Contemporary information and communication technology facilitates data acquisition and provides affordable storage for vast quantities of data. Business world resides on transactional data, and the largest part of transactional data comes from recording interactions with customers. The true potential of transactional databases is not in raw transactional data, but in implicit regularities that underlie the data. In the modern world of global competition, discovering, understanding and exploiting patterns in customer characteristics and behavior has an important role in obtaining and maintaining substantial market share. Data mining techniques enable automatic extraction of implicit regularities and hidden relationships that exist in large databases. This thesis explicates fundamental principles and ideas that form the basis of data mining techniques, explains essential problems in their practical application, and offers standard solutions to these problems. The most popular techniques for each phase of data mining process are also described, along with their properties and important differences. Particularly, the thesis explores adequacy, efficiency and usefulness of practical application of the described data mining techniques on real-world business problems. As a typical example, a problem of constructing customer profiles is taken from the insurance business. Conducted analysis and experiments resulted in a specific combination of probabilistic and descriptive modeling techniques, primarily suited for problems that exhibit a high degree of undeterministic behavior. Derived from these results, a computer application was developed, implementing data mining techniques in the context of target problem type.