Technical Note: Mathematical Properties of the Optimal Product Line Selection Problem Using Choice-Based Conjoint Analysis

Kyle D. Chen () and Warren H. Hausman ()
Additional contact information
Kyle D. Chen: Computer Science Department, IBM Almaden Research Center, San Jose, California 95120-6099
Warren H. Hausman: Department of Industrial Engineering and Engineering Management, Stanford University, Stanford, California 94305-4024

Management Science, 2000, vol. 46, issue 2, 327-332

Abstract: Selecting and pricing product lines is an essential activity in many businesses. In recent years, quantitative approaches for such tasks have been gaining in popularity. One often-employed method is to use data from traditional rankings/ratings-based conjoint analysis and attack the product line selection problem with enumeration or heuristics. In this note, we employ a relatively new methodology known as choice-based conjoint analysis (to model customer preferences) and investigate its mathematical properties when used to model the product line selection problem. Despite some inherent limitations resulting from its aggregated formulation, we show that this more parsimonious conjoint approach has some special mathematical properties that lead to an efficient optimal algorithm to tackle the product line/price selection problem. As a result, problems of realistic size can be solved efficiently using standard, commercially available mathematical programming codes.

Keywords: product design; multinomial logit model; choice-based conjoint analysis; independence of irrelevant alternatives; totally unimodular binary integer program; optimal product line selection (search for similar items in EconPapers)
Date: 2000
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (41)

Downloads: (external link)
http://dx.doi.org/10.1287/mnsc.46.2.327.11931 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:46:y:2000:i:2:p:327-332

Access Statistics for this article

More articles in Management Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().