Machine learning in marketing strategy: A socio-technical approach in South Africa

dc.contributor.authorGovender, Aleasha
dc.contributor.supervisorQuaye, Emmanuel
dc.date.accessioned2025-01-23T07:41:14Z
dc.date.available2025-01-23T07:41:14Z
dc.date.issued2024
dc.descriptionA research report submitted in partial fulfillment of the requirements for the degree of Master of Business Administration to the Faculty of Commerce, Law and Management, Wits Business School, University of the Witwatersrand, Johannesburg, 2024
dc.description.abstractThe purpose of this research study was to determine whether the existing market segmentation, targeting and positioning (STP) approaches are optimal for marketing strategy in South Africa, and to what extent AI and machine learning are being used to improve marketing strategy in South Africa. The methods used have drawn on qualitative data research and document analysis. There were 10 participants in the study, the industries include Banking, Telecommunication and Medical Insurance. The methods used have drawn on qualitative data research and document analysis. The key results of the research have determined that Machine Learning is in its inception phase in terms of being used in marketing strategy in corporate South Africa. The research further finds that there are factors that are slowing the development in this field that are aligned with both hard and soft capabilities, for example, along with infrastructural capabilities like software integration, strategic capabilities like interdepartmental alignment are required for effective deployment of these technologies. Further, the research finds that the current segmentation, targeting and positioning methods used in isolation are not optimally contributing to marketing strategy, rather a blended approach including insights from customer data will provide a more accurate STP strategy. This research supports marketeers, technologists, business structures, researchers in South Africa, as well as strategists who deal with mass consumer bases, because market segmentation, targeting and positioning underpin how marketing strategy is rolled out throughout corporate South Africa and AI and Machine Learning are emerging technologies that are highly topical and are only at the inception phase of optimal utilisation
dc.description.submitterMM2025
dc.facultyFaculty of Commerce, Law and Management
dc.identifier.citationGovender, Aleasha. (2024). Machine learning in marketing strategy: A socio-technical approach in South Africa [Master’s dissertation, University of the Witwatersrand, Johannesburg].WireDSpace.https://hdl.handle.net/10539/43630
dc.identifier.urihttps://hdl.handle.net/10539/43630
dc.language.isoen
dc.publisherUniversity of the Witwatersrand, Johannesburg
dc.rights© 2025 University of the Witwatersrand, Johannesburg. All rights reserved. The copyright in this work vests in the University of the Witwatersrand, Johannesburg. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of University of the Witwatersrand, Johannesburg.
dc.rights.holderUniversity of the Witwatersrand, Johannesburg
dc.schoolWITS Business School
dc.subjectMarket segmentation
dc.subjectTargeting
dc.subjectPositioning
dc.subjectLiving standards measure (LSM)
dc.subjectSocio- economic measure (SEM)
dc.subjectMarketing strategy
dc.subjectMachine learning
dc.subjectUCTD
dc.subject.otherSDG-9: Industry, innovation and infrastructure
dc.titleMachine learning in marketing strategy: A socio-technical approach in South Africa
dc.typeDissertation
Files
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
Govender_Machine_2025.pdf
Size:
833.11 KB
Format:
Adobe Portable Document Format
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
2.43 KB
Format:
Item-specific license agreed upon to submission
Description: