This article considers effective procedures for clustering customers in E-commerce, according to the importance ascribed by a firm to various aspects of the clustering. These procedures are based on data on the behavior of customers online and enable websites to be adapted to differing groups of customers. The effectiveness of a clustering procedure can be assessed according to various aspects, e.g. the clarity of the clusters, their relative sizes and the predictive value of the clustering. There is no single variable that definitively describes the effectiveness of an algorithm according to each aspect. Hence, using principal component analysis, we define standardized synthetic variables that describe the attractiveness of a clustering algorithm according to each aspect. This reduces the dimension of the resulting multi-criteria assessment. Weights can then be ascribed by an expert to a small number of synthetic variables that have a clear interpretation. We use such a procedure to compare the effectiveness of forty clustering procedures. These are based on a combination of five algorithms and eight different numbers of clusters. Although the data used are problem-specific, the results illustrate some of the strengths and weaknesses of the procedures used.