Improving Sales Forecasting Models by Integrating Customers’ Feedbacks: A Case Study of Fashion Products

In this paper, we investigate the task of predicting sales in the fashion companies – a very fascinating sector by utilizing advanced machine learning models incorporated with rich features. This can help businesses predict the sales by using data from past transactions and other factors. To this end, we propose a method to improve the performance of sale forecasting models by enriching the models with the information of customers’ online feedbacks (i.e., consumers’ ratings and comments). This method involves leveraging both historical sales data and direct customer feedback to create predictive models that offer a comprehensive understanding of market dynamics. To facilitate the experiments, we also introduce a newly-built dataset about fashion products on a large e-commerce platform in Vietnam. We conducted extensive experiments on this dataset using three robust regression models which are Linear Regression, Decision Tree, and Random Forest. To classify customers’ reviews, we exploit the innovative pre-trained language model, namely Bidirectional Encoder Representation from Transformer (BERT). Experimental results on this dataset showed that integrating this kind of information indeed boosts the sale forecasting models’ accuracy significantly by all conventional evaluation metrics such as MAE and RMSE scores. Specifically, the proposed sale forecasting models integrated with customers’ feedbacks significantly decreased the error rates of RMSE scores by 12%, 23.3%, and 17,8% using Linear Regression, Decision Tree, and Random Forest respectively.