Publicação
The accuracy of loyalty programs in small office home office and small and medium companies
| Resumo: | Telecommunications companies have enabled societies to prosper socially and economically and be more interconnected. With the decreasing data storage and processing cost, businesses are now trying to extract actionable information from the available data. This is to improve and optimise their resource allocation and planning. Although topics of segmentation and loyalty programs are of central importance in the Telecommunications industry, research about alternative ways to increase the loyalty program is limited. This study aims to provide an overview of the Loyalty Program for SoHo and SME Markets, using data mining methods to identify, classify, and predict customers' purchases. For these segments, companies were assessed with data from 347 834 clients. TPOT SKM Confusion Matrix and several methods of correlation were used. To identify if the loyalty program was accurate and valuable for clients and to determine the current state of the art. Results indicate that loyalty programs are valuable to clients and have been widely adopted. However, the most significant feature could be used to improve brand loyalty. Findings suggest that some sectors and channel sales need to be revised. Hence, more efforts should be focused on truly valuable companies/sectors. Loyalty programs are not equally suitable for all companies. Data mining technologies can be beneficial to support Vodafone in designing a more efficient and valuable loyalty program with tailored strategies and rewards. |
|---|---|
| Autores principais: | Ladeira, António Rui Mendes |
| Assunto: | Telecommunications Loyalty Automated Machine Learning Business to Business SDG 8 - Decent work and economic growth SDG 9 - Industry, innovation and infrastructure SDG 11 - Sustainable cities and communities SDG 12 - Responsible production and consumption SDG 13 - Climate action SDG 16 - Peace, justice and strong institutions SDG 17 - Partnerships for the goals |
| Ano: | 2023 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| _version_ | 1865920617110306816 |
|---|---|
| author | Ladeira, António Rui Mendes |
| author_facet | Ladeira, António Rui Mendes Ladeira, António Rui Mendes |
| author_role | author |
| contributor_name_str_mv | Pinto, Diego Costa Rita, Paulo Miguel Rasquinho Ferreira RUN |
| country_str | PT |
| creators_json_str | [{\"Person.name\":\"Ladeira, António Rui Mendes\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Pinto, Diego Costa Rita, Paulo Miguel Rasquinho Ferreira RUN |
| datacite.creators.creator.creatorName.fl_str_mv | Ladeira, António Rui Mendes |
| datacite.date.Accepted.fl_str_mv | 2023-01-25T00:00:00Z |
| datacite.date.available.fl_str_mv | 2026-01-25T00:00:00Z |
| datacite.date.embargoed.fl_str_mv | 2026-01-25T00:00:00Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Telecommunications Loyalty Automated Machine Learning Business to Business SDG 8 - Decent work and economic growth SDG 9 - Industry, innovation and infrastructure SDG 11 - Sustainable cities and communities SDG 12 - Responsible production and consumption SDG 13 - Climate action SDG 16 - Peace, justice and strong institutions SDG 17 - Partnerships for the goals |
| datacite.titles.title.fl_str_mv | The accuracy of loyalty programs in small office home office and small and medium companies |
| dc.contributor.none.fl_str_mv | Pinto, Diego Costa Rita, Paulo Miguel Rasquinho Ferreira RUN |
| dc.creator.none.fl_str_mv | Ladeira, António Rui Mendes |
| dc.date.Accepted.fl_str_mv | 2023-01-25T00:00:00Z |
| dc.date.available.fl_str_mv | 2026-01-25T00:00:00Z |
| dc.date.embargoed.fl_str_mv | 2026-01-25T00:00:00Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10362/149307 |
| dc.language.none.fl_str_mv | eng |
| dc.rights.cclincense.fl_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.subject.none.fl_str_mv | Telecommunications Loyalty Automated Machine Learning Business to Business SDG 8 - Decent work and economic growth SDG 9 - Industry, innovation and infrastructure SDG 11 - Sustainable cities and communities SDG 12 - Responsible production and consumption SDG 13 - Climate action SDG 16 - Peace, justice and strong institutions SDG 17 - Partnerships for the goals |
| dc.title.fl_str_mv | The accuracy of loyalty programs in small office home office and small and medium companies |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_bdcc |
| description | Telecommunications companies have enabled societies to prosper socially and economically and be more interconnected. With the decreasing data storage and processing cost, businesses are now trying to extract actionable information from the available data. This is to improve and optimise their resource allocation and planning. Although topics of segmentation and loyalty programs are of central importance in the Telecommunications industry, research about alternative ways to increase the loyalty program is limited. This study aims to provide an overview of the Loyalty Program for SoHo and SME Markets, using data mining methods to identify, classify, and predict customers' purchases. For these segments, companies were assessed with data from 347 834 clients. TPOT SKM Confusion Matrix and several methods of correlation were used. To identify if the loyalty program was accurate and valuable for clients and to determine the current state of the art. Results indicate that loyalty programs are valuable to clients and have been widely adopted. However, the most significant feature could be used to improve brand loyalty. Findings suggest that some sectors and channel sales need to be revised. Hence, more efforts should be focused on truly valuable companies/sectors. Loyalty programs are not equally suitable for all companies. Data mining technologies can be beneficial to support Vodafone in designing a more efficient and valuable loyalty program with tailored strategies and rewards. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | masterThesis |
| fulltext.url.fl_str_mv | https://run.unl.pt/bitstreams/172987cf-0383-4740-8420-01a8cd35bd42/download |
| id | run_7bebbcf5f60de5441b9e331c9e2ccc53 |
