The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods

Sule Birim (), Ipek Kazancoglu (), Sachin Kumar Mangla (), Aysun Kahraman () and Yigit Kazancoglu ()
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Sule Birim: Manisa Celal Bayar University
Ipek Kazancoglu: Ege University
Sachin Kumar Mangla: OP Jindal Global University, Jindal Global Business School, Operations Management
Aysun Kahraman: Manisa Celal Bayar University
Yigit Kazancoglu: Yasar Universitesi

Annals of Operations Research, 2024, vol. 339, issue 1, No 6, 161 pages

Abstract: Abstract 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.

Keywords: Advertisement; Demand forecasting; Machine learning; Marketing intelligence (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10479-021-04429-x

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