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Authors: Gary L. Lilien, Arvind Rangaswamy, and Arnaud De Bruyn
Publisher: DecisionPro, Inc. (2017)
ISBN-10: 0985764821
ISBN-13: 978-0985764821
Available in select countries via Amazon (print and Kindle edition) and Google Play store (ebook)
| Printed Edition from Amazon | Kindle Edition from Amazon | Ebook Edition from Google Play |
The 21st century business environment demands more analysis and rigor in marketing decision making. Increasingly, marketing decision making resembles design engineering—putting together concepts, data, analyses, and simulations to learn about the marketplace and to design effective marketing plans. While many view traditional marketing as art and some view it as science, the new marketing increasingly looks like engineering (that is, combining art and science to solve specific problems).
We offer an accessible overview of the most widely used marketing engineering concepts and tools and show how they drive the collection of the right data and information to perform the right analyses to make better marketing plans, better product designs, and better marketing decisions.
We have designed this book primarily for the business school student or marketing manager, who, with minimal background and technical training, must understand and employ the basic tools and models associated with Marketing Engineering.
The interested reader can go beyond the conceptual material in this book and learn how to apply these concepts using the Microsoft Excel-based software tools. That material, in concert with the descriptions here will both inform and help marketing engineers translate concepts into context-specific operational decisions and actions.
Technical Notes: The Technical Notes are designed as an additional reference to the Principles of Marketing Engineering. The Notes provide the analytical background behind the theories discussed in the text. These notes are available for free download on the website.
Subject Areas
The Principles of Marketing Engineering includes the following topics:- Market Response Models
- Customer Value Assessment and Valuing Customers
- Segmentation and Targeting
- Positioning Analysis
- Forecasting
- New Product and Service Design
- Digital Marketing
- The Marketing Mix