A Computational Model to Predict Consumer Behaviour During COVID-19 Pandemic
Open Access
- 1 April 2022
- journal article
- research article
- Published by Springer Science and Business Media LLC in Computational Economics
- Vol. 59 (4), 1525-1538
- https://doi.org/10.1007/s10614-020-10069-3
Abstract
The knowledge-based economy has drawn increasing attention recently, particularly in online shopping applications where all the transactions and consumer opinions are logged. Machine learning methods could be used to extract implicit knowledge from the logs. Industries and businesses use the knowledge to better understand the consumer behavior, and opportunities and threats correspondingly. The outbreak of coronavirus (COVID-19) pandemic has a great impact on the different aspects of our daily life, in particular, on our shopping behaviour. To predict electronic consumer behaviour could be of valuable help for managers in government, supply chain and retail industry. Although, before coronavirus pandemic we have experienced online shopping, during the disease the number of online shopping increased dramatically. Due to high speed transmission of COVID-19, we have to observe personal and social health issues such as social distancing and staying at home. These issues have direct effect on consumer behaviour in online shopping. In this paper, a prediction model is proposed to anticipate the consumers behaviour using machine learning methods. Five individual classifiers, and their ensembles with Bagging and Boosting are examined on the dataset collected from an online shopping site. The results indicate the model constructed using decision tree ensembles with Bagging achieved the best prediction of consumer behavior with the accuracy of 95.3%. In addition, correlation analysis is performed to determine the most important features influencing the volume of online purchase during coronavirus pandemic.This publication has 26 references indexed in Scilit:
- A model checking approach for user relationship management in the social networkKybernetes, 2019
- Studying the Contribution of Machine Learning and Artificial Intelligence in the Interface Design of E-commerce SitePublished by Springer Science and Business Media LLC ,2018
- User behaviour modeling, recommendations, and purchase prediction during shopping festivalsElectronic Markets, 2018
- A Data Mining Approach to Predict E-Commerce Customer BehaviourPublished by Springer Science and Business Media LLC ,2018
- An artificial intelligence system for predicting customer default in e-commerceExpert Systems with Applications, 2018
- Customer Churn Prediction Modelling Based on Behavioural Patterns Analysis using Deep LearningPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2018
- Software as a service based CRM providers in the cloud computing: Challenges and technical issuesJournal of Service Science Research, 2017
- Comparison of supervised machine learning techniques for customer churn prediction based on analysis of customer behaviorJournal of Systems and Information Technology, 2017
- Analysis of the behavior of customers in the social networks using data mining techniquesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2016
- Predicting customer purchase behavior in the e-commerce contextElectronic Commerce Research, 2015