Can segmentation improve predictive accuracy in conjoint analysis?

Journal of the Academy of Marketing Science

Volumn 21, Issue 4, 1993, Pages 353-355

  Hagerty, Michael R ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 33947582374     PISSN: 00920703     EISSN: 15527824     Source Type: Journal    
DOI: 10.1007/BF02894528     Document Type: Article

References (17)

1. How Advertising Affects Sales: Meta-Analysis of Econometric Results
2. Latent Class Metric Conjoint Analysis
3. A Simulated Annealing Methodology for Clusterwise Linear Regression
4. Techniques in Market Measurement: The Jackknife
5. Cross-Validation Assessment of Alternatives to Individual-Level Conjoint Analysis: A Case Study
6. An Empirical Test of Optimal Respondent Weighting in Conjoint Analysis
7. A Hybrid Conjoint Model with Individual-Level Interaction Estimation
8. Improving the Predictive Power of Conjoint Analysis: The Use of Factor Analysis and Cluster Analysis
9. The Cost of Simplifying Preference Models
10. Testing the Accuracy, Usefulness, and Significance of Probabilistic Choice Models: An Information Theoretic Approach
11. When Choice Models Fail: Compensatory Models in Negatively Correlated Environments
12. A Least Squares Procedure for Benefit Segmentation for Conjoint Analysis
13. The Choice Theory Approach to Market Research
14. An Approach to Simultaneous Estimation and Segmentation in Conjoint Analysis
15. The Shifting Paradigm in Marketing Research
16. Consumer Benefit Segmentation Using Clusterwise Linear Regression
17. A Clusterwise Regression Method for Simultaneous Fuzzy Market Structuring and Benefit Segmentation