Due to the increasing demand for high-speed processing of large amounts of data in the business world, data mining is becoming widely used within the different types of CRM systems. CRM systems are complemented with recommender systems that both customers and salespeople use for selecting various actions within the system. In this thesis we reviewed the existing techniques for recommendation systems and updated the CRM system i.e. an online store with a recommendation system for customers and salespeople. For salespeople we are using an algorithm Apriori which formed the association rules between products compared to previous customers' orders. Association rules have proved to be useless, because there is no close links between products. For customers, we are using ID3 algorithm to built a recommendation tree to recommend products which may be of interest to them. We have built two trees based on the history of online shop visits. Visits data were obtained from Google Analytics system.
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