Potential Artificial Intelligence Features for Digital Marketing Performance Analytics Product
Glan, Juska (2018)
Glan, Juska
Haaga-Helia ammattikorkeakoulu
2018
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2018112117757
https://urn.fi/URN:NBN:fi:amk-2018112117757
Tiivistelmä
This thesis is a qualitative study for a technology start-up with a SaaS-product in marketing performance reporting. The researches’ objective was to understand the possibilities of artificial intelligence; an inquiry of product development through gained knowledge formulated the objectives and methods used in this thesis. Recommendations for a specific domain require an understanding of a concept at a general level.
The theoretical framework for the study is formed around understanding the concept of artificial intelligence and the theory it withholds, benchmarking the digital marketing reporting industry leaders and interviewing the experts of the subject technology and the case company’s target customer segment of digital marketing agencies. Machine learning was identified as the principal technology. Its models and algorithms formed the basis for the theoretical framework.
The methodology of the study describes the research process and methods used. The key components of the research were a qualitative analysis of data which was acquired by desktop research, empirical studies, and interviews. Primary data was collected by interviewing experts in artificial intelligence and the personnel of a digital marketing agency. The main themes of the interviews consisted of relations between the participants and artificial intelligence and the general characteristics of artificial intelligence. Secondary data was collected by researching written sources. It consisted of scientific articles, books and other text sources addressing the subject technologies.
Research data was then analysed and validated through qualitative analysis. Benchmarking of the marketing reporting industry leaders was conducted to understand the implementation of the findings.
The study formed two feature recommendations for the case company: a system to bring insights from accumulated data and a prescriptive analytics dashboard. The results were validated after thesis work through discussions between the main stakeholders of the study. The findings serve as solid foundations for the author in future studies and future life.
This study was conducted primarily between April 2018 and October 2018.
The theoretical framework for the study is formed around understanding the concept of artificial intelligence and the theory it withholds, benchmarking the digital marketing reporting industry leaders and interviewing the experts of the subject technology and the case company’s target customer segment of digital marketing agencies. Machine learning was identified as the principal technology. Its models and algorithms formed the basis for the theoretical framework.
The methodology of the study describes the research process and methods used. The key components of the research were a qualitative analysis of data which was acquired by desktop research, empirical studies, and interviews. Primary data was collected by interviewing experts in artificial intelligence and the personnel of a digital marketing agency. The main themes of the interviews consisted of relations between the participants and artificial intelligence and the general characteristics of artificial intelligence. Secondary data was collected by researching written sources. It consisted of scientific articles, books and other text sources addressing the subject technologies.
Research data was then analysed and validated through qualitative analysis. Benchmarking of the marketing reporting industry leaders was conducted to understand the implementation of the findings.
The study formed two feature recommendations for the case company: a system to bring insights from accumulated data and a prescriptive analytics dashboard. The results were validated after thesis work through discussions between the main stakeholders of the study. The findings serve as solid foundations for the author in future studies and future life.
This study was conducted primarily between April 2018 and October 2018.