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Given the vast amount of data generated by customers' online and offline purchases, many organizations today are turning to data analytics to help design their direct marketing campaigns and introduce personalized promotions for customers. Data analytics allows companies to implement more effective market segmentation strategies, customize promotional offers, allocate marketing resources efficiently, and improve customer relationship management. The implementation of such strategies is often hampered by limited budgets and the ever-changing priorities and goals of marketing campaigns. This paper suggests and demonstrates the use of a goal programming approach to determine which customer segments should be targeted to achieve profit maximization given various priorities and budget constraints for a hypothetical direct marketing campaign. Using historical data, the proposed model identifies customer segments based on the classic RFM model - ie, recency, frequency, and monetary value profiles. Then, considering different marketing priorities, the goal programming model helps identify the profile segments most worthy of pursuit. Real marketing data is used to illustrate the proposed approach.