Predicting Consumer Behaviour and Results Using Social Media and Deep Learning

The personal connections made possible by social networking sites between employees and brand representatives are encouraging to current and potential clients. Consumers regularly utilize social media brand forums to research and learn about the brands they consider purchasing. The customer brand supplies play a role in determining how consumers benefit from their interactions and impressions of a brand. In light of this, the authors of this study propose a deep learning-based prediction model (DL-PM) for estimating the preferences of social media users. Items were developed using information from a previous study utilizing the proposed DL-PM method. The suggested DL-PM considers the effects of consumers' previous behaviors to enhance the probability of future outcomes. By combining a social network business strategy with other factors, such as Deep Learning, companies may provide engaging content to customers at every possible interaction point. The experimental findings demonstrate that the DL-PM outperforms traditional statistical methods in predicting market choice in nonlinear and linear settings. Analysis of consumer behavior (93.5%), the quality of the item (96.2%), client fulfillment (97.5%), accuracy (97.8%), and timely delivery (97.5%) were all shown to improve with the use of DL-PM.