bank: Bank Card Conjoint Data

Description Usage Format Details Source References Examples

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

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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.
http://www.perossi.org/home/bsm-1

Examples

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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")
  }
}

Example output

 table of Binary Dep Var

   0    1 
8326 6473 
 table of Attribute Variables
$Med_FInt

   -1     0     1 
 2381 10409  2009 

$Low_FInt

   -1     0     1 
 1181 12185  1433 

$Med_VInt

    0     1 
13252  1547 

$Rewrd_2

   -1     0     1 
 2129 10427  2243 

$Rewrd_3

   -1     0     1 
 2243 10436  2120 

$Rewrd_4

   -1     0     1 
 1281 12799   719 

$Med_Fee

   -1     0     1 
 1788 10983  2028 

$Low_Fee

   -1     0     1 
 1304 12771   724 

$Bank_B

   -1     0     1 
 2688 10129  1982 

$Out_State

   -1     0     1 
 1497 12819   483 

$Med_Rebate

   -1     0     1 
 1902 10875  2022 

$High_Rebate

   -1     0     1 
 1776 12777   246 

$High_CredLine

   -1     0     1 
 2392 12287   120 

$Long_Grace

   -1     0 
 2133 12666 

 means of Demographic Variables
    age  income 
46.3055 46.1945 

bayesm documentation built on July 21, 2017, 7:18 p.m.