Data-driven customer acceptance for attended home delivery
Home delivery services require the attendance of the customer during delivery. Hence, retailers and customers mutually agree on a delivery time window in the booking process. However, when a customer requests a time window, it is not clear how much accepting the ongoing request significantly reduces...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10066988/ https://www.ncbi.nlm.nih.gov/pubmed/37360931 http://dx.doi.org/10.1007/s00291-023-00712-4 |
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author | Köhler, Charlotte Campbell, Ann Melissa Ehmke, Jan Fabian |
author_facet | Köhler, Charlotte Campbell, Ann Melissa Ehmke, Jan Fabian |
author_sort | Köhler, Charlotte |
collection | PubMed |
description | Home delivery services require the attendance of the customer during delivery. Hence, retailers and customers mutually agree on a delivery time window in the booking process. However, when a customer requests a time window, it is not clear how much accepting the ongoing request significantly reduces the availability of time windows for future customers. In this paper, we explore using historical order data to manage scarce delivery capacities efficiently. We propose a sampling-based customer acceptance approach that is fed with different combinations of these data to assess the impact of the current request on route efficiency and the ability to accept future requests. We propose a data-science process to investigate the best use of historical order data in terms of recency and amount of sampling data. We identify features that help to improve the acceptance decision as well as the retailer’s revenue. We demonstrate our approach with large amounts of real historical order data from two cities served by an online grocery in Germany. |
format | Online Article Text |
id | pubmed-10066988 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-100669882023-04-03 Data-driven customer acceptance for attended home delivery Köhler, Charlotte Campbell, Ann Melissa Ehmke, Jan Fabian OR Spectr Original Article Home delivery services require the attendance of the customer during delivery. Hence, retailers and customers mutually agree on a delivery time window in the booking process. However, when a customer requests a time window, it is not clear how much accepting the ongoing request significantly reduces the availability of time windows for future customers. In this paper, we explore using historical order data to manage scarce delivery capacities efficiently. We propose a sampling-based customer acceptance approach that is fed with different combinations of these data to assess the impact of the current request on route efficiency and the ability to accept future requests. We propose a data-science process to investigate the best use of historical order data in terms of recency and amount of sampling data. We identify features that help to improve the acceptance decision as well as the retailer’s revenue. We demonstrate our approach with large amounts of real historical order data from two cities served by an online grocery in Germany. Springer Berlin Heidelberg 2023-04-01 /pmc/articles/PMC10066988/ /pubmed/37360931 http://dx.doi.org/10.1007/s00291-023-00712-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Article Köhler, Charlotte Campbell, Ann Melissa Ehmke, Jan Fabian Data-driven customer acceptance for attended home delivery |
title | Data-driven customer acceptance for attended home delivery |
title_full | Data-driven customer acceptance for attended home delivery |
title_fullStr | Data-driven customer acceptance for attended home delivery |
title_full_unstemmed | Data-driven customer acceptance for attended home delivery |
title_short | Data-driven customer acceptance for attended home delivery |
title_sort | data-driven customer acceptance for attended home delivery |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10066988/ https://www.ncbi.nlm.nih.gov/pubmed/37360931 http://dx.doi.org/10.1007/s00291-023-00712-4 |
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