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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

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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....

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Published in: Australian & New Zealand Journal of Statistics
ISSN: 1369-1473 1467-842X
Published: Wiley 2024
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URI: https://cronfa.swan.ac.uk/Record/cronfa65339
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first_indexed 2023-12-18T13:28:00Z
last_indexed 2023-12-18T13:28:00Z
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spelling v2 65339 2023-12-18 A novel response model and target selection method with applications to marketing eff7b8626ab4cc6428eef52516fda7d6 0000-0003-3509-9787 Yuzhi Cai Yuzhi Cai true false 2023-12-18 BAF 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. Journal Article Australian & New Zealand Journal of Statistics 66 1 48 76 Wiley 1369-1473 1467-842X marketing; propensity score; quantile function; response model; target selection. 1 3 2024 2024-03-01 10.1111/anzs.12406 http://dx.doi.org/10.1111/anzs.12406 COLLEGE NANME Accounting and Finance COLLEGE CODE BAF Swansea University SU Library paid the OA fee (TA Institutional Deal) Swansea University 2024-03-25T11:52:33.1288895 2023-12-18T13:25:53.7658854 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Yuzhi Cai 0000-0003-3509-9787 1 65339__29822__afe0bb4c9eb64328b47c05924d35d968.pdf 65339.VOR.pdf 2024-03-25T11:51:09.7774155 Output 1353706 application/pdf Version of Record true 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. true eng http://creativecommons.org/licenses/by/4.0/
title A novel response model and target selection method with applications to marketing
spellingShingle A novel response model and target selection method with applications to marketing
Yuzhi Cai
title_short A novel response model and target selection method with applications to marketing
title_full A novel response model and target selection method with applications to marketing
title_fullStr A novel response model and target selection method with applications to marketing
title_full_unstemmed A novel response model and target selection method with applications to marketing
title_sort A novel response model and target selection method with applications to marketing
author_id_str_mv eff7b8626ab4cc6428eef52516fda7d6
author_id_fullname_str_mv eff7b8626ab4cc6428eef52516fda7d6_***_Yuzhi Cai
author Yuzhi Cai
author2 Yuzhi Cai
format Journal article
container_title Australian & New Zealand Journal of Statistics
container_volume 66
container_issue 1
container_start_page 48
publishDate 2024
institution Swansea University
issn 1369-1473
1467-842X
doi_str_mv 10.1111/anzs.12406
publisher Wiley
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Accounting and Finance{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Accounting and Finance
url http://dx.doi.org/10.1111/anzs.12406
document_store_str 1
active_str 0
description 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.
published_date 2024-03-01T11:52:30Z
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score 11.036706

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