The increase of voice-based interaction has changed the way people seek information, making search more conversational. Development of effective conversational approaches to search requires better understanding of how people express information needs in dialogue. This paper describes the creation and examination of over 32K spoken utterances collected during 34 hours of collaborative search tasks. The contribution of this work is three-fold. First, we propose a model of conversational information needs (CINs) based on a synthesis of relevant theories in Information Seeking and Retrieval. Second, we show several behavioural patterns of CINs based on the proposed model. Third, we identify effective feature groups that may be useful for detecting CINs categories from conversations. This paper concludes with a discussion of how these findings can facilitate advance of conversational search applications.