Using Segmentation Approaches for Better Prediction and Understanding from Consumer Mode Choice Models
研究了利益细分和情境细分两种市场细分方案在消费者出行方式选择模型中的诊断和预测效果，发现细分能提高市场份额估计的准确性。
The diagnostic and predictive efficacy of market segmentation and the relative power of two segmentation schemes (benefit and situational) are investigated by using a market share probabilistic choice model (LOGIT) as a dependent variable. The model relates consumer perceptions of several alternatives on various characteristics to discrete choice and is estimated first for the entire sample and then for each of the benefit and situational market segments. Empirical findings derived from data on consumers’ transportation preferences, perceptions, and choices in the San Francisco Bay area suggest that the models provide fairly accurate estimates of market share and that using the segmentation concept affords diagnostic and predictive advantages.