Investigating the relationship between mobile network performance metrics and customer satisfaction
| dc.contributor.advisor | Bassett, Bruce | |
| dc.contributor.advisor | Little, Francesca | |
| dc.contributor.author | Labuschagne, Louwrens | |
| dc.date.accessioned | 2020-03-17T11:42:41Z | |
| dc.date.available | 2020-03-17T11:42:41Z | |
| dc.date.issued | 2019 | |
| dc.date.updated | 2020-03-16T14:47:51Z | |
| dc.description.abstract | Fixed and mobile communication service providers (CSPs) are facing fierce competition among each other. In a globally saturated market, the primary di↵erentiator between CSPs has become customer satisfaction, typically measured by the Net Promoter Score (NPS) for a subscriber. The NPS is the answer to the question: ”How likely is it that you will recommend this product/company to a friend or colleague?” The responses range from 0 representing not at all likely to 10 representing extremely likely. In this thesis, we aim to identify which, if any, network performance metrics contribute to subscriber satisfaction. In particular, we investigate the relationship between the NPS survey results and 11 network performance metrics of the respondents of a major mobile operator in South Africa. We identify the most influential performance metrics by fitting both linear and non-linear statistical models to the February 2018 survey dataset and test the models on the June 2018 dataset. We find that metrics such as Call Drop Rate, Call Setup Failure Rate, Call Duration and Server Setup Latency are consistently selected as significant features in models of NPS prediction. Nevertheless we find that all the tested statistical and machine learning models, whether linear or non-linear, are poor predictors of NPS scores in a month, when only the network performance metrics in the same month are provided. This suggests that either NPS is driven primarily by other factors (such as customer service interactions at branches and contact centres) or are determined by historical network performance over multiple months. | |
| dc.identifier.apacitation | Labuschagne, L. (2019). <i>Investigating the relationship between mobile network performance metrics and customer satisfaction</i>. (). ,Faculty of Science ,Department of Statistical Sciences. Retrieved from | en_ZA |
| dc.identifier.chicagocitation | Labuschagne, Louwrens. <i>"Investigating the relationship between mobile network performance metrics and customer satisfaction."</i> ., ,Faculty of Science ,Department of Statistical Sciences, 2019. | en_ZA |
| dc.identifier.citation | Labuschagne, L. 2019. Investigating the relationship between mobile network performance metrics and customer satisfaction. . ,Faculty of Science ,Department of Statistical Sciences. | en_ZA |
| dc.identifier.ris | TY - Thesis / Dissertation AU - Labuschagne, Louwrens AB - Fixed and mobile communication service providers (CSPs) are facing fierce competition among each other. In a globally saturated market, the primary di↵erentiator between CSPs has become customer satisfaction, typically measured by the Net Promoter Score (NPS) for a subscriber. The NPS is the answer to the question: ”How likely is it that you will recommend this product/company to a friend or colleague?” The responses range from 0 representing not at all likely to 10 representing extremely likely. In this thesis, we aim to identify which, if any, network performance metrics contribute to subscriber satisfaction. In particular, we investigate the relationship between the NPS survey results and 11 network performance metrics of the respondents of a major mobile operator in South Africa. We identify the most influential performance metrics by fitting both linear and non-linear statistical models to the February 2018 survey dataset and test the models on the June 2018 dataset. We find that metrics such as Call Drop Rate, Call Setup Failure Rate, Call Duration and Server Setup Latency are consistently selected as significant features in models of NPS prediction. Nevertheless we find that all the tested statistical and machine learning models, whether linear or non-linear, are poor predictors of NPS scores in a month, when only the network performance metrics in the same month are provided. This suggests that either NPS is driven primarily by other factors (such as customer service interactions at branches and contact centres) or are determined by historical network performance over multiple months. DA - 2019 DB - OpenUCT DP - University of Cape Town KW - data science KW - mobile network KW - performance metrics LK - https://open.uct.ac.za PY - 2019 T1 - Investigating the relationship between mobile network performance metrics and customer satisfaction TI - Investigating the relationship between mobile network performance metrics and customer satisfaction UR - ER - | en_ZA |
| dc.identifier.uri | https://hdl.handle.net/11427/31605 | |
| dc.identifier.vancouvercitation | Labuschagne L. Investigating the relationship between mobile network performance metrics and customer satisfaction. []. ,Faculty of Science ,Department of Statistical Sciences, 2019 [cited yyyy month dd]. Available from: | en_ZA |
| dc.language.rfc3066 | eng | |
| dc.publisher.department | Department of Statistical Sciences | |
| dc.publisher.faculty | Faculty of Science | |
| dc.subject | data science | |
| dc.subject | mobile network | |
| dc.subject | performance metrics | |
| dc.title | Investigating the relationship between mobile network performance metrics and customer satisfaction | |
| dc.type | Master Thesis | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationname | MSc |