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[{"key": "dc.contributor.advisor", "value": "Karjaluoto, Heikki", "language": "", "element": "contributor", "qualifier": "advisor", "schema": "dc"}, {"key": "dc.contributor.advisor", "value": "Badham, Mark", "language": "", "element": "contributor", "qualifier": "advisor", "schema": "dc"}, {"key": "dc.contributor.author", "value": "Varmavuo, Eevi", "language": "", "element": "contributor", "qualifier": "author", "schema": "dc"}, {"key": "dc.date.accessioned", "value": "2020-08-28T10:00:31Z", "language": null, "element": "date", "qualifier": "accessioned", "schema": "dc"}, {"key": "dc.date.available", "value": "2020-08-28T10:00:31Z", "language": null, "element": "date", "qualifier": "available", "schema": "dc"}, {"key": "dc.date.issued", "value": "2020", "language": "", "element": "date", "qualifier": "issued", "schema": "dc"}, {"key": "dc.identifier.uri", "value": "https://jyx.jyu.fi/handle/123456789/71539", "language": null, "element": "identifier", "qualifier": "uri", "schema": "dc"}, {"key": "dc.description.abstract", "value": "Teko\u00e4ly on tuonut suuria muutoksia markkinoinnin k\u00e4yt\u00e4nt\u00f6ihin viime vuosina. Vaikkakin teko\u00e4lyteknologiat sek\u00e4 big data ovat mahdollistaneet uusia innovatiivisia ratkaisuja markkinoinnin toteuttamiseen, ala on viel\u00e4 alkutekij\u00f6iss\u00e4\u00e4n. Teko\u00e4ly\u00e4 hy\u00f6dynt\u00e4v\u00e4 markkinointi on ollut sek\u00e4 tutkijoiden ett\u00e4 ammattilaisten suurennuslasin alla viime vuosina. Aiemmat tutkimukset ovat kuitenkin tarjonneet vasta hyvin v\u00e4h\u00e4n ymm\u00e4rryst\u00e4 siihen, millaista dataa markkinoijien tulisi k\u00e4ytt\u00e4\u00e4 teko\u00e4ly\u00e4 hy\u00f6dynt\u00e4v\u00e4\u00e4 markkinointia toteuttaessaan, jotta parhaat mahdolliset tulokset voidaan saavuttaa.\nT\u00e4m\u00e4n tutkimuksen tavoitteena on tutkia millaista dataa markkinoijien tulisi k\u00e4ytt\u00e4\u00e4 teko\u00e4ly\u00e4 hy\u00f6dynt\u00e4v\u00e4ss\u00e4 markkinoinnissa onnistuakseen t\u00e4m\u00e4n toteutuksessa. Tavoitteena on lis\u00e4ksi tarjota ymm\u00e4rryst\u00e4 tekij\u00f6ist\u00e4, jotka vaikuttavat n\u00e4ihin datan ominaisuuksiin. Empiirisen datan ker\u00e4\u00e4miseksi toteutettiin kolmetoista asiantuntijahaastattelua.\nT\u00e4m\u00e4 tutkimus tukee pitk\u00e4lti aiempaa kirjallisuutta, mutta tarjoaa lis\u00e4ksi uusia n\u00e4k\u00f6kulmia datan tarvittaviin ominaisuuksiin. Tutkimuksen tulokset osoittavat, ett\u00e4 datan tulisi olla puhdasta, luotettavaa ja laadukasta, jotta tulokset ovat tehokkaita, t\u00e4sm\u00e4llisi\u00e4 ja puolueettomia. Sis\u00e4ist\u00e4 dataa tulisi priorisoida ja t\u00e4ydent\u00e4\u00e4 ulkoisilla tietol\u00e4hteill\u00e4, kuten sosiaalisen median datalla. N\u00e4in voidaan saavuttaa syvempi insight ja parempi ennustus toteutetusta kampanjasta. Tulosten mukaan sis\u00e4isten ja ulkoisten tietol\u00e4hteiden yhdist\u00e4minen on palkitsevin toimintatapa. Liiketoiminnan tavoitteet tulisi asettaa keski\u00f6\u00f6n datan ker\u00e4\u00e4miseen, hallinnointiin ja analysointiin liittyviss\u00e4 teht\u00e4viss\u00e4. Lis\u00e4ksi l\u00e4pin\u00e4kyvyys dataan liittyviss\u00e4 asioissa tunnistetaan t\u00e4rke\u00e4ksi, jotta asiakkaat luovuttavat todenmukaista henkil\u00f6kohtaista dataa itsest\u00e4\u00e4n markkinointitarkoituksiin.", "language": "fi", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.abstract", "value": "Artificial intelligence (AI) has been reshaping marketing in many ways during recent years. While AI technologies and the rise of big data have enabled innovative ways to practice marketing, the industry is still rather young. The applications of AI in marketing have been of interest among researchers and industry leaders, yet prior research focus provided little knowledge on what type of data marketers should rely on to reach successful outcomes of AI campaigns.\nThis study aims to offer knowledge on the data related factors that determine what type of data marketers should rely on to ensure successful delivery of AI campaigns. The empirical data was collected through thirteen expert interviews.