Marketing forecasting based on Big Data information

Abstract

In the paper discusses the use of big data as a tool to increase data transfer speed while providing access to multidimensional data in the process of forecasting product sales in the market. In this paper discusses modern big data tools that use the MapReduce model. The big data presented in this article is a single, centralized source of information across your entire domain. In the paper also proposes the structure of a marketing analytics system that includes many databases in which transactions are processed in real time. For marketing forecasting of multidimensional data in Matlab, a neural network is considered and built. For training and building a network, it is proposed to construct a matrix of input data for presentation in a neural network and a matrix of target data that determine the output statistical information. Input and output data in the neural network is presented in the form of a 5x10 matrix, which represents static information about 10 products for five days of the week. The application of the Levenberg-Marquardt algorithm for training a neural network is considered. The results of the neural network training process in Matlab are also presented. The obtained forecasting results are given, which allows us to conclude about the advantages of a neural network in multivariate forecasting in real time.

Sales forecasting of marketing using adaptive response rate single exponential smoothing algorithm

“…Other study analyzed power bumi product sales during the COVID-19 pandemic, compared forecasting methods, and determined that exponential smoothing alpha 0.9 resulted in the most accurate forecast of 627.628 boxes with a median absolute deviation (MAD) value of 130.329, MSE of 28,251.23, and MAPE of 22.00% [13]. Another study employs the trend moment method to predict Unilever Indonesia's sales and earnings by year-end, with a MAPE error rate below 10% indicating the method's success [14]. In addition, machine learning methods have also been developed for the purpose of predicting product sales using several algorithms such as artificial neural networks [15], support vector machine [16], linear regression [17] and random forest [18].…”