customerSat

Responses to a satisfaction survey for a Yellow Pages advertising product.
 All responses are on a 10 point scale from 1 to 10 (10 is "Excellent"
 and 1 is "Poor")

datasets

Covers many important models used
in marketing and micro-econometrics applications.
The package includes:
Bayes Regression (univariate or multivariate dep var),
Bayes Seemingly Unrelated Regression (SUR),
Binary and Ordinal Probit,
Multinomial Logit (MNL) and Multinomial Probit (MNP),
Multivariate Probit,
Negative Binomial (Poisson) Regression,
Multivariate Mixtures of Normals (including clustering),
Dirichlet Process Prior Density Estimation with normal base,
Hierarchical Linear Models with normal prior and covariates,
Hierarchical Linear Models with a mixture of normals prior and covariates,
Hierarchical Multinomial Logits with a mixture of normals prior
and covariates,
Hierarchical Multinomial Logits with a Dirichlet Process prior and covariates,
Hierarchical Negative Binomial Regression Models,
Bayesian analysis of choice-based conjoint data,
Bayesian treatment of linear instrumental variables models,
Analysis of Multivariate Ordinal survey data with scale
usage heterogeneity (as in Rossi et al, JASA (01)),
Bayesian Analysis of Aggregate Random Coefficient Logit Models as in BLP (see
Jiang, Manchanda, Rossi 2009)
For further reference, consult our book, Bayesian Statistics and
Marketing by Rossi, Allenby and McCulloch (Wiley 2005) and Bayesian Non- and Semi-Parametric
Methods and Applications (Princeton U Press 2014).

Peter Rossi

bayesm

Bayesian Inference for Marketing/Micro-Econometrics

customerSat function

A data frame with 1811 observations on the following 10 variables.<dl class="dl-horizontal">
 <dt><code>q1</code></dt><dd>Overall Satisfaction</dd>
 <dt><code>q2</code></dt><dd>Setting Competitive Prices</dd>
 <dt><code>q3</code></dt><dd>Holding Price Increase to a Minimum</dd>
 <dt><code>q4</code></dt><dd>Appropriate Pricing given Volume</dd>
 <dt><code>q5</code></dt><dd>Demonstrating Effectiveness of Purchase</dd>
 <dt><code>q6</code></dt><dd>Reach a Large # of Customers</dd>
 <dt><code>q7</code></dt><dd>Reach of Advertising</dd>
 <dt><code>q8</code></dt><dd>Long-term Exposure</dd>
 <dt><code>q9</code></dt><dd>Distribution</dd>
 <dt><code>q10</code></dt><dd>Distribution to Right Geographic Areas</dd>
 </dl>

Format

Customer Satisfaction Data — customerSat

A data frame with 1811 observations on the following 10 variables.<dl class='dl-horizontal'>
 <dt><code>q1</code></dt><dd>Overall Satisfaction</dd>
 <dt><code>q2</code></dt><dd>Setting Competitive Prices</dd>
 <dt><code>q3</code></dt><dd>Holding Price Increase to a Minimum</dd>
 <dt><code>q4</code></dt><dd>Appropriate Pricing given Volume</dd>
 <dt><code>q5</code></dt><dd>Demonstrating Effectiveness of Purchase</dd>
 <dt><code>q6</code></dt><dd>Reach a Large # of Customers</dd>
 <dt><code>q7</code></dt><dd>Reach of Advertising</dd>
 <dt><code>q8</code></dt><dd>Long-term Exposure</dd>
 <dt><code>q9</code></dt><dd>Distribution</dd>
 <dt><code>q10</code></dt><dd>Distribution to Right Geographic Areas</dd>
 </dl>

customerSat: Customer Satisfaction Data

Description

Usage

Arguments

Format

References

Examples