Retention of latent segments in regression-based marketing models
Section snippets
The criteria
The simulation experiment compares seven major segment retention criteria: AIC (Akaike, 1973); AIC with a penalty factor of 3, referred to as AIC3 Bozdogan, 1992, Bozdogan, 1994; BIC (Schwarz, 1978); CAIC (Bozdogan, 1987); ICOMP Bozdogan, 1988, Bozdogan, 1990; the validation sample log likelihood LOGLV (Andrews & Currim, 2003); and the Normed Entropy Criterion (NEC) (Celeux & Soromenho, 1996). The reader is referred to the references cited above for detailed discussions of the theoretical
Summary of results
We measure the performance of the various segment retention criteria by (i) their success rates, or the percentage of datasets in which the criteria identify the true number of segments; and (ii) the Root Mean Square Error between the true and estimated β parameters, RMSE(β), of the models selected by the criteria. Given two criteria with similar success rates, we prefer underfitting to overfitting Andrews & Currim, 2003, Cutler & Windham, 1994. Of course, we also prefer model selection
Conclusion
It is generally clear from comparing the results of this study to those of Andrews and Currim (2003) and Cutler and Windham (1994) that the type of distribution being mixed, the model specification, and the characteristics of the market affect the performance of segment retention criteria. Consequently, the finding that AIC3 is the best criterion to use with regression (e.g., conjoint and market response) models for normally distributed data and with logit models for multinomial data (Andrews &
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