Purpose: The main purpose of the article was to present the results of the analysis of the after-sales service process using data mining on the example of data gathered in an authorized car service station. As a result of the completed literature review and identification of cognitive gaps, two research questions were formulated (RQ). RQ1: Does the after-sales service meet the parameters of business process category? RQ2: Is the after-sales service characterized by trends or is it seasonal in nature? Design/methodology/approach: The following research methods were used in the study: quantitative bibliographic analysis, systematic literature review, participant observation and statistical methods. Theoretical and empirical study used R programming language and Gretl software. Findings: Basing on relational database designed for the purpose of carrying out the research procedure, the presented results were of: the analysis of the service sales structure, sales dynamics, as well as trend and seasonality analyses. As a result of research procedure, the effects of after-sales service process were presented in terms of quantity and value (amount). In addition, it has been shown that after-sales service should be identified in the business process category. Originality/value: The article uses data mining and R programming language to analyze the effects generated in after-sales service on the example of a complete sample of 13,418 completed repairs carried out in 2013-2018. On the basis of empirical proceedings carried out, the structure of a customer-supplier relationship was recreated in external and internal terms on the example of examined organization. In addition, the possibilities of using data generated from the domain system were characterized and further research directions, as well as application recommendations in the area of after-sales services was presented.