Using online search data to forecast new product sales

Search engines are rapidly emerging to be the "go-to" sites for consumers to learn more about a product, concept or a term of interest, irrespective of the initial channel in which the interest originated - text, radio. TV, multimedia channels, word of mouth, etc. In this paper we argue that data on the search terms used by consumers can provide valuable measures and indicators of consumer interest in a product, concept or a term. Such data can be particularly valuable to managers in gauging potential product interest in a new product launch context or consumption interest in the post-release context. Based on this premise, we develop a model of pre-launch search activity and link the pre-launch search behavior and product characteristics to early sales of the product, thus providing a useful forecasting tool. Applying the model in the context of motion pictures, we find that search term usage follows rather predictable patterns in the pre-launch and post-launch periods and the model provides significant power in forecasting release week sales as a function of pre-release search activity. With advertising data included in the model, we find that the pre-release search data offers additional explanatory and forecasting power, thus highlighting the ability of the search data to capture other factors, such as possibly word-of-mouth, in impacting early sales. We offer specific insights into how managers can use search volume data and the model to plan their new product release. (C) 2011 Elsevier B.V. All rights reserved.