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Accurate forecasting of sales/consumption is particularly important for marketing because this information can be used to adjust marketing budget allocations and overall marketing strategies. Recently, online social platforms have produced an unparalleled amount of data on consumer behavior. However, two challenges have limited the use of these data in obtaining meaningful business marketing insights. First, the data are typically in an unstructured format, such as texts, images, audio, and video. Second, the sheer volume of the data makes standard analysis procedures computationally unworkable. In this study, we combine methods from cloud computing, machine learning, and text mining to illustrate how online platform content, such as Twitter, can be effectively used for forecasting. We conduct our analysis on a significant volume of nearly two billion Tweets and 400 billion Wikipedia pages. Our main findings emphasize that, by contrast to basic surface-level measures such as the volume of or sentiments in Tweets, the information content of Tweets and their timeliness significantly improve forecasting accuracy. Our method endogenously summarizes the information in Tweets. The advantage of our method is that the classification of the Tweets is based on what is in the Tweets rather than preconceived topics that may not be relevant. We also find that, by contrast to Twitter, other online data (e.g., Google Trends, Wikipedia views, IMDB reviews, and Huffington Post news) are very weak predictors of TV show demand because users tweet about TV shows before, during, and after a TV show, whereas Google searches, Wikipedia views, IMDB reviews, and news posts typically lag behind the show. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mksc.2015.0972 .
Liu et al. (Thu,) studied this question.