Ricardo Montoya
Education
Ph.D. in Marketing, Columbia Business School.
M. Phil. in Marketing, Columbia Business School.
M.S. in Operations Management, University of Chile.
Industrial Engineering, University of Chile.
Research Interests
Bayesian Econometrics, Models of Consumer and Firm Behavior, Dynamic Marketing Mix Models, Stochastic Dynamic Programming, Product Design, Non-compensatory Decision Models.
Journal Articles
Preference Estimation under Bounded Rationality: Identification of Attribute Non-Attendance in Stated-Choice Data Using a Support Vector Machines Approach (2023). Co-authors: Verónica Díaz and Sebastián Maldonado. European Journal of Operational Research, 304, 797–812.
Drivers of customer satisfaction in the grocery retail industry: A longitudinal analysis across store formats (2021). Co-authors: Marcel Goic and Camilo Levenier. Journal of Retailing and Consumer Services, 60,102505.
Components of effort for repeated tasks (2021). Co-authors: Andres Musalem, Martin Meißner and Joel Huber. Journal of Behavioral Decision Making, 34(1), 99-115.
A hidden Markov model to detect on-shelf out-of-stocks (2019). Co-author: Carlos Gonzalez. Manufacturing and Service Operations Management, 21(4):932-948.
Buying free rewards: the impact of a points-plus-cash promotion on purchase and reward redemption (2019). Co-author: Constanza Flores. Marketing Letters, 30, 107-118.
The effect of house ads on multichannel sales (2018). Co-authors: Marcel Goic and Rodolfo Alvarez. Journal of Interactive Marketing, 42, 32-45.
Simultaneous preference estimation and heterogeneity control for choice-based conjoint via support vector machines (2017). Co-authors: Sebastian Maldonado and Julio Lopez. Journal of the Operational Research Society, 68(1), 1323-1334.
Embedded heterogeneous feature selection for conjoint analysis: a SVM approach using L1 penalty (2017). Co-authors: Sebastian Maldonado and Julio Lopez. Applied Intelligence, 46, 775-787.
Contingent preannounced pricing policies with strategic consumers (2016). Co-authors: Jose Correa and Charles Thraves. Operations Research, 64(1), 251-272.
Advanced conjoint analysis using feature selection via support vector machines (2015). Co-authors: Sebastian Maldonado and Richard Weber. European Journal of Operational Research, 241, 564-574.
Dynamic learning in behavioral games: A hidden Markov mixture of experts approach (2012). Co-authors: Asim Ansari and Oded Netzer. Quantitative Marketing and Economics, 10, 475-503.
The design of durable goods (2011). Co-authors: Oded Koenigsberg and Rajeev Kohli. Marketing Science, 30(1), 111-122.
Dynamic allocation of pharmaceutical detailing and sampling for long-term profitability (2010). Co-authors: Oded Netzer and Kamel Jedidi. Marketing Science, 29(5), 909-924.
Package size decisions (2010). Co-authors: Oded Koenigsberg and Rajeev Kohli. Management Science, 56(3), 485-494.
Linear penalization support vector machines for feature selection (2005). Co-authors: Jaime Miranda and Richard Weber. Lecture Notes in Computer Science, 3776, 188-192.
Book Chapters
Dynamic allocation of pharmaceutical detailing and sampling for long-term profitability (2016). Co-authors: Kamel Jedidi and Oded Netzer. In From Little's Law to Marketing Science: Essays in Honor of John D.C. Little.
Working Papers
Heterogeneity in HMMs: Allowing for heterogeneity in the number of states. Co-authors: Nicolas Padilla and Oded Netzer. [Download Paper]
Probabilistic lexicographic choice. Co-authors: Kamel Jedidi and Rajeev Kohli.
Probabilistic choice in optimal product design. Co-author: Rajeev Kohli.
Work in Progress
The effect of pregnancy and childbirth on consumption behavior, with Verónica Diaz and Oded Netzer.
The impact of the Covid-19 pandemic on the nutritional quality of food purchases, with Francisca Grandón, Andrés Elberg, and Cristián Dagnino.
Estimating the Breakage Rate of an Airline's Frequent-flyer program using Individual-Level Data, with Marcel Goic and Isamar Troncoso.
The effects of reward programs, with Ran Kivetz and Oded Netzer.
Optimal pricing of points in points plus cash reward programs.