No Cover Image

Journal article 193 views 46 downloads

A novel response model and target selection method with applications to marketing

Yuzhi Cai Orcid Logo

Australian & New Zealand Journal of Statistics, Volume: 66, Issue: 1, Pages: 48 - 76

Swansea University Author: Yuzhi Cai Orcid Logo

  • 65339.VOR.pdf

    PDF | Version of Record

    This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

    Download (1.29MB)

Check full text

DOI (Published version): 10.1111/anzs.12406

Abstract

Response models used in marketing are not always constructed for later marketing optimisation, which often results in unsatisfactory results in target selection for future marketing activities. To solve this problem, we develop a new binary response model and a new marketing target selection method....

Full description

Published in: Australian & New Zealand Journal of Statistics
ISSN: 1369-1473 1467-842X
Published: Wiley 2024
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa65339
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract: Response models used in marketing are not always constructed for later marketing optimisation, which often results in unsatisfactory results in target selection for future marketing activities. To solve this problem, we develop a new binary response model and a new marketing target selection method. The proposed model can predict multiple propensity scores per customer through customer-specific propensity score distributions, which is not possible with existing response models, filling a gap in the literature. The target selection method can determine the best propensity scores from those predicted by the proposed model and use them to select customers for further marketing activities. Our simulation results and application to real marketing data confirm that the performance of the proposed model in target selection is significantly better than that of the existing models, including some popular machine learning methods, which indicate that our method can be very useful in practice.
Keywords: marketing; propensity score; quantile function; response model; target selection.
College: Faculty of Humanities and Social Sciences
Funders: Swansea University
Issue: 1
Start Page: 48
End Page: 76