Marketing Research : The Application of Auto Sales Forecasting Software to Optimize Product Marketing Strategies

  • Rizal Bakri Departmen of Accounting of STIEM Bongaya (ID)
  • Umar Data Department of Management of STIEM Bongaya (ID)
  • Andika Saputra epartment of Mathematics Education of STKIP YPUP (ID)
Keywords: Computational Intelligence Website, Auto Sales Forecasting, shiny dashboard package, Marketing Strategies, forecast

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Abstract

The aims of this study is to apply the Auto Sales Forecasting software to predict sales transaction data. The Auto Sales Forecasting software consists of two main features namely descriptive analysis and forcasting features along with its visualization. Forecasting methods contained in the Auto Sales Forecasting application are forecasting methods of Simple Moving Average, Robust Exponantial Smoothing, Auto ARIMA, Artificial Neural Network, Holt-Winters, and Hybrid Forecast. The Auto Sales Forecasting software can intelligently choose the best forecasting method based on RMSE values. The results showed that the Auto Sales Forecasting software successfully analyzed the sales transaction data. From the analysis it was found that there were 43 types of products produced and sold by the Futry Bakery & Cake Store. Three of them are the types of products that are most in demand by consumers, namely Sweet Bread, Maros Bread, and Traditional Cakes 3500. The best selling product type, Sweet Bread, is used to build forecasting models. The best forecasting method is the Robust Exponential Smoothing method with the smallest RMSE value of 0.83 on the variable number of sold out products. Forecasting results using the Robust Exponantial Smoothing method show that the average number of products to sell for the next seven days ranges from 116 products with a certain confidence interval value.



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Author Biographies

Rizal Bakri, Departmen of Accounting of STIEM Bongaya

Jl. Mappaoudang No. 28, Makassar 90223, Indonesia

Umar Data, Department of Management of STIEM Bongaya

Jl. Mappaoudang No. 28, Makassar 90223, Indonesia

References

Assauri S. 2013. Manajemen Pemasaran. Raja Grafindo : Depok

Bakri R, Halim A, Astuti NP. 2018. Sistem Informasi Strategi Pemasaran Produk dengan Metode Market Basket Analysis dan Sales Forecasting : Swalayan Kota Makassar. Jurnal Manajemen Teori dan Terapan 11(2): 89-106

Swastha, Basu, dan Irawan. 2008. Manajemen Pemasaran Modern. Yogyakarta: Liberty.

Montgomery DC, Jennings CL, Kulahci M. 2008. Introduction to Time Series Analysis and Forecasting. John Wiley & Sons, Inc : Hoboken, New Jersey.

Crevits R & Croux C. 2016. Forecasting with Robust Exponential Smoothing with Damped Trend and Seasonal Components. SSRN : KBI_1741 https://dx.doi.org/10.2139/ssrn.3068634

Shumway RH, Stoffer DS. 2011. Time Series Analysis and Its Applications with R Examples 3nd. Springer : New York USA.

Desrosiers. 2013. Feedforward Artificial Neural Network optimized by Genetic Algorithm. URL http://www2.uaem.mx/r-mirror/web/packages/ANN/ANN.pdf. R Version : 0.1.4.

Shumway RH, Stoffer DS. 2011. Time Series Analysis and Its Applications with R Examples 3nd. Springer : New York USA.

Hyndman R, Athanasopoulos G, Bergmeir C, Caceres G, Chhay L, O’Hara-Wild M, et al. Forecasting Functions for Time Series and Linear Models. 2019; Available from: https://cran.r-project.org/web/packages/forecast/forecast.pdf

Svetunkov I. 2018. Smooth : Forecasting using State Space Models. R Package version 2.4.7. URL https://cran.r-project.org/package=smooth.

Crevits R, Bergmeir C, Hyndman R. 2018. Forecasting Time series with Robust Exponential Smoothing. URL : https://cran.r-project.org/web/packages/robets/robets.pdf. R Version Package 1.4.

Hyndman R dkk. 2018. Forecasting Functions for Time Series and Linear Models. URL : https://cran.r-project.org/web/packages/forecast/forecast.pdf. R Package Version : 8.4.

R Core Team. 2017. R : A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Austria. URL https://www.r-project.org/

Shaub D, Peter Ellis. Convenient Functions for Ensemble Time Series Forecasts. CRAN [Internet]. 2019; Available from: https://cran.r-project.org/web/packages/forecastHybrid/forecastHybrid.pdf

Rahman A and Ahmar AS. 2017. Forecasting of primary energy consumption data in the United States: A comparison between ARIMA and Holer-Winters. AIP Conference Proceedings 1885.

Published
2019-11-10
Section
Articles
How to Cite
[1]
R. Bakri, U. Data, and A. Saputra, “Marketing Research : The Application of Auto Sales Forecasting Software to Optimize Product Marketing Strategies”, J. Appl. Sci. Eng. Technol. Educ., vol. 1, no. 1, pp. 6-12, Nov. 2019.