The elicitation and monitoring of customer needs is an important task for businesses, allowing them to design customer-centric products and services and control marketing activities. While there are different approaches available, most lack in automation, scalability and monitoring capabilities. In this work, we demonstrate the feasibility towards an automated prioritization and quantification of customer needs from social media data. To do so, we apply a supervised machine learning approach on the example of previously labeled Twitter data from the domain of e-mobility. We descriptively code over 1000 German tweets and build eight distinct classification models, so that a resulting artifact can independently determine the probabilities of a tweet containing each of the eight previously defined needs. To increase the scope of application, we deploy the machine learning models as part of a web service for public use. The resulting artifact can provide valuable insights for need elicitation and monitoring when analyzing user-generated content on a large scale.