MIT Sloan Website




STORE
Search   
 
Home View Cart Check Out Contact Us Help/FAQs

Business Ethics and Public Policy
Corporate Strategy
Financial Management
Human Resources
Global Business
Leadership
Information Systems
Technology and Innovation
Managerial Economics
Marketing
Operations
Service and Quality
Miscellaneous
Back Issues
Sustainability
Collections
The Prediction Lover's Handbook
By Thomas H. Davenport and Jeanne G. Harris
Winter 2009
Reprint 50208
Volume 50, Number 2, pages 32-34, 3 pages
Primary Topic: Marketing
Secondary Topic: Technology and Innovation

Summary

When picking assessment tools to inform better decisions about future paths, executives are faced with a wide variety of options -- some of which are well established, while others are in early stages of development. The authors provide an insider's guide to prediction and recommendation techniques and technologies. They cover prediction tools including attributized Bayesian analysis, biological responses analysis, cluster analysis, collaborative filtering, content-based filtering/decision trees, neural network analysis, prediction (or opinion) markets, regression analysis, social network-based recommendations and textual analytics. With each potential tool, they briefly describe the technique, who uses it and for what purpose, its strengths and weaknesses, and its future prospects as a prediction tool. Finally, the authors offer up an indication of the best time in the decision process to begin using the tool.

OR

Includes one pdf to copy from.
Pricing is based on # of
copies made.

Info on pricing and academic discounts.


 
 
Copyright © Massachusetts Institute of Technology
1977-2009. All rights reserved.
877-727-7170, mitsmr@pubservice.com