Prediction Analysis for Business To Business (B2B) Sales of Telecommunication Services using Machine Learning Techniques

  • Oryza Wisesa Department of Electrical Engineerng, Universitas Mercu Buana, Jakarta, Indonesia.
  • Andi Andriansyah Department of Electrical Engineerng, Universitas Mercu Buana, Jakarta, Indonesia.
  • Osamah Ibrahim Khalaf College of Information Engineering, Al-Nahrain University, Baghdad, Iraq.
Keywords: B2B, Business to business, Economy Engineering, Machine Learning Techniques, Sales Forecasting, Prediction, Telecommunication, Reliability


Sales prediction analysis requires intelligent data mining techniques with accurate prediction models and high reliability. In most cases, business highly relies on information as well as demand forecast of the sales trends. This research uses B2B sales data for analysis. The B2B data could provide information on how telecommunication company should manage its sales team, products, and budgeting flows. The accurate estimates enable Telecommunication company to survive the market war and increase with market growth. Comprehensible predictive models were studied and analyzed using a technique of machine learning to improve the prediction of the future sale. It is hard to cope with big data and sale prediction accuracy if the system of traditional forecast is used. In this study, machine learning technique was also used to analyze the reliability of B2B sales. In addition, at the end of this research, other measures and techniques used to predict sales were introduced. The predictive model with best performance evaluation is recommended to forecast the trending B2B sales. The study results are put into an order of reliability and accuracy of the best method to predict and forecast including estimation, evaluation, and transformation. The best performance model found was Gradient Boost Algorithm. The result form graph the data close together from beginning till end of data target MSE and MAPE result are the best result than other method, MSE =24.743.000.000,00 and MAPE =0,18. This model performed maximum accuracy in predicting and forecasting of the future B2B sales.


[1] Sastry, S. H., Babu, P., & Prasada, M. S. “Analysis & Prediction of Sales Data in SAP-ERP System using Clustering Algorithms”. arXiv preprint arXiv:1312.2678, 2013.
[2] Shrivastava, V., & Arya, N. “A study of various clustering algorithms on retail sales data”. Int. J. Comput. Commun. Netw, 1(2), 2012.
[3] Rajagopal, D. “Customer data clustering using data mining technique”. arXiv preprint arXiv:1112.2663, 2011.
[4] Mann, A. K., & Kaur, N. “Review paper on clustering techniques”. Global Journal of Computer Science and Technology, 2013.
[5] Shah, N., Solanki, M., Tambe, A., & Dhangar, D. “Sales Prediction Using Effective Mining Techniques”, 2015.
[6] D’Arcy, B., Gallagher, C., and Madden, M. G., “A Bayesian Classification Approach to Improving Performance for a Real-World Sales Forecasting Application,” 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), 10.1109/ICMLA.2015.150, 2015.
[7] Aleksandrovich, P.V., Leopoldovich, K.I., and Viktorovich, P.A., “Predicting Sales Prices of the Houses Using Regression Methods of Machine Learning”, 3rd Russian-Pacific Conference on Computer Technology and Applications (RPC), 10.1109/RPC.2018.8482191, 2018.
[8] Ching-She, W., Patil, P., and Gunaseelan, S., “Comparison of Different Machine Learning Algorithms for Multiple Regression on Black Friday Sales Data,” 2018 IEEE 9th International Conference on Software Engineering and Service Science (ICSESS), 10.1109/ICSESS.2018.8663760, 2018.
[9] Cheriyan, S., Ibrahim, S., Mohanan, J., and Treesa, S., “Intelligent Sales Prediction Using Machine Learning Techniques”, 2018 International Conference on Computing, Electronics & Communications Engineering (iCCECE), 10.1109/iCCECOME.2018.8659115, 2018.
[10] Ullah, I,. Raza, B., Malik, A.K, Imran, M., Islam, S.U., Kim, S.W., “A Churn Prediction Model using Random Forest: Analysis of Machine Learning Techniques for Churn Prediction and Factor Identification in Telecom Sector,” IEEE Access, 2019.
[11] Rey, T. D., Wells, C., & Kauhl, J. “Using data mining in forecasting problems”. In SAS Global Forum 2013: Data Mining and Text Analytics, 2013.
[12] Huang, W., Zhang, Q., Xu, W., Fu, H., Wang, M., & Liang, X. “A Novel Trigger Model for Sales Prediction with Data Mining Techniques”. Data Science Journal, 14, 2015.
[13] Alpaydin, E. “Introduction to Machine Learning (Adaptive Computation and Machine Learning)”, The MIT Press, 2004.
[14] Lazăr, C., & Lazăr, M. “Using the Method of Decision Trees in the Forecasting Activity. Petroleum-Gas University of Ploiesti Bulletin”, Technical Series, Vol. 67(1), 2015.
[15] Flesch, B., Vatrapu, R., Mukkamala, R. R., & Hussain, A. “Social set visualizer: A set theoretical approach to big social data analytics of real-world events”, In Big Data (Big Data), 2015 IEEE Internationsal Conference on, pp. 2418-2427, 2015.
[16] Asooja, K., Bordea, G., Vulcu, G., & Buitelaar, P. “Forecasting Emerging Trends from Scientific Literature”. In LREC, 2016.
[17] Botchkarev, A. “Performance Metrics (Error Measures) in Machine Learning Regression, Forecasting and Prognostics: Properties and Typology”, arXiv preprint arXiv: 1809.03006, 2018.
[18] PREDA, C. and SAPORTA, G. “PLS approach for clusterwise linear regression on functional data”. In Classification, Clustering, and Data Mining Applications (D. Banks, L. House, F. R. McMorris, P. Arabie and W, 2004.
How to Cite
Wisesa, O., Andriansyah, A., & Khalaf, O. (2020). Prediction Analysis for Business To Business (B2B) Sales of Telecommunication Services using Machine Learning Techniques. Majlesi Journal of Electrical Engineering, 14(4), 145-153.