice-President of Database and Analysis at Summit Marketing Group in St. Louis, Missouri -an integrated marketing service company. Bruce has been in the database marketing industry for over ten years holding positions at GMAC and Mellon Bank. He is currently completing an Executive MBA at Washington University. J. Patrick MonteleonePat is currently the Senior Modeling Analyst at Summit Marketing Group. Pat has 12 years of experience in industry and academia and has held several positions in financial service marketing. Pat also holds a PhD in Management and Decision Sciences from Saint Louis University.Abstract This study is the first in a two-part series that proposes the use of simulation and optimisation modelling in the direct marketing environment to improve marketing results. The first part of this series outlines the benefits of probabilistic simulations of the business process to improve forecasting information. The second part focuses on how an optimisation model, after simulation of the business process, can produce significant gains in direct marketing efficiency.The problem addressed in this paper focuses on creating a forecast for a marketing effort, comparing different marketing allocation scenarios and determining which one produces the most revenue per marketing dollar. The study proposes a solution to this direct marketing problem by making contributions in two areas: 1) A forecasting method using Monte Carlo simulation techniques; and 2) an adaptation of return on promotion as the key indicator of marketing results and its use to compare alternative mailing scenarios.A simulation methodology is proposed and a simulation model is presented. The simulation model is then compared to deterministic methods that use point estimates and evaluated against actual results.

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“…In their study of brand preferences, Bronnenberg and Vanhonacker (1996) suggest a two-stage process involving a choice set formation of relevant brands, and then a selection process from the alternatives in the choice formation stage. Chiang et al (1998) proposed an integrated consideration set-choice model that accounts for heterogeneity in the first stage of the consideration set and the second step of the choice formation model using a nonparametric Markov chain Monte Carlo (MCMC) sampling procedure applied to scanner panel data. Ophem et al (1999) suggest a three-step process for selecting the number of relevant rankings.…”

Section: Introductionmentioning

confidence: 99%

Improving the prediction of ranking data

Palma

2016

Empir Econ

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By using the same number of alternatives for every respondent, all ranking elicitation methods in the literature including full, partial, and best-worst rankings assume respondents know and are able to rank the same number of alternatives. A simple survey elicitation mechanism allowing for individual heterogeneity in the number of rankings for ranked-ordered data is proposed. Using the proposed ranking mechanism as a data augmentation tool yields higher prediction of ranking choices compared to conventional rankings and best-worst methods. The results provide robust evidence of differences in error variance scale and the structure of the underlying utility preferences across ranking stages, including best-worst rankings. The highest predictive power was achieved with the proposed ranking method using only the best ranked alternative. Including any additional rankings other than the best alternative reduces predictive power. Nevertheless, if more than one ranking is used to model preferences, then better predictions are achieved by using the top two best ranked alternatives as supposed to the exploded best-worst rankings. The results stand as a warning about equating ranking choices to true underlying utility preferences across different ranking elicitation stages or mechanisms without properly testing for symmetry and stability of preferences.

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Markov chain Monte Carlo and models of consideration set and parameter heterogeneity

Cited by 137 publications

References 21 publications

Discrete Choice Methods with Simulation

Discrete Choice Methods with Simulation

Simulation and optimisation in direct marketing. Part 1: Using simulation models to develop forecasts

Improving the prediction of ranking data

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