{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:55:33Z","timestamp":1760237733160,"version":"build-2065373602"},"reference-count":27,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T00:00:00Z","timestamp":1654041600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"marine characteristic cross-campus research project","award":["111K02"],"award-info":[{"award-number":["111K02"]}]},{"name":"ANT INTERACTION TECH Co., Ltd.","award":["111K02"],"award-info":[{"award-number":["111K02"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Axioms"],"abstract":"<jats:p>The recommendation of the optimal selling rules for any product or service is challenging, owing to the complexity of the customer\u2019s behavior and the competitiveness existing in the telecom retail industry. This study proposes a recommendation model for selling rules that utilizes a hybrid decision-making approach based on K-means and the C5.0 decision tree to analyze the historical sales information of telecom retailers. To evaluate the efficacy of the proposed recommendation model, it was used to analyze original data from a case company. The results indicated that the proposed hybrid decision-making approach resulted in sales content with a high gross profit and high agreement rates. The experimental results show each cluster that can be used to identify rules for the combination of good tariff items in different tariff ranges. Rules for the recommendation of special tariffs are also established to assist salespeople.<\/jats:p>","DOI":"10.3390\/axioms11060265","type":"journal-article","created":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T03:33:18Z","timestamp":1654054398000},"page":"265","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Recommendation Model for Selling Rules in the Telecom Retail Industry"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9728-9653","authenticated-orcid":false,"given":"Tsung-Ying","family":"Ou","sequence":"first","affiliation":[{"name":"Department of Marketing and Distribution Management, National Kaohsiung University of Science and Technology, Kaohsiung 824, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wen-Lung","family":"Tsai","sequence":"additional","affiliation":[{"name":"Department of Information Management, Asia Eastern University of Science and Technology, New Taipei 220, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi-Chen","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Seafood Science, National Kaohsiung University of Science and Technology, Kaohsiung 811, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tien-Hsiang","family":"Chang","sequence":"additional","affiliation":[{"name":"Department of Intelligent Commerce, National Kaohsiung University of Science and Technology, Kaohsiung 811, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7395-556X","authenticated-orcid":false,"given":"Shih-Hsiung","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Intelligent Commerce, National Kaohsiung University of Science and Technology, Kaohsiung 811, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fen-Fen","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Health Care Administration, Asia Eastern University of Science and Technology, New Taipei 220, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.ijinfomgt.2018.10.005","article-title":"A big data analytics model for customer churn prediction in the retiree segment","volume":"48","author":"Shirazi","year":"2019","journal-title":"Int. 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