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[{"key": "dc.contributor.advisor", "value": "Clements, Kati", "language": null, "element": "contributor", "qualifier": "advisor", "schema": "dc"}, {"key": "dc.contributor.author", "value": "Heikkinen, Sami", "language": null, "element": "contributor", "qualifier": "author", "schema": "dc"}, {"key": "dc.date.accessioned", "value": "2024-12-30T07:23:56Z", "language": null, "element": "date", "qualifier": "accessioned", "schema": "dc"}, {"key": "dc.date.available", "value": "2024-12-30T07:23:56Z", "language": null, "element": "date", "qualifier": "available", "schema": "dc"}, {"key": "dc.date.issued", "value": "2024", "language": null, "element": "date", "qualifier": "issued", "schema": "dc"}, {"key": "dc.identifier.uri", "value": "https://jyx.jyu.fi/handle/123456789/99208", "language": null, "element": "identifier", "qualifier": "uri", "schema": "dc"}, {"key": "dc.description.abstract", "value": "T\u00e4m\u00e4n kirjallisuuskatsauksen tavoitteena oli selvitt\u00e4\u00e4, kuinka big data -\r\nanalytiikkaa voidaan hy\u00f6dynt\u00e4\u00e4 Spotifyn ilmaisk\u00e4ytt\u00e4jien tehokkaampaan\r\nmuuntamiseen maksaviksi tilaajiksi. Digitaalisten alustojen ja suoratoistopalveluiden my\u00f6t\u00e4 musiikkiteollisuus on kokenut merkitt\u00e4v\u00e4n murroksen. Spotify\r\non vakiinnuttanut asemansa johtavana toimijana freemiumliiketoimintamallinsa ansiosta, joka yhdist\u00e4\u00e4 maksuttoman, mainosrahoitteisen\r\npalvelun ja maksullisen premium-tilauksen. Mallin haasteena ovat kuitenkin\r\ntaloudelliset paineet, jotka korostavat maksavien asiakkaiden merkityst\u00e4.\r\nBig data -analytiikka tarjoaa tehokkaita ty\u00f6kaluja liiketoiminnan kehitt\u00e4miseen,\r\nsill\u00e4 sen avulla voidaan ker\u00e4t\u00e4, analysoida ja hy\u00f6dynt\u00e4\u00e4 suuria tietom\u00e4\u00e4ri\u00e4\r\nk\u00e4ytt\u00e4j\u00e4kokemuksen optimointiin ja asiakassuhteiden vahvistamiseen. Tutkimuksessa sovellettiin big data -analytiikan viitekehyst\u00e4, joka jakaa analytiikan\r\nprosessin kolmeen vaiheeseen: datan ker\u00e4\u00e4miseen, analysointiin ja hy\u00f6dynt\u00e4miseen.\r\nTulokset osoittivat, ett\u00e4 big data analytiikka voi paljastaa k\u00e4ytt\u00e4jien tarpeita ja\r\nk\u00e4ytt\u00e4ytymismalleja, joiden avulla markkinointitoimia ja muita liiketoimintastrategioita voidaan kohdentaa tarkemmin. Lis\u00e4ksi big data analytiikan havaittiin parantavan sek\u00e4 k\u00e4ytt\u00e4j\u00e4hankintaa ett\u00e4 asiakaspysyvyytt\u00e4 hy\u00f6dynt\u00e4m\u00e4ll\u00e4\r\npersonoituja suosituksia, kohdennettuja kampanjoita ja strategioita, kuten premium-kokeilujaksoja uusille asiakkaille ja r\u00e4\u00e4t\u00e4l\u00f6ityjen premium-tasojen kehitt\u00e4mist\u00e4 asiakaspoistuman v\u00e4hent\u00e4miseksi.\r\nTutkimus nosti esiin tietosuojaan ja eettisiin kysymyksiin liittyvi\u00e4 haasteita, jotka edellytt\u00e4v\u00e4t huolellista hallintaa erityisesti k\u00e4ytt\u00e4j\u00e4tietojen turvallisen k\u00e4sittelyn ja datan eettisen k\u00e4yt\u00f6n osalta. Jatkotutkimuksia suositellaan big data analytiikkaty\u00f6kalujen tehokkuuden arvioimiseksi, suoratoistopalveluiden liiketoimintamallien vertailuun ja levy-yhti\u00f6iden kanssa teht\u00e4v\u00e4n yhteisty\u00f6n kehitt\u00e4miseksi.", "language": "fi", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.abstract", "value": "The objective of this literature review was to investigate how big data analytics\r\ncan be leveraged to enhance the conversion of Spotify\u2019s free users into paying\r\nsubscribers. The emergence of digital platforms and streaming services has significantly transformed the music industry. Spotify has established itself as a\r\nmarket leader through its freemium business model, which combines a free, adsupported service with a paid premium subscription. However, this model faces financial challenges, highlighting the critical role of paying customers in sustaining profitability.\r\nBig data analytics provides robust tools for business development by facilitating the collection, analysis, and utilization of large datasets to optimize user\r\nexperiences and strengthen customer relationships. This study applied a big\r\ndata analytics framework that divides the process into three stages: data collection, analysis, and utilization.\r\nThe findings suggest that big data analytics can uncover user needs and behavioral patterns, enabling more precise targeting of marketing efforts and business\r\nstrategies. Moreover, big data analytics was found to enhance both user acquisition and retention through the implementation of personalized recommendations, targeted campaigns, and strategies such as premium trial periods for new\r\nusers and the development of customized premium tiers to reduce churn.