A marketing database contains a basket of items or products. Not all the products hold equal importance in profit earnings. Some of the products are sold frequently while others may sell on an irregular basis. Interestingly, irregular products may also provide high profit. The fundamental reason behind the scenes is the business policies that are followed in retail marketing. Analysis of past data is required to develop business policies for future time. Association rule-based exploration of hidden knowledge solves the problem in some sort. The business policies of one retail organization may differ from others. Business policy making depends on user’s expectations. Therefore, implementation of a common pattern or rule mining strategy remains ineffective in knowledge discovery in many real-world marketing applications. This paper addresses different association rule mining strategies and explores their significance in retail market analysis and promotion. Moreover, the paper highlights the problems in different association rule mining strategies and draws a limelight on the probable solutions of the problems.