Machine Learning and Marketing: A Systematic Literature Review
Even though machine learning (ML) applications are not novel, they have gained popularity partly due to the advance in computing processing. This study explores the adoption of ML methods in marketing applications through a bibliographic review of the period 2008–2022. In this period, the...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9869838/ |
| _version_ | 1798001000350482432 |
|---|---|
| author | Vannessa Duarte Sergio Zuniga-Jara Sergio Contreras |
| author_facet | Vannessa Duarte Sergio Zuniga-Jara Sergio Contreras |
| author_sort | Vannessa Duarte |
| collection | DOAJ |
| description | Even though machine learning (ML) applications are not novel, they have gained popularity partly due to the advance in computing processing. This study explores the adoption of ML methods in marketing applications through a bibliographic review of the period 2008–2022. In this period, the adoption of ML in marketing has grown significantly. This growth has been quite heterogeneous, varying from the use of classical methods such as artificial neural networks to hybrid methods that combine different techniques to improve results. Generally, maturity in the use of ML in marketing and increasing specialization in the type of problems that are solved were observed. Strikingly, the types of ML methods used to solve marketing problems vary wildly, including deep learning, supervised learning, reinforcement learning, unsupervised learning, and hybrid methods. Finally, we found that the main marketing problems solved with machine learning were related to consumer behavior, recommender systems, forecasting, marketing segmentation, and text analysis—content analysis. |
| first_indexed | 2024-04-11T11:29:05Z |
| format | Article |
| id | doaj.art-40d1f610cf5542e291dd4cd3db1e1180 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T11:29:05Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-40d1f610cf5542e291dd4cd3db1e11802022-12-22T04:26:12ZengIEEEIEEE Access2169-35362022-01-0110932739328810.1109/ACCESS.2022.32028969869838Machine Learning and Marketing: A Systematic Literature ReviewVannessa Duarte0https://orcid.org/0000-0001-5399-6620Sergio Zuniga-Jara1https://orcid.org/0000-0002-2845-0113Sergio Contreras2https://orcid.org/0000-0003-3999-1916Escuela de Ciencias Empresariales, Universidad Catolica del Norte, Larrondo, Coquimbo, ChileEscuela de Ciencias Empresariales, Universidad Catolica del Norte, Larrondo, Coquimbo, ChileEscuela de Ciencias Empresariales, Universidad Catolica del Norte, Larrondo, Coquimbo, ChileEven though machine learning (ML) applications are not novel, they have gained popularity partly due to the advance in computing processing. This study explores the adoption of ML methods in marketing applications through a bibliographic review of the period 2008–2022. In this period, the adoption of ML in marketing has grown significantly. This growth has been quite heterogeneous, varying from the use of classical methods such as artificial neural networks to hybrid methods that combine different techniques to improve results. Generally, maturity in the use of ML in marketing and increasing specialization in the type of problems that are solved were observed. Strikingly, the types of ML methods used to solve marketing problems vary wildly, including deep learning, supervised learning, reinforcement learning, unsupervised learning, and hybrid methods. Finally, we found that the main marketing problems solved with machine learning were related to consumer behavior, recommender systems, forecasting, marketing segmentation, and text analysis—content analysis.https://ieeexplore.ieee.org/document/9869838/Machine learningmarketingscientific publicationsdeep learningsupervised learningunsupervised learning |
| spellingShingle | Vannessa Duarte Sergio Zuniga-Jara Sergio Contreras Machine Learning and Marketing: A Systematic Literature Review IEEE Access Machine learning marketing scientific publications deep learning supervised learning unsupervised learning |
| title | Machine Learning and Marketing: A Systematic Literature Review |
| title_full | Machine Learning and Marketing: A Systematic Literature Review |
| title_fullStr | Machine Learning and Marketing: A Systematic Literature Review |
| title_full_unstemmed | Machine Learning and Marketing: A Systematic Literature Review |
| title_short | Machine Learning and Marketing: A Systematic Literature Review |
| title_sort | machine learning and marketing a systematic literature review |
| topic | Machine learning marketing scientific publications deep learning supervised learning unsupervised learning |
| url | https://ieeexplore.ieee.org/document/9869838/ |
| work_keys_str_mv | AT vannessaduarte machinelearningandmarketingasystematicliteraturereview AT sergiozunigajara machinelearningandmarketingasystematicliteraturereview AT sergiocontreras machinelearningandmarketingasystematicliteraturereview |