Telecommunication data has provided new opportunities for both businesses and academia to analyze subscribers' behavioral patterns. Recently, there have been many changes in this industry, i.e., lessening of market regulations/restrictions in exchange for greater participation. New services, emerging technologies, and competitive offerings are factors causing customers to move to different companies. In this work, we intend to develop a logistic regression model tailored for a telecommunication company in Macau by forecasting potential subscribers intending to leave their current services. To implement such prediction we should assign a probability value to subscribers, based on a relationship between customers' historical data and their future behavioral pattern. Then customers with the highest propensity to leave can receive various marketing offers. To improve the analysis result we have utilized a combination of two datasets. Our experimental results show how such data aggregation can improve the model accuracy.