Discrete choice analysis is a popular method of estimating heterogeneous customer preferences. Although model accuracy can be increased by including more choice data, this option is untenable when the obtaining of choice data from target customers is costly and time-consuming.. We thus propose a method for choice data generation for commercial products whose expected money value is a key factor in consumer choice (e.g., commercial vehicles and financial product). Using an individual usage scenario, we generate a discounted cash flow (DCF) model instead of a utility model to estimate the discount rates, rather than partworths, of individual consumers. The DCF model helps us generate synthetic choice data from choice sets consisting of various combinations of attribute levels. Using these data, we employ a hierarchical Bayesian (HB) discrete choice analysis. We conclude the study with a case study on the preference estimation of a hybrid courier truck conversion. The results reveal that the DCF-based HB estimation outperforms the traditional HB estimation.

离散选择分析是一种估计异构客户偏好的流行方法。尽管可以通过包含更多选择数据来提高模型的准确性，但是当从目标客户那里获取选择数据既昂贵又费时时，此选项就站不住脚了。因此，我们提出了一种为预期货币价值为消费者选择的关键因素（例如商用车和金融产品）。使用个人使用场景，我们生成折现现金流量（DCF）模型而不是实用程序模型来估计单个消费者的折现率，而不是部分价值。DCF模型可帮助我们从由属性级别的各种组合组成的选择集中生成综合选择数据。使用这些数据，我们采用分级贝叶斯（HB）离散选择分析。我们以混合动力快递卡车改装的偏好估算为例，完成了本研究。结果表明，基于DCF的HB估计优于传统的HB估计。