Title
Robust Product Positioning Considering Customer Preferences
Abstract
This paper investigates the product-positioning problem for a monopolist developing of a new product. The majority of the literature on product positioning assumes that the consumer choice model and parameters are both precisely known. In reality, however, the marketer may be uncertain about the model structure or the parameters due to incomplete information, small sample data size, etc. In this paper, we investigate a robust product-positioning problem based on the multinomial logit model to account for parameter uncertainty. We first establish a nominal product-positioning model, in which valuation parameters are assumed to be known. Then, we investigate a static robust product-positioning problem with the multinomial logit model to obtain the maximin revenue. We also propose a two-period dynamic robust model. For each setting, we consider both homogeneous and heterogeneous customers' preferences. Finally, numerical experiments are presented to compare the nominal model with the static robust model, and to compare the static robust model with the dynamic robust model. The experiments show that when facing uncertain parameters, the robust model obtains more stable revenue than the nominal model; the dynamic robust model obtains higher revenue than the static robust model.
Year
DOI
Venue
2019
10.1109/jsyst.2019.2911011
IEEE Systems Journal
Keywords
Field
DocType
Optimization,Numerical models,Uncertainty,Pricing,Probabilistic logic,Uncertain systems
Revenue,Mathematical optimization,Minimax,Computer science,Multinomial logistic regression,Consumer choice,Real-time computing,Probabilistic logic,Valuation (finance),Complete information,New product development
Journal
Volume
Issue
ISSN
13
3
1932-8184
Citations 
PageRank 
References 
0
0.34
0
Authors
6
Name
Order
Citations
PageRank
Wei Qi16018.07
Xing-Gang Luo213814.85
Huihui Wang3202.66
Xuwang Liu431.75
Yang Yu5105.28
Zhong-Liang Zhang600.34