Gizem KayarTolga SümerFurkan SoytürkGalip Erkin DorukCihan CobanogluSümer, TolgaSoytürk, FurkanDoruk, Galip ErkinÇobanoğlu, CihanKayar, Gizem2025-10-062019194158421941-58422-s2.0-85078080111https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078080111&partnerID=40&md5=8d9f84ea4f29b2c7a7e3bebbeae304echttps://gcris.yasar.edu.tr/handle/123456789/9463Restaurant-like service areas have been adapting different technologies to enhance customer satisfaction for many years. In this LBR we share our research idea about how to integrate music data and its analysis for this purpose. In the first part we propose a voting system tocarry your favorite song to the top of the list to be played next in your place. In the secondpart we propose a recommendation system to find a place that suits your music requirements inyour close proximity. Our preliminary survey results for the first part and the data analysis results for the second part shows that our approach has a promising potential for customer satisfaction. © 2020 Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/closedAccessCustomer Satisfaction, Data Analysis, Spotify ApiCustomer SatisfactionData AnalysisSpotify APIArticle