| identifier.url.fl_str_mv | http://hdl.handle.net/10362/149307 |
| instacron_str | unl |
| institution | Universidade Nova de Lisboa |
| instname_str | Universidade Nova de Lisboa |
| language | eng |
| network_acronym_str | run |
| network_name_str | Repositório Institucional da UNL |
| oai_identifier_str | oai:run.unl.pt:10362/149307 |
| organization_str_mv | urn:organizationAcronym:unl |
| person_str_mv | Ladeira, António Rui Mendes |
| publishDate | 2023 |
| reponame_str | Repositório Institucional da UNL |
| repository_id_str | urn:repositoryAcronym:run |
| service_str_mv | urn:repositoryAcronym:run |
| spelling | engpt_PTTelecommunications companies have enabled societies to prosper socially and economically and be more interconnected. With the decreasing data storage and processing cost, businesses are now trying to extract actionable information from the available data. This is to improve and optimise their resource allocation and planning. Although topics of segmentation and loyalty programs are of central importance in the Telecommunications industry, research about alternative ways to increase the loyalty program is limited. This study aims to provide an overview of the Loyalty Program for SoHo and SME Markets, using data mining methods to identify, classify, and predict customers' purchases. For these segments, companies were assessed with data from 347 834 clients. TPOT SKM Confusion Matrix and several methods of correlation were used. To identify if the loyalty program was accurate and valuable for clients and to determine the current state of the art. Results indicate that loyalty programs are valuable to clients and have been widely adopted. However, the most significant feature could be used to improve brand loyalty. Findings suggest that some sectors and channel sales need to be revised. Hence, more efforts should be focused on truly valuable companies/sectors. Loyalty programs are not equally suitable for all companies. Data mining technologies can be beneficial to support Vodafone in designing a more efficient and valuable loyalty program with tailored strategies and rewards.application/pdfpt_PTThe accuracy of loyalty programs in small office home office and small and medium companiesLadeira, António Rui MendesPinto, Diego CostaRita, Paulo Miguel Rasquinho FerreiraHostingInstitutionOrganizationalRUNe-mailmailto:run@unl.ptrun@unl.ptURNurn:tid:2032299162023-01-252026-01-25T00:00:00Z2023-01-25T00:00:00ZHandlehttp://hdl.handle.net/10362/149307http://purl.org/coar/access_right/c_abf2open accessTelecommunicationsLoyaltyAutomated Machine LearningBusiness to BusinessSDG 8 - Decent work and economic growthSDG 9 - Industry, innovation and infrastructureSDG 11 - Sustainable cities and communitiesSDG 12 - Responsible production and consumptionSDG 13 - Climate actionSDG 16 - Peace, justice and strong institutionsSDG 17 - Partnerships for the goals31380906 bytesliteraturehttp://purl.org/coar/resource_type/c_bdccmaster thesis2023-01-25http://creativecommons.org/licenses/by/4.0/http://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://run.unl.pt/bitstreams/172987cf-0383-4740-8420-01a8cd35bd42/download |
| spellingShingle | The accuracy of loyalty programs in small office home office and small and medium companies The accuracy of loyalty programs in small office home office and small and medium companies Ladeira, António Rui Mendes Telecommunications Loyalty Automated Machine Learning Business to Business SDG 8 - Decent work and economic growth SDG 9 - Industry, innovation and infrastructure SDG 11 - Sustainable cities and communities SDG 12 - Responsible production and consumption SDG 13 - Climate action SDG 16 - Peace, justice and strong institutions SDG 17 - Partnerships for the goals Ladeira, António Rui Mendes Telecommunications Loyalty Automated Machine Learning Business to Business SDG 8 - Decent work and economic growth SDG 9 - Industry, innovation and infrastructure SDG 11 - Sustainable cities and communities SDG 12 - Responsible production and consumption SDG 13 - Climate action SDG 16 - Peace, justice and strong institutions SDG 17 - Partnerships for the goals |
| status | NEW |
| subject.fl_str_mv | Telecommunications Loyalty Automated Machine Learning Business to Business SDG 8 - Decent work and economic growth SDG 9 - Industry, innovation and infrastructure SDG 11 - Sustainable cities and communities SDG 12 - Responsible production and consumption SDG 13 - Climate action SDG 16 - Peace, justice and strong institutions SDG 17 - Partnerships for the goals |
| title | The accuracy of loyalty programs in small office home office and small and medium companies |
| title_full | The accuracy of loyalty programs in small office home office and small and medium companies |
| title_fullStr | The accuracy of loyalty programs in small office home office and small and medium companies The accuracy of loyalty programs in small office home office and small and medium companies |
| title_full_unstemmed | The accuracy of loyalty programs in small office home office and small and medium companies The accuracy of loyalty programs in small office home office and small and medium companies |
| title_short | The accuracy of loyalty programs in small office home office and small and medium companies |
| title_sort | The accuracy of loyalty programs in small office home office and small and medium companies |
| topic | Telecommunications Loyalty Automated Machine Learning Business to Business SDG 8 - Decent work and economic growth SDG 9 - Industry, innovation and infrastructure SDG 11 - Sustainable cities and communities SDG 12 - Responsible production and consumption SDG 13 - Climate action SDG 16 - Peace, justice and strong institutions SDG 17 - Partnerships for the goals |
| topic_facet | Telecommunications Loyalty Automated Machine Learning Business to Business SDG 8 - Decent work and economic growth SDG 9 - Industry, innovation and infrastructure SDG 11 - Sustainable cities and communities SDG 12 - Responsible production and consumption SDG 13 - Climate action SDG 16 - Peace, justice and strong institutions SDG 17 - Partnerships for the goals |
| url | http://hdl.handle.net/10362/149307 |
| visible | 1 |