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Prediction of mobile network subscriber satisfaction by using network probing experience measures and machine learning
The mobile telecommunications market is a highly competitive and mature market and mobile network operators (MNOs) increasingly rely on the quality and reliability of the core services they offer to distinguish themselves from other market players. Customer satisfaction plays a crucial role in such...
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| Format: | Thesis |
| Language: | English English |
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Department of Statistical Sciences
2025
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| _version_ | 1867613615726526464 |
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| access_status_str | Open Access |
| author | Kruger, Martin |
| author2 | Pienaar, Etienne |
| author_browse | Kruger, Martin Pienaar, Etienne |
| author_facet | Pienaar, Etienne Kruger, Martin |
| author_sort | Kruger, Martin |
| collection | Thesis |
| description | The mobile telecommunications market is a highly competitive and mature market and mobile network operators (MNOs) increasingly rely on the quality and reliability of the core services they offer to distinguish themselves from other market players. Customer satisfaction plays a crucial role in such a landscape where negative word of mouth could severely damage the reputation of a business. Customer satisfaction has therefore become a key differentiator for many companies. A popular metric to track customers' experience with a business is the Net Promoter Score® (NPS). NPS is measured by customer surveys, prompting them to answer a simple question: “How likely are you to recommend company X to a friend or colleague?” The response ranges between zero, representing not likely, to ten, representing very likely. The score value is obtained by grouping responses into three categories: Promoters, Neutrals or Detractors, and calculating the percentage difference between promoters and detractors. The more positive the value, the better overall customer perception is likely to be. A key shortcoming of NPS is that it does not provide tangible and directly interpretable reasons for customer responses. This thesis aims to establish whether machine learning models, combined with network experience data collected by passive probing of mobile network interfaces, can accurately predict whether a subscriber will likely be a detractor. In addition, we would like to understand which network experience metrics are the best indicators of poor performance and negatively influence subscriber perception. We make use of survey and network data sourced from a large mobile network operator in South Africa over six months to create modelling features for cross validation of classification models with varying complexity to predict the NPS class of subscribers. We find that mobile network data provided by present Customer Experience Management (CEM) systems may not be ideal for use in machine learning applications. The standard library of metrics and data structures used to perform classical CEM requires much effort to clean and prepare it as viable input to machine learning models. In addition, we find that all tested machine learning models, whether linear or non-linear, are poor predictors of NPS. This suggests that NPS may instead be driven by other factors, such as pricing or the interaction of customers with other processes that are more important and not represented within the present data. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/42363 |
| institution | University of Cape Town (South Africa) |
| language | English eng |
| last_indexed | 2026-06-10T12:38:58.289Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Department of Statistical Sciences |
| publisherStr | Department of Statistical Sciences |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/42363 Prediction of mobile network subscriber satisfaction by using network probing experience measures and machine learning Kruger, Martin Pienaar, Etienne Machine learning The mobile telecommunications market is a highly competitive and mature market and mobile network operators (MNOs) increasingly rely on the quality and reliability of the core services they offer to distinguish themselves from other market players. Customer satisfaction plays a crucial role in such a landscape where negative word of mouth could severely damage the reputation of a business. Customer satisfaction has therefore become a key differentiator for many companies. A popular metric to track customers' experience with a business is the Net Promoter Score® (NPS). NPS is measured by customer surveys, prompting them to answer a simple question: “How likely are you to recommend company X to a friend or colleague?” The response ranges between zero, representing not likely, to ten, representing very likely. The score value is obtained by grouping responses into three categories: Promoters, Neutrals or Detractors, and calculating the percentage difference between promoters and detractors. The more positive the value, the better overall customer perception is likely to be. A key shortcoming of NPS is that it does not provide tangible and directly interpretable reasons for customer responses. This thesis aims to establish whether machine learning models, combined with network experience data collected by passive probing of mobile network interfaces, can accurately predict whether a subscriber will likely be a detractor. In addition, we would like to understand which network experience metrics are the best indicators of poor performance and negatively influence subscriber perception. We make use of survey and network data sourced from a large mobile network operator in South Africa over six months to create modelling features for cross validation of classification models with varying complexity to predict the NPS class of subscribers. We find that mobile network data provided by present Customer Experience Management (CEM) systems may not be ideal for use in machine learning applications. The standard library of metrics and data structures used to perform classical CEM requires much effort to clean and prepare it as viable input to machine learning models. In addition, we find that all tested machine learning models, whether linear or non-linear, are poor predictors of NPS. This suggests that NPS may instead be driven by other factors, such as pricing or the interaction of customers with other processes that are more important and not represented within the present data. 2025-11-26T14:01:42Z 2025-11-26T14:01:42Z 2025 2025-11-26T13:56:30Z Thesis / Dissertation Masters MSc http://hdl.handle.net/11427/42363 en eng application/pdf Department of Statistical Sciences Faculty of Science University of Cape Town |
| spellingShingle | Machine learning Kruger, Martin Prediction of mobile network subscriber satisfaction by using network probing experience measures and machine learning |
| thesis_degree_str | Master's |
| title | Prediction of mobile network subscriber satisfaction by using network probing experience measures and machine learning |
| title_full | Prediction of mobile network subscriber satisfaction by using network probing experience measures and machine learning |
| title_fullStr | Prediction of mobile network subscriber satisfaction by using network probing experience measures and machine learning |
| title_full_unstemmed | Prediction of mobile network subscriber satisfaction by using network probing experience measures and machine learning |
| title_short | Prediction of mobile network subscriber satisfaction by using network probing experience measures and machine learning |
| title_sort | prediction of mobile network subscriber satisfaction by using network probing experience measures and machine learning |
| topic | Machine learning |
| url | http://hdl.handle.net/11427/42363 |
| work_keys_str_mv | AT krugermartin predictionofmobilenetworksubscribersatisfactionbyusingnetworkprobingexperiencemeasuresandmachinelearning |