Publicação
Using Machine Learning in E-commerce to Predict Customer Churn and Identify Supply Chain Inefficiencies
| Resumo: | In the highly competitive landscape of retail e-commerce, customer churn poses a significant challenge, undermining profitability and growth. Understanding the drivers of customer churn within supply chain operations can illuminate pathways to enhance operational efficiencies. This study aims to identify critical factors contributing to customer churn in retail e-commerce, leveraging advanced predictive analytics to uncover underlying supply chain inefficiencies. The study followed the CRISP-DM methodology. Four classification models were tested: two single classification methods (Neural Networks and K-Nearest Neighbors) and two ensemble methods (Random Forest and XGBoost). The results revealed that the XGBoost model outperformed the others, demonstrating superior performance across all evaluation metrics. Utilizing the SHAP method, as predicted, it was determined that supply chain variables had the most significant impact on the model's predictions. This finding underscores the critical role of supply chain factors in influencing customer churn rates. This research contributes to the academic literature by integrating predictive analytics of customer churn with supply chain operations. For business practitioners, the implications are insightful. Companies can leverage this insight to optimize their operations and enhance customer retention strategies by identifying specific supply chain inefficiencies that drive customer churn. |
|---|---|
| Autores principais: | Ferreira, Katia Florinda Narcy |
| Assunto: | Customer Churn Machine Learning Supply Chain Ensemble Methods E-commerce Churn Prediction SDG 4 - Quality education SDG 8 - Decent work and economic growth SDG 9 - Industry, innovation and infrastructure |
| Ano: | 2024 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso embargado |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| _version_ | 1865920621144178689 |
|---|---|
| author | Ferreira, Katia Florinda Narcy |
| author_facet | Ferreira, Katia Florinda Narcy Ferreira, Katia Florinda Narcy |
| author_role | author |
| contributor_name_str_mv | António, Nuno Miguel da Conceição RUN |
| country_str | PT |
| creators_json_str | [{\"Person.name\":\"Ferreira, Katia Florinda Narcy\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | António, Nuno Miguel da Conceição RUN |
| datacite.creators.creator.creatorName.fl_str_mv | Ferreira, Katia Florinda Narcy |
| datacite.date.Accepted.fl_str_mv | 2024-10-31T00:00:00Z |
| datacite.date.available.fl_str_mv | 2027-10-31T00:00:00Z |
| datacite.date.embargoed.fl_str_mv | 2027-10-31T00:00:00Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_f1cf |
| datacite.subjects.subject.fl_str_mv | Customer Churn Machine Learning Supply Chain Ensemble Methods E-commerce Churn Prediction SDG 4 - Quality education SDG 8 - Decent work and economic growth SDG 9 - Industry, innovation and infrastructure |
| datacite.titles.title.fl_str_mv | Using Machine Learning in E-commerce to Predict Customer Churn and Identify Supply Chain Inefficiencies |
| dc.contributor.none.fl_str_mv | António, Nuno Miguel da Conceição RUN |
| dc.creator.none.fl_str_mv | Ferreira, Katia Florinda Narcy |
| dc.date.Accepted.fl_str_mv | 2024-10-31T00:00:00Z |
| dc.date.available.fl_str_mv | 2027-10-31T00:00:00Z |
| dc.date.embargoed.fl_str_mv | 2027-10-31T00:00:00Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10362/174692 |
| 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_f1cf |
| dc.subject.none.fl_str_mv | Customer Churn Machine Learning Supply Chain Ensemble Methods E-commerce Churn Prediction SDG 4 - Quality education SDG 8 - Decent work and economic growth SDG 9 - Industry, innovation and infrastructure |
| dc.title.fl_str_mv | Using Machine Learning in E-commerce to Predict Customer Churn and Identify Supply Chain Inefficiencies |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_bdcc |
| description | In the highly competitive landscape of retail e-commerce, customer churn poses a significant challenge, undermining profitability and growth. Understanding the drivers of customer churn within supply chain operations can illuminate pathways to enhance operational efficiencies. This study aims to identify critical factors contributing to customer churn in retail e-commerce, leveraging advanced predictive analytics to uncover underlying supply chain inefficiencies. The study followed the CRISP-DM methodology. Four classification models were tested: two single classification methods (Neural Networks and K-Nearest Neighbors) and two ensemble methods (Random Forest and XGBoost). The results revealed that the XGBoost model outperformed the others, demonstrating superior performance across all evaluation metrics. Utilizing the SHAP method, as predicted, it was determined that supply chain variables had the most significant impact on the model's predictions. This finding underscores the critical role of supply chain factors in influencing customer churn rates. This research contributes to the academic literature by integrating predictive analytics of customer churn with supply chain operations. For business practitioners, the implications are insightful. Companies can leverage this insight to optimize their operations and enhance customer retention strategies by identifying specific supply chain inefficiencies that drive customer churn. |
