By using internet as a source of data, we estimate pricing models based on the information contained in the description of items on sale. The novel application is on one category of the Fashion industry. Our estimation strategy uses text mining and methods of sparse modelling, namely shrinkage methods and dimension reduction methods, with the aim of obtaining a model which gives the best out-of-sample predictive performance with a high level of interpretability. The results show that compared with the simple predictor, the average price, the models developed in the paper produce a decrease in the pricing error which is up to 7.7% when the brand is considered and up to 58.3%, when the brand is not considered, in the case when shrinkage methods are used. When dimension reduction methods are used, the decrease is up to 41.4% when brand is included and up to 66.8% in the no-brand case.
Text Based Pricing Modelling: an Application to the Fashion Industry
Federico Crescenzi;Marzia Freo
;Alessandra Luati
2019
Abstract
By using internet as a source of data, we estimate pricing models based on the information contained in the description of items on sale. The novel application is on one category of the Fashion industry. Our estimation strategy uses text mining and methods of sparse modelling, namely shrinkage methods and dimension reduction methods, with the aim of obtaining a model which gives the best out-of-sample predictive performance with a high level of interpretability. The results show that compared with the simple predictor, the average price, the models developed in the paper produce a decrease in the pricing error which is up to 7.7% when the brand is considered and up to 58.3%, when the brand is not considered, in the case when shrinkage methods are used. When dimension reduction methods are used, the decrease is up to 41.4% when brand is included and up to 66.8% in the no-brand case.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
