Investigating Consumer Information Search Behavior and Consumer Emotions to Improve Sales Forecasting

Sales forecasting is an essential topic for organizations and accurate forecasts can help to establish competitive advantage. Organizations can integrate a variety of information sources into their forecasting process. In this regard, the information search behavior of potential consumers as well as their emotions about a brand are supposed to improve forecasts. In this study, we combine these two data sources to forecast sales figures of two large automobile manufacturers. We analyze data from Google's search engine as well as microblogging data from Twitter to perform in-sample and out-of-sample analyses. The results show that the conjunction of these data sources allow superior sales forecasting in contrast to an individual consideration of information search behavior and consumer emotions. Hence, data about consumer information search behavior should be enriched with the emotional state of consumers to support organizational decision making.