In a growing market with demanding consumers, the retail industry needs decision support tools that reflect emerging practices and integrate key decisions cutting across several of its departments (e.g. marketing and operations). The current tools may not be adequate to sufficiently tackle this kind of integration complexity in relation to pricing in order to satisfy the retailing experience of the customers. Of course, it has to be understood that the retailing experience can differ from one country to another and from one culture to another. Therefore, the off-the-shelve models may not be able to capture the behavior of customers in a particular region. This research aims at developing novel optimization mixed-integer linear/nonlinear formulations with extensive analytical and computational studies based on the experience of a large retailer in Qatar. The model addresses a product lines of substitutable items serving the same customer need but differ by secondary attributes. The research done in this project demonstrates that there is added value in identifying the shopping characteristics of the consumer base and well studying the consumer behavior in order to develop the appropriate retail analytics solution. The research is supported by a grant obtained through the NPRP project of Qatar Foundation.


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