E-word of mouth in sales volume forecasting: Toyota Camry case study

Abstract

In recent years, electronic word of mouth has become a significant factor in purchasing decisions, with consumers' sentiments playing a crucial role in shaping the sales of products and services.

This paper introduces a novel approach to sales forecasting that addresses consumers' sentiments toward goods or services by combining the sales volume time series with a quantitative proxy of the unobservable true sentiment. Numerous studies have explored various methods to capture sentiment and accurately predict sales. We have integrated an estimated sentiment signal, variously built via lexicon-based, machine-learning, and deep-learning approaches, into a multivariate autoregressive state space (MARSS) model. We have tested our model on a dataset of 163,000 tweets about the Toyota Camry, covering the period from June 2009 to December 2022 and sales volumes in the US market over the same timeframe.