\r\nThe study also identified challenges related to data privacy and ethical considerations, emphasizing the need for rigorous data management practices and\r\nethical application of analytics. Further research is recommended to evaluate\r\nthe effectiveness of big data analytics tools, compare business models across\r\nstreaming platforms, and develop sustainable collaboration frameworks with\r\nrecord labels.", "language": "en", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Submitted by Paivi Vuorio (paelvuor@jyu.fi) on 2024-12-30T07:23:56Z\r\nNo. of bitstreams: 0", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Made available in DSpace on 2024-12-30T07:23:56Z (GMT). No. of bitstreams: 0\r\n Previous issue date: 2024", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.format.extent", "value": "25", "language": null, "element": "format", "qualifier": "extent", "schema": "dc"}, {"key": "dc.language.iso", "value": "eng", "language": null, "element": "language", "qualifier": "iso", "schema": "dc"}, {"key": "dc.rights", "value": "In Copyright", "language": "en", "element": "rights", "qualifier": null, "schema": "dc"}, {"key": "dc.subject.other", "value": "freemium", "language": null, "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "spotify", "language": null, "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "big data analytics", "language": null, "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.title", "value": "Optimizing Spotify\u2019s business through big data analytics", "language": null, "element": "title", "qualifier": null, "schema": "dc"}, {"key": "dc.type", "value": "bachelor thesis", "language": null, "element": "type", "qualifier": null, "schema": "dc"}, {"key": "dc.identifier.urn", "value": "URN:NBN:fi:jyu-202412308013", "language": null, "element": "identifier", "qualifier": "urn", "schema": "dc"}, {"key": "dc.type.ontasot", "value": "Bachelor's thesis", "language": "en", "element": "type", "qualifier": "ontasot", "schema": "dc"}, {"key": "dc.type.ontasot", "value": "Kandidaatinty\u00f6", "language": "fi", "element": "type", "qualifier": "ontasot", "schema": "dc"}, {"key": "dc.contributor.faculty", "value": "Informaatioteknologian tiedekunta", "language": "fi", "element": "contributor", "qualifier": "faculty", "schema": "dc"}, {"key": "dc.contributor.faculty", "value": "Faculty of Information Technology", "language": "en", "element": "contributor", "qualifier": "faculty", "schema": "dc"}, {"key": "dc.contributor.department", "value": "Informaatioteknologia", "language": "fi", "element": "contributor", "qualifier": "department", "schema": "dc"}, {"key": "dc.contributor.department", "value": "Information Technology", "language": "en", "element": "contributor", "qualifier": "department", "schema": "dc"}, {"key": "dc.contributor.organization", "value": "Jyv\u00e4skyl\u00e4n yliopisto", "language": "fi", "element": "contributor", "qualifier": "organization", "schema": "dc"}, {"key": "dc.contributor.organization", "value": "University of Jyv\u00e4skyl\u00e4", "language": "en", "element": "contributor", "qualifier": "organization", "schema": "dc"}, {"key": "dc.subject.discipline", "value": "Tietoj\u00e4rjestelm\u00e4tiede", "language": "fi", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "dc.subject.discipline", "value": "Information Systems Science", "language": "en", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "yvv.contractresearch.funding", "value": "0", "language": null, "element": "contractresearch", "qualifier": "funding", "schema": "yvv"}, {"key": "dc.type.coar", "value": "http://purl.org/coar/resource_type/c_7a1f", "language": null, "element": "type", "qualifier": "coar", "schema": "dc"}, {"key": "dc.rights.accesslevel", "value": "openAccess", "language": null, "element": "rights", "qualifier": "accesslevel", "schema": "dc"}, {"key": "dc.type.publication", "value": "bachelorThesis", "language": null, "element": "type", "qualifier": "publication", "schema": "dc"}, {"key": "dc.subject.oppiainekoodi", "value": "601", "language": null, "element": "subject", "qualifier": "oppiainekoodi", "schema": "dc"}, {"key": "dc.subject.yso", "value": "suoratoistopalvelut", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "big data", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "streaming services", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "big data", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.rights.url", "value": "https://rightsstatements.org/page/InC/1.0/", "language": null, "element": "rights", "qualifier": "url", "schema": "dc"}]
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