User Friendly Net Promoter Score Based Recommender System for Boosting Business Revenue

Duration: Half day

Short Description: Popular industry standard for measuring customer satisfaction is called Net Promoter Score (NPS). It is computed as %Promoters -%Detractors, where percentage is understood as the total number of promoters/detractors divided by the total number of surveys. But, most executives would like to know not only the changes of that score, but also why the score moved up or down. More helpful and insightful is to look beyond the surface level of customer responses and dive into the entire anatomy of their feedback. Collected customers feedback includes not only simple score ratings (such 0-10 scale or start ratings) – these are also customers thoughts, feelings, expectations in free-form text. They convey specific messages, for example, that company’s service technician needs more training or invoices came in late and prices were higher than expected. This tutorial will present the entire process of building a user friendly NPS-based Recommender System for boosting business revenue taking into account a large variety of data collected by telephone surveys on customer satisfaction. The system has hierarchical structure and is driven by actionable knowledge hidden in customer surveys.

Lecturer

Zbigniew Ras Dr. Zbigniew Ras is a professor of computer science in the College of Computing and Informatics at the University of North Carolina, Charlotte. He also holds professorship position at the Polish-Japanese Academy of Information Technology as well as in the Institute of Computer Science at Warsaw University of Technology, both in Poland. His PhD degree is from University of Warsaw and his habilitation degree from the Polish Academy of Sciences. In 2012, he was awarded national professorship title by the President of Poland. Dr. Ras areas of specialization include Knowledge Discovery and Data Mining, Recommender Systems, Business Analytics, Health Informatics, Music Information Retrieval, and Flexible Query Answering. He is Editor-in-Chief of the Journal of Intelligent Information Systems (Springer), Editor-in Chief of the International Journal of Social Network Mining (IJSNM), Senior Editor of International Journal of Data Mining, Modelling and Management (InderScience Publishers), and he served as the Deputy Editor-in-Chief of Fundamenta Informaticae Journal (IOS Press), from 1994 till 2010. He is the author of more than 350 publications and the editor of more than 40 books published by Springer and North Holland. He has received many awards at the University of North Carolina including the Harshini V. de Silva Graduate Mentor Award, 2009; Finalist of the Bank of America Award for Teaching Excellence, 2008; the COIT Graduate Faculty Excellence in Teaching Award, 2003; and the Alcoa Foundation Outstanding Faculty Award, 1999-2000. He received competitive grants and contracts from NSF, DOD/ARO, ONR, ORNL, SAS, DOE, IBM, Committee for Scientific Research (Poland), and AMVIS (Czech Republic).