bank: Bank Card Conjoint Data

In bayesm: Bayesian Inference for Marketing/Micro-Econometrics

Bank Card Conjoint Data

Description

A panel dataset from a conjoint experiment in which two partial profiles of credit cards were presented to 946 respondents from a regional bank wanting to offer credit cards to customers outside of its normal operating region. Each respondent was presented with between 13 and 17 paired comparisons. The bank and attribute levels are disguised to protect the proprietary interests of the cooperating firm.

Usage

data(bank)

Format

The bank object is a list containing two data frames. The first, choiceAtt, provides choice attributes for the partial credit card profiles. The second, demo, provides demographic information on the respondents.

Details

In the choiceAtt data frame:

...$id	respondent id
...$choice	profile chosen
...$Med_FInt	medium fixed interest rate
...$Low_FInt	low fixed interest rate
...$Med_VInt	variable interest rate
...$Rewrd_2	reward level 2
...$Rewrd_3	reward level 3
...$Rewrd_4	reward level 4
...$Med_Fee	medium annual fee level
...$Low_Fee	low annual fee level
...$Bank_B	bank offering the credit card
...$Out_State	location of the bank offering the credit card
...$Med_Rebate	medium rebate level
...$High_Rebate	high rebate level
...$High_CredLine	high credit line level
...$Long_Grace	grace period

The profiles are coded as the difference in attribute levels. Thus, that a "-1" means the profile coded as a choice of "0" has the attribute. A value of 0 means that the attribute was not present in the comparison.

In the demo data frame:

...$id	respondent id
...$age	respondent age in years
...$income	respondent income category
...$gender	female=1

Source

Allenby, Gregg and James Ginter (1995), "Using Extremes to Design Products and Segment Markets," Journal of Marketing Research, 392–403.

References

Appendix A, Bayesian Statistics and Marketing by Rossi, Allenby, and McCulloch.

Examples

data(bank)
cat(" table of Binary Dep Var", fill=TRUE)
print(table(bank$choiceAtt[,2]))
cat(" table of Attribute Variables", fill=TRUE)
mat = apply(as.matrix(bank$choiceAtt[,3:16]), 2, table)
print(mat)
cat(" means of Demographic Variables", fill=TRUE)
mat=apply(as.matrix(bank$demo[,2:3]), 2, mean)
print(mat)

## example of processing for use with rhierBinLogit
if(0) {

  choiceAtt = bank$choiceAtt
  Z = bank$demo
  
  ## center demo data so that mean of random-effects
  ## distribution can be interpreted as the average respondent
  Z[,1] = rep(1,nrow(Z))
  Z[,2] = Z[,2] - mean(Z[,2])
  Z[,3] = Z[,3] - mean(Z[,3])
  Z[,4] = Z[,4] - mean(Z[,4])
  Z = as.matrix(Z)
  
  hh = levels(factor(choiceAtt$id))
  nhh = length(hh)
  lgtdata = NULL
  for (i in 1:nhh) {
    y = choiceAtt[choiceAtt[,1]==hh[i], 2]
    nobs = length(y)
    X = as.matrix(choiceAtt[choiceAtt[,1]==hh[i], c(3:16)])
    lgtdata[[i]] = list(y=y, X=X)
  }
  cat("Finished Reading data", fill=TRUE)
  
  Data = list(lgtdata=lgtdata, Z=Z)
  Mcmc = list(R=10000, sbeta=0.2, keep=20)
  
  set.seed(66)
  out = rhierBinLogit(Data=Data, Mcmc=Mcmc)
  
  begin = 5000/20
  summary(out$Deltadraw, burnin=begin)
  summary(out$Vbetadraw, burnin=begin)
  
  ## plotting examples
  if(0){
    
    ## plot grand means of random effects distribution (first row of Delta)
    index = 4*c(0:13)+1
    matplot(out$Deltadraw[,index], type="l", xlab="Iterations/20", ylab="",
            main="Average Respondent Part-Worths")
    
    ## plot hierarchical coefs
    plot(out$betadraw)
    
    ## plot log-likelihood
    plot(out$llike, type="l", xlab="Iterations/20", ylab="", 
         main="Log Likelihood")
  }
}

bayesm documentation built on Sept. 24, 2023, 1:07 a.m.