Recent technological advances have enabled the easy collection of consumer behavior data in real-time. Typically, these data contain the time at which a consumer engages in a particular activity such as entering a store, buying a product, or making a call. The occurrence time of certain events must be analyzed as circular random variables, with 24:00 corresponding to 0:00. To effectively implement a marketing strategy (pricing, promotion, or product design), consumers should be segmented into homogeneous groups. This paper proposes a methodology based on circular statistical models from which we construct a clustering algorithm based on the use patterns of consumers. In particular, we model temporal patterns as circular distributions based on nonnegative trigonometric sums (NNTSs). Consumers are clustered into homogeneous groups based on their vectors of parameter estimates by using a spherical k-means clustering algorithm. For this purpose, we define the parameter space of NNTS models as a hypersphere. The methodology is applied to three real datasets comprising the times at which individuals send short-service messages and start voice calls and the check-in times of the users of a mobile application Foursquare.