The Operational Value of Social Media Information

While the value of using social media information has been established in multiple business contexts, the field of operations and supply chain management has not yet explored the possibilities it offers in improving firms' operational decisions. This paper attempts to do that by empirically studying whether using publicly available social media information can improve the accuracy of daily sales forecasts. We collaborated with an online apparel retailer to assemble a data set that combines (1) detailed internal operational information, including data on sales, advertising, and promotions, and (2) publicly available social media information obtained from Facebook. We implement a variety of machine learning methods to create daily sales forecasts. For each method, we compare the accuracy of the forecasts we obtain with and without the social media information. We find that using social media information results in statistically significant improvements in the out-of-sample accuracy of the forecasts, with relative improvements ranging from 12.85 percent to 23.23 percent over different forecast horizons. Nonlinear models with feature selection, such as random forests, perform significantly better than linear models, such as linear regression. Our preferred method (random forest) yields an out-of-sample MAPE of 7.21 percent when not using social media information and 5.73 percent when using social media information. In both cases, this significantly improves the accuracy of the company's internal forecasts (a MAPE of 11.97 percent). We decompose the improvement into the value of utilizing state-of-the-art machine-learning methods and the value of incorporating social media information, and we explore the contribution of each of the social media features to the improvement of forecast accuracy, finding that the comments encoded using natural language processing techniques have the highest predictive power