\nThis study reinforces the existing literature to great extent, but also provides new perspectives to the identified factors. The findings of this study show that data used for AI campaigns in marketing should be clean, reliable and of high quality to ensure outcomes are effective, accurate and unbiased. Internal data should be prioritized and complemented with external data such as social media data to gain deeper insights and better predictions. Combining both internal and external data sets is identified as best practice to run AI campaigns. Additionally, the business goals and wanted outcomes of AI campaigns should be placed at the center of any data collection, management and analysis process to ensure successful results of AI practices. Finally, transparency in data related matters was found important as it builds trust and ensures customers provide accurate personal data for marketing purposes.", "language": "en", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Submitted by Paivi Vuorio (paelvuor@jyu.fi) on 2020-08-28T10:00:31Z\nNo. of bitstreams: 0", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Made available in DSpace on 2020-08-28T10:00:31Z (GMT). No. of bitstreams: 0\n Previous issue date: 2020", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.format.extent", "value": "61", "language": "", "element": "format", "qualifier": "extent", "schema": "dc"}, {"key": "dc.format.mimetype", "value": "application/pdf", "language": null, "element": "format", "qualifier": "mimetype", "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": "data-driven marketing", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.title", "value": "Factors affecting the success of AI campaigns in marketing : data perspective", "language": "", "element": "title", "qualifier": null, "schema": "dc"}, {"key": "dc.type", "value": "master thesis", "language": null, "element": "type", "qualifier": null, "schema": "dc"}, {"key": "dc.identifier.urn", "value": "URN:NBN:fi:jyu-202008285669", "language": "", "element": "identifier", "qualifier": "urn", "schema": "dc"}, {"key": "dc.type.ontasot", "value": "Pro gradu -tutkielma", "language": "fi", "element": "type", "qualifier": "ontasot", "schema": "dc"}, {"key": "dc.type.ontasot", "value": "Master\u2019s thesis", "language": "en", "element": "type", "qualifier": "ontasot", "schema": "dc"}, {"key": "dc.contributor.faculty", "value": "Jyv\u00e4skyl\u00e4 University School of Business and Economics", "language": "en", "element": "contributor", "qualifier": "faculty", "schema": "dc"}, {"key": "dc.contributor.faculty", "value": "Jyv\u00e4skyl\u00e4n yliopiston kauppakorkeakoulu", "language": "fi", "element": "contributor", "qualifier": "faculty", "schema": "dc"}, {"key": "dc.contributor.department", "value": "Taloustieteet", "language": "fi", "element": "contributor", "qualifier": "department", "schema": "dc"}, {"key": "dc.contributor.department", "value": "Business and Economics", "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": "Markkinointi", "language": "fi", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "dc.subject.discipline", "value": "Marketing", "language": "en", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "yvv.contractresearch.funding", "value": "0", "language": "", "element": "contractresearch", "qualifier": "funding", "schema": "yvv"}, {"key": "dc.type.coar", "value": "http://purl.org/coar/resource_type/c_bdcc", "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": "masterThesis", "language": null, "element": "type", "qualifier": "publication", "schema": "dc"}, {"key": "dc.subject.oppiainekoodi", "value": "20423", "language": "", "element": "subject", "qualifier": "oppiainekoodi", "schema": "dc"}, {"key": "dc.subject.yso", "value": "teko\u00e4ly", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "koneoppiminen", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "digitaalinen markkinointi", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "artificial intelligence", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "machine learning", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "digital marketing", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.format.content", "value": "fulltext", "language": null, "element": "format", "qualifier": "content", "schema": "dc"}, {"key": "dc.rights.url", "value": "https://rightsstatements.org/page/InC/1.0/", "language": null, "element": "rights", "qualifier": "url", "schema": "dc"}, {"key": "dc.type.okm", "value": "G2", "language": null, "element": "type", "qualifier": "okm", "schema": "dc"}]
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