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The single housing industry is currently experiencing a continuous expansion in demand for housing. Addressing the needs of different customer groups is the key to increasing the rate of sales conversion. The objective of this research is to propose a two-stage single house customer journey analytical model that consists of two stages. The first stage concerns the customer journey between registration and reservation process and the second one identifies the customer loyalty from reservation to transfer stage. The four classification data mining techniques have experimented. The experiments include the accuracy and F-Measure in comparison and also perform the statistical testing. The Artificial Neural Network was the most accurate model for both stages. This model analyzes the probability of the customer progressing through the stages to the conclusion of purchase by learning the customer’s characteristics and the factors involved in the customer's decision. The model displays the reservation and transfer result for customers who have achieved the respective reservation and transference steps according to their registration profile. Experiments showed that the proposed two-stage models could predict customer loyalty, thereby enhancing relationship management between customers and organizations. It also confers a competitive advantage within the industry.
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