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The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods
In recent years, machine learning models based on big data have been introduced into marketing in order to transform customer data into meaningful insights and to make strategic decisions by making more accurate predictions. Although there is a large amount of literature on demand forecasting, there...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8736292/ https://www.ncbi.nlm.nih.gov/pubmed/35017781 http://dx.doi.org/10.1007/s10479-021-04429-x |
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author | Birim, Sule Kazancoglu, Ipek Mangla, Sachin Kumar Kahraman, Aysun Kazancoglu, Yigit |
author_facet | Birim, Sule Kazancoglu, Ipek Mangla, Sachin Kumar Kahraman, Aysun Kazancoglu, Yigit |
author_sort | Birim, Sule |
collection | PubMed |
description | In recent years, machine learning models based on big data have been introduced into marketing in order to transform customer data into meaningful insights and to make strategic decisions by making more accurate predictions. Although there is a large amount of literature on demand forecasting, there is a lack of research about how marketing strategies such as advertising and other promotional activities affect demand. Therefore, an accurate demand-forecasting model can make significant academic and practical contributions for business sustainability. The purpose of this article is to evaluate machine learning methods to provide accuracy in forecasting demand based on advertising expenses. The study focuses on a prediction mechanism based on several Machine Learning techniques—Support Vector Regression (SVR), Random Forest Regression (RFR) and Decision Tree Regressor (DTR) and deep learning techniques—Artificial Neural Network (ANN), Long Short Term Memory (LSTM),—to deal with demand forecasting based on advertising expenses. Deep learning is a powerful technique that can solve marketing problems based on both classification and regression algorithms. Accordingly, a television manufacturer’s real market dataset consisting of advertising expenditures, sales and demand forecasting via chosen machine learning methods was analyzed and compared in terms of the accuracy of demand forecasting. As a result, Long Short Term Memory has been found to be superior to other models in providing highly accurate prediction results for demand forecasting based on advertising expenses. |
format | Online Article Text |
id | pubmed-8736292 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-87362922022-01-07 The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods Birim, Sule Kazancoglu, Ipek Mangla, Sachin Kumar Kahraman, Aysun Kazancoglu, Yigit Ann Oper Res Original Research In recent years, machine learning models based on big data have been introduced into marketing in order to transform customer data into meaningful insights and to make strategic decisions by making more accurate predictions. Although there is a large amount of literature on demand forecasting, there is a lack of research about how marketing strategies such as advertising and other promotional activities affect demand. Therefore, an accurate demand-forecasting model can make significant academic and practical contributions for business sustainability. The purpose of this article is to evaluate machine learning methods to provide accuracy in forecasting demand based on advertising expenses. The study focuses on a prediction mechanism based on several Machine Learning techniques—Support Vector Regression (SVR), Random Forest Regression (RFR) and Decision Tree Regressor (DTR) and deep learning techniques—Artificial Neural Network (ANN), Long Short Term Memory (LSTM),—to deal with demand forecasting based on advertising expenses. Deep learning is a powerful technique that can solve marketing problems based on both classification and regression algorithms. Accordingly, a television manufacturer’s real market dataset consisting of advertising expenditures, sales and demand forecasting via chosen machine learning methods was analyzed and compared in terms of the accuracy of demand forecasting. As a result, Long Short Term Memory has been found to be superior to other models in providing highly accurate prediction results for demand forecasting based on advertising expenses. Springer US 2022-01-07 /pmc/articles/PMC8736292/ /pubmed/35017781 http://dx.doi.org/10.1007/s10479-021-04429-x Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Original Research Birim, Sule Kazancoglu, Ipek Mangla, Sachin Kumar Kahraman, Aysun Kazancoglu, Yigit The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods |
title | The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods |
title_full | The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods |
title_fullStr | The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods |
title_full_unstemmed | The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods |
title_short | The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods |
title_sort | derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8736292/ https://www.ncbi.nlm.nih.gov/pubmed/35017781 http://dx.doi.org/10.1007/s10479-021-04429-x |
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