{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T01:57:33Z","timestamp":1787882253071,"version":"build-2784847793"},"reference-count":0,"publisher":"Sciedu Press","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AIR"],"abstract":"<jats:p>The automotive industry is in the strongest competition ever, as this sector gets disrupted by new arising competitors. Providing\u00a0services to maximum customer satisfaction will be one of the most crucial competitive advantages in the future. Around 1\u00a0Terabyte of objective data is created every hour today. This volume will significantly grow in the future by the increasing numberof connected services within the automotive industry. However, customer satisfaction determination is solely based on subjective\u00a0questionnaires today without taking the vast amount of objective sensor and service process data into account. This work presents\u00a0an industrial application that fills this lack of research and thus provides a solution with a high practical impact to survive in\u00a0the tough competition of the automotive industry. Therefore, the work addresses these fundamental business questions: 1) Candissatisfied customers be classified based on data that is produced during every service visit? 2) Can the dissatisfaction indicators\u00a0be derived from service process data? A machine learning problem is set up that compared 5 classifiers and analyzed data from\u00a019,008 real service visits from an automotive company. The 105 extracted features were drawn from the most significant available\u00a0sources: warranty, diagnostic, dealer system and general vehicle data. The best result for customer dissatisfaction classification\u00a0was 88.8% achieved with the SVM classifier (RBF kernel). Furthermore, the 46 most potential indicators for dissatisfaction were\u00a0identified by the evolutionary feature selection. Our system was capable of classifying customer dissatisfaction solely based on\u00a0the objective data that is generated by almost every service visit. As the amount of these data is continuously growing, we expect\u00a0that the presented data-driven approach can achieve even better results in the future with a higher amount of data.<\/jats:p>","DOI":"10.5430\/air.v6n1p80","type":"journal-article","created":{"date-parts":[[2016,12,25]],"date-time":"2016-12-25T22:22:43Z","timestamp":1482704563000},"page":"80","source":"Crossref","is-referenced-by-count":11,"title":["Can machine learning techniques predict customer dissatisfaction? A feasibility study for the automotive industry"],"prefix":"10.5430","volume":"6","author":[{"given":"Stefan","family":"Meinzer","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ulf","family":"Jensen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander","family":"Thamm","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joachim","family":"Hornegger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bj\u00f6rn M.","family":"Eskofier","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"3394","published-online":{"date-parts":[[2016,12,26]]},"container-title":["Artificial Intelligence Research"],"original-title":[],"link":[{"URL":"http:\/\/www.sciedu.ca\/journal\/index.php\/air\/article\/viewFile\/10534\/6572","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/www.sciedu.ca\/journal\/index.php\/air\/article\/viewFile\/10534\/6572","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2016,12,26]],"date-time":"2016-12-26T21:12:17Z","timestamp":1482786737000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.sciedu.ca\/journal\/index.php\/air\/article\/view\/10534"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,12,26]]},"references-count":0,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2016,7,19]]}},"URL":"https:\/\/doi.org\/10.5430\/air.v6n1p80","relation":{},"ISSN":["1927-6982","1927-6974"],"issn-type":[{"value":"1927-6982","type":"electronic"},{"value":"1927-6974","type":"print"}],"subject":[],"published":{"date-parts":[[2016,12,26]]}}}