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Consumers are very important assets for the company. This is the reason why companies must design and use strategies that are quite clear in treating consumers. With the large number of consumers owned by a company, the problem that must be faced is how to determine potential consumers. With clustering methods in data mining, companies can identify potential consumers by grouping consumers. The purpose of the consumer grouping process is to find out consumer behavior and apply the right marketing strategy so that it can bring benefits to the company. This study discusses how to process data mining from consumer data at TB 80, which is a company engaged in the field of matrial, selling material and aims to find potential consumers who are expected to make managerial decisions to increase revenue. The data mining process begins with the process of preprocessing data (selection, clending and transformation) then in the clustering stage using the K-Means algorithm by determining the number of clusters. The clustering results of algorithm K-Means are used to group consumers and form consumer classes based on Frequency and Monetary attributes.