Effective CRM using Predictive Analytics
Chorianopoulos, Antonios
Venduto da Textbooks_Source, Columbia, MO, U.S.A.
Venditore AbeBooks dal 10 novembre 2017
Usato - Rilegato
Condizione: Good
Quantità: 1 disponibili
Aggiungere al carrelloVenduto da Textbooks_Source, Columbia, MO, U.S.A.
Venditore AbeBooks dal 10 novembre 2017
Condizione: Good
Quantità: 1 disponibili
Aggiungere al carrelloShips in a BOX from Central Missouri! May not include working access code. Will not include dust jacket. Has used sticker(s) and some writing or highlighting. UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes).
Codice articolo 001967124U
A step-by-step guide to data mining applications in CRM.
Following a handbook approach, this book bridges the gap between analytics and their use in everyday marketing, providing guidance on solving real business problems using data mining techniques.
The book is organized into three parts. Part one provides a methodological roadmap, covering both the business and the technical aspects. The data mining process is presented in detail along with specific guidelines for the development of optimized acquisition, cross/ deep/ up selling and retention campaigns, as well as effective customer segmentation schemes.
In part two, some of the most useful data mining algorithms are explained in a simple and comprehensive way for business users with no technical expertise.
Part three is packed with real world case studies which employ the use of three leading data mining tools: IBM SPSS Modeler, RapidMiner and Data Mining for Excel. Case studies from industries including banking, retail and telecommunications are presented in detail so as to serve as templates for developing similar applications.
Key Features:
Combining data mining and business knowledge, this practical book provides all the necessary information for designing, setting up, executing and deploying data mining techniques in CRM.
Effective CRM using Predictive Analytics will benefit data mining practitioners and consultants, data analysts, statisticians, and CRM officers. The book will also be useful to academics and students interested in applied data mining.
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