"Data science should become less scientific and more practical - and this is what this book strives for. It gives marketing experts the necessary background to apply predictive analytics in CRM and improve business outcomes with techniques like cross-selling and customer segmentation." - Dr. Ingo Mierswa, Founder & CTO of, RapidMiner
"Effective CRM using Predictive Analytics demonstrates how organizations can become customer centric, while driving profitable revenue. The book is a great introduction to the predictive analytics technology that provides deep customer insight and data driven recommended actions. It is full of practical examples describing a variety of use cases, what data is required and what models are deployed." - Vivian Braun, WW Predictive Customer Intelligence Marketing Lead, IBM Analytics
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 I provides a methodological roadmap, covering both business and 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 and effective customer segmentation schemes.
In Part II, some of the most useful data mining algorithms are explained in a simple and comprehensive way for business users with no technical expertise.
Part III is packed with real-world case studies that 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 to serve as templates for developing similar applications.
Key Features:
• Includes numerous real-world case studies that are presented step by step, demystifying the usage of data mining models and clarifying all the methodological issues.
• Topics are presented with the use of three leading data mining tools: IBM SPSS Modeler, RapidMiner, and Data Mining for Excel.
• Accompanied by a website featuring material from each case study, including datasets and relevant code.
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.
