Coastal areas are highly dynamic systems sensitive to natural and anthropogenic change. The range of social, economic, and environmental functions served by diverse coastal environments makes understanding their geomorphology valuable. Parametric shape functions have historically been used to represent the cross shore profile of specific coastal environments. Geometric (consequential) parameters of these functions are often related to parameters representing key morphological drivers (causal parameters) to provide first order predictive capabilities. Advances in the application of data-driven modelling techniques presents opportunity to develop a unified characterization of cross shore morphology in diverse coastal environments. However, the ability to represent diverse profile geometries using a single parametric representation has not been observed. The aim of this study is therefore to evaluate the ability of parametric expressions to effectively characterize diverse coastal profile geometries. Here, a new parametric function is presented that can effectively represent key geometric attributes of diverse cross shore profiles. This function demonstrated high performance in the representation of both theoretical profile morphotypes (mean SSE = 0.04) and measured profile data from Narrabeen-Collaroy Beach, Australia (mean RMSE = 0.05 m) compared to eight existing parametric functions. Parametric values were effectively applied to group profiles with similar geometry using manual labelling and K-means clustering. The average profile of each cluster (i.e., the cluster centroid) provided a good representation of the grouped profiles, particularly when parametric grouping was used in place of empirical grouping. Correlation analyses between causal and consequential parameters demonstrated an ability to identify expected geomorphological trends using the new parametric function. Application of cluster centroids for correlation analysis provided amplified correlation strengths for expected geomorphological relationships, particularly when clustering with fitted parametric values. These results suggest that a parametric representation of the coastal profile has potential to characterize and group diverse profile geometries, and that causal-consequential parameter relationships have potential to identify geomorphological trends. The application of these results provide a foundation for the development of a predictive coastal profile model. This model could be trained using data from diverse coastal environments to predict coastal profile response to changes in the metocean climate related to human interventions and climate change.