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Choice-Based Conjoint Analysis

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Handbook of Market Research

Abstract

Conjoint analysis is one of the most popular methods to measure preferences of individuals or groups. It determines, for instance, the degree how much consumers like or value specific products, which then leads to a purchase decision. In particular, the method discovers the utilities that (product) attributes add to the overall utility of a product (or stimuli). Conjoint analysis has emerged from the traditional rating- or ranking-based method in marketing to a general experimental method to study individual’s discrete choice behavior with the choice-based conjoint variant. It is therefore not limited to classical applications in marketing, such as new product development, pricing, branding, or market simulations, but can be applied to study research questions from related disciplines, for instance, how marketing managers choose their ad campaign, how managers select internationalization options, why consumers engage in or react to social media, etc. This chapter describes comprehensively the “state-of-the-art” of conjoint analysis and choice-based conjoint experiments and related estimation procedures.

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Author information

Authors and Affiliations

  1. University of Groningen, Groningen, The Netherlands

    Felix Eggers

  2. University of Hamburg, Hamburg, Germany

    Henrik Sattler & Thorsten Teichert

  3. Department of Marketing and Brand Management, University of Cologne, Köln, Germany

    Franziska Völckner

Authors
  1. Felix Eggers
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  2. Henrik Sattler
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  3. Thorsten Teichert
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  4. Franziska Völckner
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Corresponding author

Correspondence to Franziska Völckner .

Editor information

Editors and Affiliations

  1. Universität Mannheim, Mannheim, Germany

    Christian Homburg

  2. Inst. Informations Systems and Marketing Marketing & Sales Research Group, Karlsruher Institut für Technologie, Karlsruhe, Germany

    Martin Klarmann

  3. LS für ABWL und Marketing I, Universität Mannheim, Mannheim, Baden-Württemberg, Germany

    Arnd Vomberg

Appendix: R Code

Appendix: R Code

The R code and dataset that correspond to the ebook reader example and estimated models can be found at: http://www.preferencelab.com/data/CBC.R. The estimation uses the mlogit package (Croissant 2012), which needs to be installed first. A less documented version of the R code can be found below (# indicates a comment):

# load the library to estimate multinomial choice models. library(mlogit) # load (simulated) data about ebook readers cbc <- read.csv(url("http://www.preferencelab.com/data/ Ebook_Reader.csv")) # convert data for mlogit cbc <- mlogit.data(cbc, choice="Selected", shape="long", alt.var="Alt_id", id.var = "Resp_id") ### calculate models ### ### partworth model ### ml1 <- mlogit(Selected ~ Storage_4GB + Storage_8GB + Screen.size_5inch + Screen.size_6inch + Color_black + Color_white + Price_79 + Price_99 + Price_119 + None | 0, cbc) summary(ml1) # recover reference level estimates (effect-coding) # Storage_16GB -(coef(ml1)["Storage_4GB"] + coef(ml1)["Storage_8GB"]) # Screen.size_7inch -(coef(ml1)["Screen.size_5inch"] + coef(ml1)["Screen.size_6inch"]) # Color_silver -(coef(ml1)["Color_black"] + coef(ml1)["Color_white"]) # Price_139 -(coef(ml1)["Price_79"] + coef(ml1)["Price_99"] + coef(ml1)["Price_119"]) # standard errors of the effects are given by the # square root of the diagonal elements of the # variance-covariance matrix covMatrix <- vcov(ml1) sqrt(diag(covMatrix)) # with effect-coding, the standard error of the reference # level needs to consider the off-diagonal elements of the # corresponding attribute levels # Std. Error Storage_16GB sqrt(sum(covMatrix[1:2, 1:2])) # Std. Error Screen.size_7inch sqrt(sum(covMatrix[3:4, 3:4])) # Std. Error Color_silver sqrt(sum(covMatrix[5:6, 5:6])) # Std. Error Price_139 sqrt(sum(covMatrix[7:9, 7:9])) ### Vector model ### # Storage and Price follow a linear trend. Replacing # parameters leads to a more parsimonious model. ml2 <- mlogit(Selected ~ Storage + Screen.size_5inch + Screen.size_6inch + Color_black + Color_white + Price + None | 0, cbc) summary(ml2) # likelihood ratio test lrtest(ml2, ml1) # incremental willingness-to-pay for storage coef(ml2)["Storage"]/coef(ml2)["Price"] # WTP to upgrade from a black to a white ebook reader (coef(ml2)["Color_white"] - coef(ml2)["Color_black"])/coef(ml2)["Price"] ### Vector model for screen size has sig. worse fit ### ml3 <- mlogit(Selected ~ Storage + Screen.size + Color_black + Color_white + Price + None | 0, cbc) summary(ml3) lrtest(ml3, ml2) ### Testing an ideal point model for screen size ### ml4 <- mlogit(Selected ~ Storage + Screen.size + I(Screen.size**2) + Color_black + Color_white + Price + None | 0, cbc) summary(ml4) # same model fit because no differences in df lrtest(ml4, ml2) ### Adding interactions between screen size and color ### ml5 <- mlogit(Selected ~ Storage + Screen.size_5inch + Screen.size_6inch + Color_black + Color_white + Price + Screen.size_5inch * Color_black + Screen.size_6inch * Color_black + Screen.size_5inch * Color_white + Screen.size_6inch * Color_white + None| 0, cbc) summary(ml5) # likelihood ratio test lrtest(ml2, ml5)

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Eggers, F., Sattler, H., Teichert, T., Völckner, F. (2018). Choice-Based Conjoint Analysis. In: Homburg, C., Klarmann, M., Vomberg, A. (eds) Handbook of Market Research. Springer, Cham. https://doi.org/10.1007/978-3-319-05542-8_23-1

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