Birim Sule, Kazancoglu Ipek, Mangla Sachin Kumar, Kahraman Aysun, Kazancoglu Yigit
Department of Business Administration, Salihli Faculty of Economics and Administrative Sciences, Manisa Celal Bayar University, Manisa, Turkey.
Department of Business Administration, Faculty of Economics and Administrative Sciences, Ege University, İzmir, Turkey.
Ann Oper Res. 2022 Jan 7:1-31. doi: 10.1007/s10479-021-04429-x.
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.
近年来，基于大数据的机器学习模型已被引入市场营销领域，以便将客户数据转化为有意义的见解，并通过做出更准确的预测来制定战略决策。尽管关于需求预测的文献众多，但对于广告等营销策略以及其他促销活动如何影响需求的研究却很匮乏。因此，一个准确的需求预测模型能够为企业可持续发展做出重大的学术和实践贡献。本文的目的是评估机器学习方法在基于广告费用预测需求方面的准确性。该研究聚焦于一种基于多种机器学习技术——支持向量回归（SVR）、随机森林回归（RFR）和决策树回归器（DTR）以及深度学习技术——人工神经网络（ANN）、长短期记忆网络（LSTM）的预测机制，以处理基于广告费用的需求预测问题。深度学习是一种强大的技术，能够基于分类和回归算法解决营销问题。据此，对一个电视制造商的真实市场数据集进行了分析和比较，该数据集包含广告支出、销售额以及通过选定的机器学习方法进行的需求预测，比较内容涉及需求预测的准确性。结果发现，长短期记忆网络在基于广告费用的需求预测方面提供高度准确的预测结果时，优于其他模型。
