Recently, most companies are utilizing data from various sources to solve complex business tasks that arise from management activities. In order to establish such data based decision making as a culture throughout the company, we are also focusing on building a data culture. In addition, as the legal basis for the use of personal information has been established due to the revision of Personal Information Protection Act, the formation of data alliances is active, focusing on the finance, communications, and distribution sectors. Major companies are using it to establish corporate strategies, support decision making, and create new customer value by combining data from various sources. However, other industrial sectors other than the financial and public sectors, which do not have legal grounds to request data transmission to third parties, lack visible results through data convergence between different industries. In this study, we propose a method for converging and utilizing heterogeneous industry data through customer life stage estimation as part of customer segmentation. The number of customers who use all the services of companies within the existing data alliance is limited, and there are differences among customers depending on the characteristics of the industry and company, making life stage estimation difficult. To minimize the differences in customer distribution according to these company characteristics, the propensity score matching method was used, and through this, it was possible to estimate the life stage of customers even if they did not use all of the services of companies within the alliance.