Churn prediction, recognition, and mitigation have become essential topics in various industries. As a means for forecasting and managing risk, further research in this field can greatly assist companies in making informed decisions based on future possible scenarios. Developing Churn Models Using Data Mining Techniques and Social Network Analysis provides an in-depth analysis of attrition modeling relevant to business planning and management. Through its insightful and detailed explanation of best practices, tools, and theory surrounding churn prediction and the integration of analytics tools, this publication is especially relevant to managers, data specialists, business analysts, academicians, and upper-level students.
Goran Klepac, PhD, University College Professor works as a head of Strategic unit in Sector of credit risk in Raiffeisenbank Austria Inc, Croatia, Europe. In several universities in Croatia, he lectures subjects in domain of data mining, predictive analytics, decision support system, banking risk, risk evaluation models, expert system, database marketing and business intelligence. As a team leader, he successfully finished many data mining projects in different domains like retail, finance, insurance, hospitality, telecommunications, and productions. He is an author/coauthor of several books published in Croatian and English in domain of data mining. www.goranklepac.com
Robert Kopal, PhD, Dean of University College for Law and Finance Effectus in Zagreb, Croatia, Europe; University College Professor; lecturer at several university colleges in Croatia, and at CROMA (Croatian Managers' and Entrepreneurs' Association) EduCare Program; author & co-author of six books (on competitive intelligence analysis, game theory, etc.), numerous chapters in books of various authors, and more than forty scientific and professional papers; workshop manager and teacher at more than hundred business and intelligence analysis workshops; designer of several specialized IT systems; certified trainer in the area of structured intelligence analysis techniques and SW; SCIP and IALEIA member; held presentations at various national and international conferences; participated in and led a number of national and international intelligence analysis projects.
Leo Mric, PhD, University College Professor graduated with a major in insurance; earned MSc degree with a major in business statistics; earned PhD degree in data science all at University of Zagreb, Croatia, Europe. Combining business and technology approach, he has great field experience related to many industries like retail, insurance, finance, ICT, business law and project management. Has relevant top management and consulting experience participating in many projects across the supply chain with focus on retail. Active in conferences and guest lecturer on several university programs related to various aspects of business like management, consumer behavior, data science/data mining and managing business risks. Co-author on several books in area of data science and its appliance in business. Member of the board at Croatian Oracle Users Group, member and mentor at Young Executives Society in Croatia, member of Croatian Association of Court Expert Witness in Croatia.
