Data mining techniques for marketing, sales and customer relationship management. Practical implications for collaborative tools.
| dc.contributor.author | Filippoudi, Zoi | en |
| dc.date.accessioned | 2024-06-20T10:48:19Z | |
| dc.date.available | 2024-06-20T10:48:19Z | |
| dc.date.issued | 2024-06-20 | |
| dc.identifier.uri | https://repository.ihu.edu.gr//xmlui/handle/11544/30469 | |
| dc.rights | Default License | |
| dc.subject | Data mining techniques | en |
| dc.title | Data mining techniques for marketing, sales and customer relationship management. Practical implications for collaborative tools. | en |
| heal.abstract | This dissertation is written as a part of the MSc in e-Business & Digital Marketing at the International Hellenic University. The purpose of the research paper is to provide insight for researchers, academic peers, learners, data miners, companies, and individuals interested in staying updated on data mining techniques and most used collaborative tools in the fields of sales, marketing and customer relationship management. The methodology adopted for my thesis involved conducting a comprehensive literature review. Over one hundred academic articles and books, retrieved from Google Scholar, were reviewed and fifty five were subsequently selected, as seen in references. Moreover, as a part of this thesis I delved deeper into some of the data mining tools that are presenting in this paper, completing some online courses. My contribution involves a novel comparison of three data mining software tools- from the perspective of their features, components, user interface, reporting and visualization capabilities- synthesizing insights from literature and courses. This analysis addresses a gap in existing research, offering valuable guidance for researchers and practitioners navigating the field.This research concludes that when it comes to choosing data mining tool, there is not a single solution that fits every scenario, but various factors have to be considered. Moreover, there is a presentation of benefits and challenges in the process of data mining. Finally, there is a brief mention in the emerging trends in the field of data mining: artificial intelligence, machine learning and big data analytics. | en |
| heal.academicPublisher | IHU | en |
| heal.academicPublisherID | ihu | en_US |
| heal.access | free | en_US |
| heal.advisorName | Asimakopoulos, Costas | en |
| heal.committeeMemberName | Stalidis, Georgios | en |
| heal.committeeMemberName | Baltatzis, Dimitrios | en |
| heal.dateAvailable | 2024-06-06 | |
| heal.language | en | en_US |
| heal.license | http://creativecommons.org/licenses/by-nc/4.0 | en_US |
| heal.publicationDate | 2024-06-06 | |
| heal.recordProvider | School of Science and Technology, MSc in e-Business and Digital Marketing | en_US |
| heal.type | masterThesis | en_US |
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