Visual analysis of relevant features in Customer Loyalty Improvement Recommendation

This chapter describes a practical area of application of decision reducts to a real-life business problem. It presents a feature selection (attribute reduction) methodology based on the decision reducts theory, which is supported by a designed and developed visualization system. The chapter overviews an application area Customer Loyalty Improvement Recommendation, which has become a very popular and important topic area in today’s business decision problems. The chapter describes a real-world dataset, which consists of about 400,000 surveys on customer satisfaction collected in years 2011-2016. Major machine learning techniques used to develop knowledge-based recommender system, such as decision reducts, classification, clustering, action rules, are described. Next, visualization techniques used for the implemented interactive system are presented. The experimental results on the customer dataset illustrate the correlation between classification features and the decision feature called the promoter score and how these help to understand changes in customer sentiment.