Developing technologies in customer analytics provide several opportunities to retailers. Analyzing customer movements in the stores as a part of customer analytics can reveal various shopping behaviors. It facilitates to understand better customers' visit purposes. This study applies four sequential mining algorithms, CMRules, CMDeo, ERMiner, and RuleGrowth, to analyze the visit purposes of customers in a supermarket. Moreover, it compares variations among different visits belonging to the same customers. This study concludes three main results. First, it indicates that customers prefer visiting the supermarket not only for their specific needs but also for all their needs at every visit. Second, the ERMiner algorithm is faster than the other algorithms. Third, customers who visit {Construction, Kitchen} and {Sanitary ware, Garden} bought at least one product with a high probability. Moreover, this study describes the concept of interesting rule, which has a lower support value and higher confidence value. Customers can visit the supermarket for various purposes resulting in different interesting rules. As an interesting rule in the second visit, purchased customers visited Construction, Garden and Kitchen aisles before leaving the supermarket whereas this rule did not appear in the first visits. Customers visited the Construction aisle more after they visited the Entrance and Ironmongery aisles in their second visit.