Skip to main content
Mobile
  • Finance, Accounting & Economics
  • Global Business Management
  • Management, Leadership & Organisation
  • Marketing & Sales
  • Strategy
  • Technology & Operations
HS Talks HS Talks
Subjects  
Search
  • Notifications
    Notifications

    No current notifications.

  • User
    Welcome Guest
    You have Limited Access The Business & Management Collection
    Login
    Get Assistance
    Login
    Forgot your password?
    Login via your organisation
    Login via Organisation
    Get Assistance
Finance, Accounting & Economics
Global Business Management
Management, Leadership & Organisation
Marketing & Sales
Strategy
Technology & Operations
Research paper

The benefits of Shapley-value in key-driver analysis

Marco Vriens, Chad Vidden and Nathan Bosch
Applied Marketing Analytics: The Peer-Reviewed Journal, 6 (3), 269-278 (2021)
https://doi.org/10.69554/KDDQ4647

Abstract

Linear (and other types of) regression are often used in what is referred to as ‘driver modelling’ in customer satisfaction studies. The goal of such research is often to determine the relative importance of various sub-components of the product or service in terms of predicting and explaining overall satisfaction. Driver modelling can also be used to determine the drivers of value, likelihood to recommend, etc. A common problem is that the independent variables are correlated, making it difficult to get a good estimate of the importance of the ‘drivers’. This problem is well known under conditions of severe multicollinearity, and alternatives like the Shapley-value approach have been proposed to mitigate this issue. This paper shows that Shapley-value may even have benefits in conditions of mild collinearity. The study compares linear regression, random forests and gradient boosting with the Shapley-value approach to regression and shows that the results are more consistent with bivariate correlations. However, Shapley-value regression does result in a small decrease in k-fold validation results.

Keywords: driver modelling; regression; Shapley-value; customer satisfaction; random forests; gradient boosting

The full article is available to subscribers to the journal.

Already a subscriber? Login or review other options.

Author's Biography

Marco Vriens has a PhD in marketing analytics and is a recognised expert in applied analytics. He led analytics teams for Microsoft, GE and supplier firms. Marco is the author of three books: ‘The Insights Advantage: Knowing How to Win’ (2012), ‘Handbook of Marketing Research’ (2006) and ‘Conjoint Analysis in Marketing’ (1995). Marco has been published in academic and industry journals and has won several best paper awards including the David K. Hardin Award.

Chad Vidden has a PhD in Applied Mathematics, with expertise in computational mathematics, data science and machine learning. He is currently an assistant professor at the University of Wisconsin – La Crosse, where he leads a data science and mathematical modelling research group that collaborates with local companies.

Nathan Bosch is a master’s student in machine learning at the KTH Royal Institute of Technology in Stockholm. He has a bachelor’s degree in artificial intelligence from the University of Groningen. His research interests include system log file analysis, predictive maintenance and business analytics. He has also worked in applied machine learning.

Citation

Vriens, Marco, Vidden, Chad and Bosch, Nathan (2021, January 1). The benefits of Shapley-value in key-driver analysis. In the Applied Marketing Analytics: The Peer-Reviewed Journal, Volume 6, Issue 3. https://doi.org/10.69554/KDDQ4647.

Options

  • Download PDF
  • Share this page
    Share This Article
    Messaging
    • Outlook
    • Gmail
    • Yahoo!
    • WhatsApp
    Social
    • Facebook
    • X
    • LinkedIn
    • VKontakte
    Permalink
cover image, Applied Marketing Analytics: The Peer-Reviewed Journal
Applied Marketing Analytics: The Peer-Reviewed Journal
Volume 6 / Issue 3
© Henry Stewart
Publications LLP

The Business & Management Collection

  • ISSN: 2059-7177
  • Contact Us
  • Request Free Trial
  • Recommend to Your Librarian
  • Subscription Information
  • Match Content
  • Share This Collection
  • Embed Options
  • View Quick Start Guide
  • Accessibility

Categories

  • Finance, Accounting & Economics
  • Global Business Management
  • Management, Leadership & Organisation
  • Marketing & Sales
  • Strategy
  • Technology & Operations

Librarian Information

  • General Information
  • MARC Records
  • Discovery Services
  • Onsite & Offsite Access
  • Federated (Shibboleth) Access
  • Usage Statistics
  • Promotional Materials
  • Testimonials

About Us

  • About HSTalks
  • Editors
  • Contact Information
  • About the Journals

HSTalks Home

Follow Us On:

HS Talks
  • Site Requirements
  • Copyright & Permissions
  • Terms
  • Privacy
  • Sitemap
© Copyright Henry Stewart Talks Ltd

Personal Account Required

To use this function, you need to be signed in with a personal account.

If you already have a personal account, please login here.

Otherwise you may sign up now for a personal account.

HS Talks

Cookies and Privacy

We use cookies, and similar tools, to improve the way this site functions, to track browsing patterns and enable marketing. For more information read our cookie policy and privacy policy.

Cookie Settings

How Cookies Are Used

Cookies are of the following types:

  • Essential to make the site function.
  • Used to analyse and improve visitor experience.

For more information see our Cookie Policy.

Some types of cookies can be disabled by you but doing so may adversely affect functionality. Please see below:

(always on)

If you block these cookies or set alerts in your browser parts of the website will not work.

Cookies that provide enhanced functionality and personalisation. If not allowed functionality may be impaired.

Cookies that count and track visits and on website activity enabling us to organise the website to optimise the experience of users. They may be blocked without immediate adverse effect.