A cost-efficient framework for finding prospective customers based on reverse skyline queries

Analyzing customers' information for marketing insights is critical for the company to remain invincible in increasingly fierce market competition. Rank-based and dominance-based customer selections are widely used for the market analysis to find prospective customers. However, rank-based solutions required companies to define weight vectors, which is not practical in real applications. Dominance-based approaches (e.g., reverse skyline queries) had problems of a high overhead and the poor progressiveness. In this paper, we propose a cost-efficient framework to find prospective customers for a target product. We first target on the most prospective customers for whom no better product exists compared with the target product. We formulate the problem based on reverse skyline queries, and propose a new algorithm that significantly reduces the query cost by pruning unqualified customers without any false positive and identifies reverse skyline points as early as possible based on decision region and effect region. We then further target on arbitrary k prospective customers and formulate the problem as top-k reverse skyline (top-k RS) queries. We extend the notion of reverse skyline to reverse skyline order in order to support arbitrary k and group-based promotions. We evaluate our framework with extensive experiments, and our results demonstrate that our framework has the promising results for reverse skyline queries, and can efficiently support top-k RS queries.