Abstract

The design of new products and services starts with the identification of needs of potential customers or users. Many existing methods like observations, surveys, and experiments draw upon specific efforts to elicit unsatisfied needs from individuals. At the same time, a huge amount of user-generated content in micro blogs is freely accessible at no cost. While this information is already analyzed to monitor sentiments towards existing offerings, it has not yet been tapped for the elicitation of needs. In this paper, we lay an important foundation for this endeavor: we propose a Machine Learning approach to identify those posts that do express needs. Our evaluation of tweets in the e-mobility domain demonstrates that the small share of relevant tweets can be identified with remarkable precision or recall results. Applied to huge data sets, the developed method should enable scalable need elicitation support for innovation managers—across thousands of users, and thus augment the service design tool set available to him.

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