{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T09:53:43Z","timestamp":1762250023350,"version":"build-2065373602"},"reference-count":19,"publisher":"Institute for Operations Research and the Management Sciences (INFORMS)","issue":"11","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Management Science"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:p>We propose an approach for determining the sample size required when using an experiment to train and certify a targeting policy. Calculating the rate at which the performance of a targeting model improves with additional training data is a complex problem. We address this challenge by assuming that customers are grouped into segments that capture relevant information about their responsiveness to the firm\u2019s marketing actions. We consider two problem formulations. The first formulation identifies the sample size required to train a targeting policy and certify that its expected performance exceeds a predefined threshold. The second formulation identifies the sample size required to train a targeting policy and certify that it outperforms a baseline in an out-of-sample statistical test. We establish theoretical properties of these problems, based on which we propose computationally efficient algorithms for optimal sample size calculations. We illustrate our algorithms and analysis using data from a luxury fashion retailer.<\/jats:p>\n                  <jats:p>This paper was accepted by David Simchi-Levi, marketing.<\/jats:p>\n                  <jats:p>Supplemental Material: The online appendix and data files are available at https:\/\/doi.org\/10.1287\/mnsc.2022.02947 .<\/jats:p>","DOI":"10.1287\/mnsc.2022.02947","type":"journal-article","created":{"date-parts":[[2025,3,24]],"date-time":"2025-03-24T13:59:01Z","timestamp":1742824741000},"page":"9503-9522","source":"Crossref","is-referenced-by-count":0,"title":["A Sample Size Calculation for Training and Certifying Targeting Policies"],"prefix":"10.1287","volume":"71","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2758-0116","authenticated-orcid":false,"given":"Duncan","family":"Simester","sequence":"first","affiliation":[{"name":"Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5431-2136","authenticated-orcid":false,"given":"Artem","family":"Timoshenko","sequence":"additional","affiliation":[{"name":"Kellogg School of Management, Northwestern University, Evanston, Illinois 60208"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4662-7787","authenticated-orcid":false,"given":"Spyros I.","family":"Zoumpoulis","sequence":"additional","affiliation":[{"name":"Decision Sciences, INSEAD, 77305 Fontainebleau, France"}]}],"member":"109","reference":[{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.2017.2920"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1177\/002224377901600204"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1287\/mksc.2022.1420"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1287\/mksc.2019.1194"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.2021.3969"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.1177\/002224378101800115"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1287\/mksc.1080.0459"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.1093\/qje\/qjv023"},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.1111\/j.1468-0262.2004.00530.x"},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.psych.59.103006.093735"},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.1002\/0470033312"},{"key":"B20","doi-asserted-by":"publisher","DOI":"10.1287\/mksc.2020.1235"},{"key":"B21","doi-asserted-by":"publisher","DOI":"10.1287\/mksc.2016.1023"},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.2019.3308"},{"key":"B23","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.2019.3379"},{"volume-title":"Reinforcement Learning: An Introduction","year":"2018","author":"Sutton RS","key":"B24"},{"key":"B25","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconom.2011.06.013"},{"key":"B27","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2017.1319839"},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.1287\/mksc.1040.0055"}],"container-title":["Management Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/pubsonline.informs.org\/doi\/pdf\/10.1287\/mnsc.2022.02947","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T09:46:46Z","timestamp":1762249606000},"score":1,"resource":{"primary":{"URL":"https:\/\/pubsonline.informs.org\/doi\/10.1287\/mnsc.2022.02947"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11]]},"references-count":19,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["10.1287\/mnsc.2022.02947"],"URL":"https:\/\/doi.org\/10.1287\/mnsc.2022.02947","relation":{},"ISSN":["0025-1909","1526-5501"],"issn-type":[{"type":"print","value":"0025-1909"},{"type":"electronic","value":"1526-5501"}],"subject":[],"published":{"date-parts":[[2025,11]]}}}