A customer segmentation oriented integrated product recommendation system: An application in fashion retail sector

Globalization and increasing competitive conditions push today's companies to find to serve new solutions to their existing and potential customers. Especially for the companies which is active in retail sector and both serving in online and offline channels, serving right products in right place and time to the customers is one of the most important problems. These companies struggle developing and using efficient product recommendation systems via using data mining techniques on their all channels to solve the problem that mentioned above. With this motivation, this work offers an integrated product recommendation system which combines various data mining techniques. Offered approach includes these five steps: analysis of existing data of customers, segmentation of the customers, adding location parameter to the segmentation data, an association rule mining with this segmented data and finally creating the right product recommendations to the customer. In this way, this model aims to create product recommendations which is more accurate and fitted with customer expectation. Offered approach applied on data which obtained from a fashion retail company and results are evaluated. The data includes company's real e-commerce and customer data.