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dc.contributor.advisorKarjaluoto, Heikki
dc.contributor.advisorShaikh, Aijaz A.
dc.contributor.authorLänsipuro, Heidi
dc.date.accessioned2020-03-23T08:17:47Z
dc.date.available2020-03-23T08:17:47Z
dc.date.issued2020
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/68270
dc.description.abstractData-driven decision-making is gaining buzz and popularity across organizational functions and industries. Consequently, data analysis and marketing analytics enable companies of various size and business volume to leverage sustainable performance outcomes and continuous growth through data-driven marketing. Still, marketing professionals lack the tools, skillsets and procedures in turning this data into insights, and, furthermore, insights into action. Furthermore, research has yet not addressed these issues of data-driven marketing practice. Hence, this thesis aims to tackle a gap in current research and practice, and to gain further knowledge into the fragmented research on data-driven marketing. The goal of this study is to discover and understand the current level of data- driven decision-making as well as marketing analytics usage in marketing departments. Additionally, this thesis seeks to discover possible barriers that hinder such process development and usage of analytics for marketers. In doing so, this thesis aims to identify and create a model that describes the degree to which marketing analytical insights and data-driven methods are used in an organization and what may block the progression in this model for marketers.  This thesis takes a qualitative approach to the research dilemma. The data and methodology used in this research include ten marketing professionals’ interviews, as well as a thorough literature review to describe the theoretical framework and to position for this thesis. The data-driven marketing maturity and capability of each case organization was evaluated through qualitative analysis by reflecting the interviewees’ answers on the different levels of the Maturity Model. Through this, a Data-driven Marketing Capability Maturity Model was conceptualized. The thesis further extends the existing research on Capability Maturity Models by introducing barriers to data-driven marketing. These barriers were classified into three different categories: organizational structure barriers, organizational culture barriers and top management barriers. The barriers were placed onto the Data-driven Marketing Capability Maturity Model, to identify the major obstacles to moving forward in each level.en
dc.format.extent76
dc.language.isoen
dc.subject.othermarketing analytics
dc.subject.otherdata-driven marketing
dc.subject.othermarketing measurement
dc.subject.otherdata-driven decision-making
dc.subject.otherCapability Maturity Model
dc.titleCapability Maturity Model for data-driven marketing
dc.identifier.urnURN:NBN:fi:jyu-202003232498
dc.type.ontasotMaster’s thesisen
dc.type.ontasotPro gradu -tutkielmafi
dc.contributor.tiedekuntaKauppakorkeakoulufi
dc.contributor.tiedekuntaSchool of Business and Economicsen
dc.contributor.laitosTaloustieteetfi
dc.contributor.laitosBusiness and Economicsen
dc.contributor.yliopistoJyväskylän yliopistofi
dc.contributor.yliopistoUniversity of Jyväskyläen
dc.contributor.oppiaineMarkkinointifi
dc.contributor.oppiaineMarketingen
dc.rights.copyrightJulkaisu on tekijänoikeussäännösten alainen. Teosta voi lukea ja tulostaa henkilökohtaista käyttöä varten. Käyttö kaupallisiin tarkoituksiin on kielletty.fi
dc.rights.copyrightThis publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited.en
dc.contributor.oppiainekoodi20423
dc.subject.ysomarkkinointi
dc.subject.ysotiedonhallinta
dc.subject.ysodigitaalinen markkinointi
dc.subject.ysotietämyksenhallinta
dc.subject.ysodata
dc.subject.ysomarketing
dc.subject.ysoinformation management
dc.subject.ysodigital marketing
dc.subject.ysoknowledge management
dc.subject.ysodata


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