Predictive Analysis of User Purchase Behavior based on Machine Learning
AUTHORS
Zhenyu Liu,Harbin University of Science and Technology, China
Xinyi Ma,Harbin University of Science and Technology, China
ABSTRACT
In corporate customer management, companies are required to evaluate the costs and benefits of investment expenditures and determine the optimal resource allocation for marketing and sales activities within a period. Understanding the buying behavior of customers in the future is a key driving force for the sales and marketing departments to effectively allocate resources. This paper proposes a combined prediction model that uses the Stacking method to integrate multiple decision tree models to predict whether users will buy in the future and their specific purchase time. The model uses the idea of stacking model fusion to fuse the prediction results of three different integrated decision tree models of Light GBM, XG Boost, and Random Forest, and then uses a simple logistic regression classification model and a linear regression model to predict separately based on the fused prediction results Whether the user will buy in the future and the specific time of purchase. In addition, in this study, we used real retail sales data to evaluate the predictive performance of the proposed method.
KEYWORDS
Machine learning, Buying behavior prediction, Light GBM algorithm, Stacking integration
REFERENCES
[1] J. D. Herniter, “An entropy model of brand purchase behavior,” Journal of Marketing Research, vol.10, no.4, pp.361-375, (1973)
[2] M. Platzer and T. Reutterer, “Ticking away the moments: Timing regularity helps to better predict customer activity,” Marketing Science, vol.35, no.5, pp.779-799, (2016)
[3] X. Wu, X. Zhu, and G. Q. Wu, “Data mining with big data,” IEEE Transactions on Knowledge and Data Engineering, vol.26, no.1, pp.97-107, (2013)
[4] Y. Richer, E. Tom-Tov, and N. Slomin, “Predicting customer churn in mobile networks through analysis of social groups,” Proceedings of the 2010 SIAM international conference on data mining. Society for Industrial and Applied Mathematics, pp.732-741, (2010)
[5] C. S. Lin, G. H. Tzeng, Y. C. Chin, “Combined rough set theory and flow network graph to predict customer churn in credit card accounts,” Expert Systems with Applications, vol.38, no.1, pp.8-15, (2011)
[6] A. Amin, S. Anwar, and A. Adnan, “Customer churn prediction in the telecommunication sector using a rough set approach,” Neurocomputing, no.237, pp.242-254, (2017)
[7] M. Hussan, A. Shome, and D. M. Lee, “Impact of forecasting methods on variance ratio in order-up-to level policy,” The International Journal of Advanced Manufacturing Technology, vol.59, no.1,2,3,4, pp.413-420, (2012)
[8] S. Lee, and D. B. Fambro, “Application of subset autoregressive integrated moving average model for short term freeway traffic volume forecasting,” Transportation Research Record, vol.1678, no.1, pp.179-188, (1999)
[9] P. Ramos, N. Santos, and R. Rebello, “Performance of state space and ARIMA models for consumer retail sales forecasting,” Robotics and Computer-integrated Manufacturing, no.34, pp.151-163, (2015)
[10] S. Ren, T. M. Choi, and N. Liu, “Fashion sales forecasting with a panel data-based particle-filter model,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol.45, no.3, pp.411-421, (2014)
[11] P. S. Fader and B. G. S. Hardie ‘A note on an integrated model of customer buying behavior,” European Journal of Operational Research, vol.139, no.3, pp.682-687, (2002)
[12] T. M. Choi, C. L. Hui, N. Liu, “Fast fashion sales forecasting with limited data and time,” Decision Support Systems, no.59, pp.84-92, (2014)
[13] C. J. Lu, “Sales forecasting of computer products based on variable selection scheme and support vector regression,” Neurocomputing, no.128, pp.491-499, (2014)
[14] A. Candelieri, “Clustering and support vector regression for water demand forecasting and anomaly detection,” Water, vol.9, no.3, pp.224, (2017)
[15] M. E. Gnay, “Forecasting annual gross electricity demand by artificial neural networks using predicted values of socio-economic indicators and climatic conditions: A case of Turkey,” Energy Policy, no.90, pp.92-101, (2016)