Choice data generation using usage scenarios and discounted cash flow analysis

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. •The predictive power of the DCF model is higher than obtained with discrete choice analysis using a utility model.•Accurate consumer preference estimation is viable with less data and fewer choice sets.•The DCF model generates synthetic choice data for HB estimation.•The DCF-based HB estimation outperforms traditional HB estimation.