Analyzing the impact of social networks and social behavior on electronic business during COVID-19 pandemic

The Covid-19 pandemic caused substantial changes, particularly concerning marketing, which led to high digital use. Social networking enables people to communicate easily with others and provides marketers with many ways to interact with consumers. As a consequence of the lockdown, economic activity is declining dramatically. The response of policymakers, the government, and industry to resolving the harm caused by economic factors and how the marketer can react to changing consumer behavior. This study analyzes the impact of social networks and social behavior on electronic business or E-Business during the COVID-19 pandemic using deep learning techniques. This paper introduces the Deep Recurrent Neural Network (DRNN) to predict online shopping behavior for improving E-business performance. The article utilizes clickstream information to forecast online purchase behavior in real-time and target marketing measures. Measures of profit impact with production from classifier metrics demonstrate the feasibility and the usage of deep recurrent learners in campaign targeting via RNN-based clickstream modeling. The numerical results show that the suggested model enhances the profitability ratio of 98.5%, the performance ratio of 97.5%, the accuracy ratio of 96.7%, the prediction ratio of 97.9%, and less error rate of 11.3% other existing methods.