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[{"key": "dc.contributor.advisor", "value": "Luukkainen, Sakari", "language": null, "element": "contributor", "qualifier": "advisor", "schema": "dc"}, {"key": "dc.contributor.author", "value": "Almonkari, Julle", "language": null, "element": "contributor", "qualifier": "author", "schema": "dc"}, {"key": "dc.date.accessioned", "value": "2025-06-02T11:59:54Z", "language": null, "element": "date", "qualifier": "accessioned", "schema": "dc"}, {"key": "dc.date.available", "value": "2025-06-02T11:59:54Z", "language": null, "element": "date", "qualifier": "available", "schema": "dc"}, {"key": "dc.date.issued", "value": "2025", "language": null, "element": "date", "qualifier": "issued", "schema": "dc"}, {"key": "dc.identifier.uri", "value": "https://jyx.jyu.fi/handle/123456789/102964", "language": null, "element": "identifier", "qualifier": "uri", "schema": "dc"}, {"key": "dc.description.abstract", "value": "This research explores the use of machine learning (ML) in refining customer data obtained through customer relationship management (CRM) systems. The objective is to provide insights on how ML, regarding CRM systems and customer data, is currently utilized together with how it could and should be utilized in the future. The topic's relevancy is very high due to the increasing focus on utilizing ML in CRM systems as well as other business functions. Feedback from interviewees indicates that there are ways it should be improved.\nThe research was conducted utilizing a qualitative approach, combining a literature review as well as semi-structured interviews which were analyzed using a content analysis approach. The literature review material was chosen based on its relevancy to the research topic while acknowledging the rapidly evolving nature of the ML field, thus aiming to use more current publications. Interviews were conducted with a limited number of participants (5) with the focus being on their subject expertise.\nThe research results gave way to some of the following discoveries: understanding that factors such as consistency and churn prediction were seen as enabling the B2B sales process to advance. Supervised (like classification models) and unsupervised learning (like clustering algorithms) were seen as the best for guiding B2B. Research views on ML in CRM systems showed that the focus is on churn prediction while understanding the possibilities with DL. Data in CRM systems is used for process management, fundraising and lead scoring.", "language": "en", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.abstract", "value": "T\u00e4ss\u00e4 tutkimuksessa tarkastellaan koneoppimisen k\u00e4ytt\u00f6\u00e4 asiakassuhteiden hallintaj\u00e4rjestelmien (CRM) kautta saatujen asiakastietojen jalostamisessa. Tutkimuksen tavoitteena on opettaa, miten koneoppimista hy\u00f6dynnet\u00e4\u00e4n t\u00e4ll\u00e4 hetkell\u00e4 CRM-j\u00e4rjestelmien ja asiakastietojen osalta sek\u00e4 miten koneoppimista voitaisiin ja pit\u00e4isi hy\u00f6dynt\u00e4\u00e4 tulevaisuudessa. Aihe on eritt\u00e4in ajankohtainen, sill\u00e4 koneoppimisen hy\u00f6dynt\u00e4minen CRM-j\u00e4rjestelmiss\u00e4 ja muussa liiketoiminnassa on yh\u00e4 t\u00e4rke\u00e4mp\u00e4\u00e4. Haastateltavilta saatu j\u00e4rjestelm\u00e4palaute osoittaa, ett\u00e4 olemassa olevissa j\u00e4rjestelmiss\u00e4 on parantamisen varaa.\nTutkimus toteutettiin laadullisella l\u00e4hestymistavalla, jossa yhdistettiin kirjallisuuskatsaus sek\u00e4 puolistrukturoidut haastattelut, jotka analysoitiin sis\u00e4ll\u00f6nanalyysin avulla. Kirjallisuuskatsauksen aineisto valittiin sen perusteella, miten se liittyy tutkimusaiheeseen, ottaen samalla otettiin huomioon koneoppimisen nopea kehitys. T\u00e4st\u00e4 johtuen pyrittiin k\u00e4ytt\u00e4m\u00e4\u00e4n ajankohtaisempaa materiaalia. Haastatteluihin valittiin viisi osallistujaa heid\u00e4n aihepiirin asiantuntemuksensa perusteella.\nTutkimustulosten perusteella l\u00f6ydettiin seuraavanlaisia havaintoja: johdonmukaisuus ja asiakaspoistumisen ennustamisen ymm\u00e4rt\u00e4minen n\u00e4htiin t\u00e4rkein\u00e4 tekij\u00f6in\u00e4 B2B-myyntiprosessien etenemiselle. Ohjattu oppiminen (kuten luokittelumallit) ja ohjaamaton oppiminen (kuten klusterointialgoritmit) n\u00e4htiin parhaana keinona ohjata B2B-myynti\u00e4. Tutkimusn\u00e4kemykset koneoppimisen k\u00e4ytt\u00f6\u00f6n CRM-j\u00e4rjestelmiss\u00e4 osoittivat, ett\u00e4 painopiste on asiakaspoistumisen ennustamisessa, mutta samalla ymm\u00e4rret\u00e4\u00e4n syv\u00e4oppimisen mahdollisuudet. CRM-j\u00e4rjestelmiss\u00e4 dataa k\u00e4ytet\u00e4\u00e4n prosessien hallintaan, varainhan-kintaan ja liidien arvioimiseen.", "language": "fi", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Submitted by jyx lomake-julkaisija (jyx-julkaisija.group@korppi.jyu.fi) on 2025-06-02T11:59:54Z\nNo. of bitstreams: 0", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Made available in DSpace on 2025-06-02T11:59:54Z (GMT). No. of bitstreams: 0", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.format.extent", "value": "76", "language": null, "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": "CC BY 4.0", "language": null, "element": "rights", "qualifier": null, "schema": "dc"}, {"key": "dc.title", "value": "Refining B2B CRM Systems Customer Data Using Machine Learning", "language": null, "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-202506024773", "language": null, "element": "identifier", "qualifier": "urn", "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.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": "Master's Degree Programme in Information Systems", "language": "fi", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "dc.subject.discipline", "value": "Master's Degree Programme in Information Systems", "language": "en", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "dc.type.coar", "value": "http://purl.org/coar/resource_type/c_bdcc", "language": null, "element": "type", "qualifier": "coar", "schema": "dc"}, {"key": "dc.rights.copyright", "value": "\u00a9 The Author(s)", "language": null, "element": "rights", "qualifier": "copyright", "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.format.content", "value": "fulltext", "language": null, "element": "format", "qualifier": "content", "schema": "dc"}, {"key": "dc.rights.url", "value": "https://creativecommons.org/licenses/by/4.0/", "language": null, "element": "rights", "qualifier": "url", "schema": "dc"}, {"key": "dc.description.accessibilityfeature", "value": "ei tietoa saavutettavuudesta", "language": "fi", "element": "description", "qualifier": "accessibilityfeature", "schema": "dc"}, {"key": "dc.description.accessibilityfeature", "value": "unknown accessibility", "language": "en", "element": "description", "qualifier": "accessibilityfeature", "schema": "dc"}]
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