  Today's businesses face a high pressure to innovate in order to succeed in
highly competitive markets. Successful innovations, though, typically require
the identification and analysis of customer needs. While traditional,
established need elicitation methods are time-proven and have demonstrated
their capabilities to deliver valuable insights, they lack automation and
scalability and, thus, are expensive and time-consuming. In this article, we
propose an approach to automatically identify and quantify customer needs by
utilizing a novel data source: Users voluntarily and publicly expose
information about themselves via social media, as for instance Facebook or
Twitter. These posts may contain valuable information about the needs, wants,
and demands of their authors. We apply a Design Science Research (DSR)
methodology to add design knowledge and artifacts for the digitalization of
innovation processes, in particular to provide digital support for the
elicitation of customer needs. We want to investigate whether automated,
speedy, and scalable need elicitation from social media is feasible. We
concentrate on Twitter as a data source and on e-mobility as an application
domain. In a first design cycle we conceive, implement and evaluate a method to
demonstrate the feasibility of identifying those social media posts that
actually express customer needs. In a second cycle, we build on this artifact
to additionally quantify the need information elicited, and prove its
feasibility. Third, we integrate both developed methods into an end-user
software artifact and test usability in an industrial use case. Thus, we add
new methods for need elicitation to the body of knowledge, and introduce
concrete tooling for innovation management in practice.