| dirty | 0 |
| eu_rights_str_mv | embargoedAccess |
| format | masterThesis |
| fulltext.url.fl_str_mv | https://run.unl.pt/bitstreams/23d2818a-c0b9-4c75-884d-2edf0f329ea6/download |
| id | run_b79009bfc52d0ace057b140f948e4dbf |
| identifier.url.fl_str_mv | http://hdl.handle.net/10362/174692 |
| 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:10362/174692 |
| organization_str_mv | urn:organizationAcronym:unl |
| person_str_mv | Ferreira, Katia Florinda Narcy |
| publishDate | 2024 |
| reponame_str | Repositório Institucional da UNL |
| repository_id_str | urn:repositoryAcronym:run |
| service_str_mv | urn:repositoryAcronym:run |
| spelling | engpt_PTIn the highly competitive landscape of retail e-commerce, customer churn poses a significant challenge, undermining profitability and growth. Understanding the drivers of customer churn within supply chain operations can illuminate pathways to enhance operational efficiencies. This study aims to identify critical factors contributing to customer churn in retail e-commerce, leveraging advanced predictive analytics to uncover underlying supply chain inefficiencies. The study followed the CRISP-DM methodology. Four classification models were tested: two single classification methods (Neural Networks and K-Nearest Neighbors) and two ensemble methods (Random Forest and XGBoost). The results revealed that the XGBoost model outperformed the others, demonstrating superior performance across all evaluation metrics. Utilizing the SHAP method, as predicted, it was determined that supply chain variables had the most significant impact on the model's predictions. This finding underscores the critical role of supply chain factors in influencing customer churn rates. This research contributes to the academic literature by integrating predictive analytics of customer churn with supply chain operations. For business practitioners, the implications are insightful. Companies can leverage this insight to optimize their operations and enhance customer retention strategies by identifying specific supply chain inefficiencies that drive customer churn.application/pdfpt_PTUsing Machine Learning in E-commerce to Predict Customer Churn and Identify Supply Chain InefficienciesFerreira, Katia Florinda NarcyAntónio, Nuno Miguel da ConceiçãoHostingInstitutionOrganizationalRUNe-mailmailto:run@unl.ptrun@unl.ptURNurn:tid:2037787902024-10-312027-10-31T00:00:00Z2024-10-31T00:00:00ZHandlehttp://hdl.handle.net/10362/174692http://purl.org/coar/access_right/c_f1cfembargoed accessCustomer ChurnMachine LearningSupply ChainEnsemble MethodsE-commerceChurn PredictionSDG 4 - Quality educationSDG 8 - Decent work and economic growthSDG 9 - Industry, innovation and infrastructure722523 bytesliteraturehttp://purl.org/coar/resource_type/c_bdccmaster thesis2024-10-31http://creativecommons.org/licenses/by/4.0/http://purl.org/coar/access_right/c_f1cfapplication/pdffulltexthttps://run.unl.pt/bitstreams/23d2818a-c0b9-4c75-884d-2edf0f329ea6/download |
| spellingShingle | Using Machine Learning in E-commerce to Predict Customer Churn and Identify Supply Chain Inefficiencies Using Machine Learning in E-commerce to Predict Customer Churn and Identify Supply Chain Inefficiencies Ferreira, Katia Florinda Narcy Customer Churn Machine Learning Supply Chain Ensemble Methods E-commerce Churn Prediction SDG 4 - Quality education SDG 8 - Decent work and economic growth SDG 9 - Industry, innovation and infrastructure Ferreira, Katia Florinda Narcy Customer Churn Machine Learning Supply Chain Ensemble Methods E-commerce Churn Prediction SDG 4 - Quality education SDG 8 - Decent work and economic growth SDG 9 - Industry, innovation and infrastructure |
| status | NEW |
| subject.fl_str_mv | Customer Churn Machine Learning Supply Chain Ensemble Methods E-commerce Churn Prediction SDG 4 - Quality education SDG 8 - Decent work and economic growth SDG 9 - Industry, innovation and infrastructure |
| title | Using Machine Learning in E-commerce to Predict Customer Churn and Identify Supply Chain Inefficiencies |
| title_full | Using Machine Learning in E-commerce to Predict Customer Churn and Identify Supply Chain Inefficiencies |
| title_fullStr | Using Machine Learning in E-commerce to Predict Customer Churn and Identify Supply Chain Inefficiencies Using Machine Learning in E-commerce to Predict Customer Churn and Identify Supply Chain Inefficiencies |
| title_full_unstemmed | Using Machine Learning in E-commerce to Predict Customer Churn and Identify Supply Chain Inefficiencies Using Machine Learning in E-commerce to Predict Customer Churn and Identify Supply Chain Inefficiencies |
| title_short | Using Machine Learning in E-commerce to Predict Customer Churn and Identify Supply Chain Inefficiencies |
| title_sort | Using Machine Learning in E-commerce to Predict Customer Churn and Identify Supply Chain Inefficiencies |
| topic | Customer Churn Machine Learning Supply Chain Ensemble Methods E-commerce Churn Prediction SDG 4 - Quality education SDG 8 - Decent work and economic growth SDG 9 - Industry, innovation and infrastructure |
| topic_facet | Customer Churn Machine Learning Supply Chain Ensemble Methods E-commerce Churn Prediction SDG 4 - Quality education SDG 8 - Decent work and economic growth SDG 9 - Industry, innovation and infrastructure |
| url | http://hdl.handle.net/10362/174692 |
| visible | 1 |