Handbook of Marketing Analytics
Show Less

Handbook of Marketing Analytics

Methods and Applications in Marketing Management, Public Policy, and Litigation Support

Edited by Natalie Mizik and Dominique M. Hanssens

Marketing Science contributes significantly to the development and validation of analytical tools with a wide range of applications in business, public policy and litigation support. The Handbook of Marketing Analytics showcases the analytical methods used in marketing and their high-impact real-life applications. Fourteen chapters provide an overview of specific marketing analytic methods in some technical detail and 22 case studies present thorough examples of the use of each method in marketing management, public policy, and litigation support. All contributing authors are recognized authorities in their area of specialty.
Show Summary Details
You do not have access to this content

Chapter 11: Machine learning and marketing

Daria Dzyabura and Hema Yoganarasimhan

Abstract

Machine learning (ML) refers to the study of methods or algorithms designed to learn the underlying patterns in the data and make predictions based on these patterns. A key characteristic of ML techniques is their ability to produce accurate out-of-sample predictions. We review two popular machine-learning methods – decision trees and Support Vector Machines (SVM) in detail.

You are not authenticated to view the full text of this chapter or article.

Elgaronline requires a subscription or purchase to access the full text of books or journals. Please login through your library system or with your personal username and password on the homepage.

Non-subscribers can freely search the site, view abstracts/ extracts and download selected front matter and introductory chapters for personal use.

Your library may not have purchased all subject areas. If you are authenticated and think you should have access to this title, please contact your librarian.


Further information

or login to access all content.