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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.
| Autorius: | Goran Klepac, Robert Kopal, Leo Mr¿i¿, |
| Leidėjas: | Information Science Reference |
| Išleidimo metai: | 2014 |
| Knygos puslapių skaičius: | 328 |
| ISBN-10: | 1466662883 |
| ISBN-13: | 9781466662889 |
| Formatas: | 260 x 183 x 22 mm. Knyga kietu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Developing Churn Models Using Data Mining Techniques and Social Network Analysis“
