Welcome to Francis Academic Press

International Journal of New Developments in Engineering and Society, 2022, 6(1); doi: 10.25236/IJNDES.2022.060104.

Predict a UK Customer’s Likelihood of Making an Online-purchase Based on the Logistic Regression Model


Jiayi Gu

Corresponding Author:
Jiayi Gu

University of Southampton, University Road, Southampton, SO17 1BJ, United Kingdom


With the rapid development of e-commerce, the scale of e-commerce consumers and online transaction volume has increased rapidly. Before e-commerce, shopping was entirely based on in-person interactions. Conventional methods of purchasing and selling were followed due to the relatively low requirements of consumers. However, due to the rapid development of Internet technology, more and more consumers choose purchase online. This study proposes to review Logistic Regression model on customers’ purchase likelihood analysis and prediction. Also, depending on the different advantages and disadvantages of Logistic Regression model, this study aims at building predict models which is suitable for the Wiggle Ltd. Finally, it will screen out the relationships between variables for recommendation and further evaluation.


Logistic Regression, Predict Models, Purchase Likelihood, Customer Behaviours

Cite This Paper

Jiayi Gu. Predict a UK Customer’s Likelihood of Making an Online-purchase Based on the Logistic Regression Model. International Journal of New Developments in Engineering and Society (2022) Vol.6, Issue 1: 20-27. https://doi.org/10.25236/IJNDES.2022.060104.


[1] Shen, B. and Chan, H.L., 2017. Forecast information sharing for managing supply chains in the big data era: Recent development and future research. Asia-Pacific Journal of Operational Research, 34(01), p.1740001.

[2] Savrul, M., Incekara, A., & Sener, S. (2014). The Potential of E-commerce for SMEs in a Globalizing Business Environment. Procedia - Social and Behavioral Sciences. https://doi.org/10.1016/j.sbspro.2014.09.005

[3] Shen, Y., Jiang, Y., Liu, W., & Liu, Y. (2015). Multi-class AdaBoost ELM. https://doi.org/10.1007/978-3-319-14066-7_18

[4] Sadagopan, S. (2008). E-commerce. In Operations Research Applications. https://doi.org/10.4018/ijwp.2014070104

[5] Sivapalan, S., Sadeghian, A., Rahnama, H., & Madni, A. M. (2014). Recommender systems in e-commerce. World Automation Congress Proceedings. 

[6] Manning, C. (2007). Logistic regression (with R). Changes. https://doi.org/10.1017/CBO9781107415324.004

[7] Lehman, M. ., Ramil, J. F., Wernick, P. D., Perry, D. E., & Turski, W. M. (1997). Metrics and laws of software evolution. Software Metrics Symposium. https://doi.org/10.1109/METRIC.1997.637156

[8] Burns, R. P., & Burns, R. (2008). Chapter 24 Logistic Regression. Business Research Methods and Statistics Using SPSS.

[9] Chang, C. C., & Lin, C. J. (2011). LIBSVM: A Library for support vector machines. ACM Transactions on Intelligent Systems and Technology. https://doi.org/10.1145/1961189.1961199

[10] Grégoire,G.(2015).Logistic regression. EAS Publications Series. https://doi.org/10.1051/eas/1466008

[11] Moshrefjavadi, M. H., Rezaie Dolatabadi, H., Nourbakhsh, M., Poursaeedi, A., & Asadollahi, A. (2012). An Analysis of Factors Affecting on Online Shopping Behavior of Consumers. International Journal of Marketing Studies. https://doi.org/10.5539/ijms.v4n5p81

[12] Davis, L. J., & Offord, K. P. (2013). Logistic regression. In Emerging Issues and Methods in Personality Assessment. https://doi.org/10.4324/9780203774618-23

[13] Pampel, F. (2011). Probit Analysis. In Logistic Regression. https://doi.org/10.4135/9781412984805.n4

[14] Sperandei, S. (2014). Understanding logistic regression analysis. Biochemia Medica. https://doi.org/10.11613/BM.2014.003

[15] LaValley,M.P.(2008).Logistic regression.In Circulation. https://doi.org/10.1161/CIRCULATIONAHA.106.682658

[16] Sarkis, J. (2001). Benchmarking for agility. Benchmarking: An International Journal. https://doi.org/10.1108/14635770110389816

[17] Brownlee, J., 2016. Supervised and unsupervised machine learning algorithms. Machine Learning Mastery, 16(03).

[18] Kissell, R. L., & Poserina, J. (2017). Optimal Sports Math, Statistics, and Fantasy. Academic Press.