Olivia Parr Rud – Data Mining Cookbook

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Author: Olivia Parr Rud

  • Trading books are a form of accounting ledger that contains records of all tradeable financial assets of a bank.
  • Trading books are subject to gains and losses that affect the financial institution directly.
  • Losses in a bank’s trading book can have a cascading effect on the global economy, such as those that occurred during the 2008 financial crisis.

Data Mining Cookbook

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Author Information

OLIVIA PARR RUD (Olivia@datasquare.com) is Executive Vice President of Data Square, LLC, a leading database marketing consulting firm. She has over 22 years’ experience in data mining, predictive modeling, and segmentation for a variety of industries, including credit card, insurance, high tech, telecommunications, and catalog industries. She provides analysis and solutions for her clients in the areas of acquisition, retention, risk, and overall profitability for direct mail, telemarketing, broadcast marketing, and the Internet.

Description

Increase profits and reduce costs by utilizing this collection of models of the most commonly asked data mining questions

In order to find new ways to improve customer sales and support, and as well as manage risk, business managers must be able to mine company databases. This book provides a step-by-step guide to creating and implementing models of the most commonly asked data mining questions. Readers will learn how to prepare data to mine, and develop accurate data mining questions. The author, who has over ten years of data mining experience, also provides actual tested models of specific data mining questions for marketing, sales, customer service and retention, and risk management. A CD-ROM, sold separately, provides these models for reader use.

Table of Contents

Acknowledgments.

Foreword.

Introduction.

About the Author.

About the Contributors.

PLANNING THE MENU.

Setting the Objective.

Selecting the Data Sources.

THE COOKING DEMONSTRATION.

Preparing the Data for Modeling.

Selecting and Transforming the Variables.

Processing and Evaluating the Model.

Validating the Model.

Implementing and Maintaining the Model.

RECIPES FOR EVERY OCCASION.

Understanding Your Customer: Profiling and Segmentation.

Targeting New Prospects: Modeling Response.

Avoiding High-Risk Customers: Modeling Risk.

Retaining Profitable Customers: Modeling Churn.

Targeting Profitable Customers: Modeling Lifetime Value.

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