The Operational Value of Social Media Information

Abstract

While the value of using social media information has been established in multiple business contexts, the field of operations and supply chain management have not yet explored the possibilities it offers in improving firms' operational decisions. This study 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 dataset that combines (1) detailed internal operational information, including data on sales, advertising, and promotions, as well as (2) publicly available social media information obtained from Facebook. We implement a variety of machine learning methods to forecast daily sales. 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% to 23.23% over different forecast horizons. We also demonstrate that nonlinear boosting models with feature selection, such as random forests, perform significantly better than traditional linear models. The best‐performing method (random forest) yields an out‐of‐sample MAPE of 7.21% when not using social media information and 5.73% when using social media information is used. In both cases, this significantly improves the accuracy of the company's internal forecasts (a MAPE of 11.97%). Combining these empirical results, we provide recommendations for forecasting sales in general as well as with social media information.

Can Google Trends Improve Your Sales Forecast?

“…Based on our work with an online retailer, we bolster Cui et al. () result by showing that adding customer search data to time series models improves out‐of‐sample forecast errors.…”

“…The intent of this note is to bolster the primary idea in Cui et al. () that user‐generated content can be helpful in improving product level forecasts. We have been working with an online retailer of speciality food over the last several years.…”

“… In this issue, Cui et al. () show how the quantity and quality of user‐generated Facebook data can be used to enhance product forecasts. The intent of this note is to show how another type of user‐generated content—customer search data, specifically one obtained from Google Trends—can be used to reduce out‐of‐sample forecast errors.…”

The Value of Weather Information for E‐Commerce Operations

“…In line with Cui et al. () and Ferreira et al. (), we argue that short‐term sales forecast accuracy is also important for setting dynamic prices, such as those in online retailing.…”

Sustainable Supply Chains in the Age of AI and Digitization: Research Challenges and Opportunities

“…Cui et al. () use the quantity and quality of Facebook comments to improve forecast errors, while Boone et al. () use consumer searchers on Google to improve product forecasts.…”