Automatic milking systems (AMS) are implemented in many different environments and situations around the world. To streamline management advice and recommendations to many producers at once, individual farming practices and challenges need to be identified. Benchmarking is often used in the dairy industry to compare farms’ performance by computing percentile ranks of the production values of groups of farms. Grouping of farms for conventional benchmarking is frequently limited to the use of a few common factors such as farms’ geographic location or breed of cattle. Tremblay et al. (2016; J. Dairy Sci. 99:5671–5680) showed that herds’ AMS production data and management information could be characterized in a meaningful way using cluster analysis and that this clustering approach yielded improved peer groups of farms than benchmarking methods based on criteria such as country, region, breed, or breed and region. The 6 clusters (i.e., peer groups) represent different management styles, unique goals or specific challenges and these peer groups could be used to distribute specialized advice to large groups of AMS producers at once (Tremblay et al., 2016). In addition, when using the cluster analysis peer groups, comparisons among farms lead to a more accurate representation of a farm's strengths and weaknesses. For example, a cluster 5 farm with an average milk speed of 2.94 kg/min would be in the 90th percentile when compared with all 529 farms. This might give the producer an exaggerated sense of achievement but when compared with only cluster 5 farms, this farm would become in the 77th percentile, which could potentially motivate the farmer to set higher goals. Cluster analysis allows general recommendations to be produced for all farms within a cluster, and for individual farms to generate more appropriate goals by comparing themselves to farms within their own cluster.