A Novel Framework to Investigate the Impact of Social Media Advertising Features on Customer Purchase Intention Using Bwo-Dann

Abstract

Social Media (SM) has turned out to be a platform for marketing and advertising activities. Nevertheless, it is always a challenge for organizations to model SM Advertising (SMA) in a means to effectively attract and also motivate customers into purchasing their brands. This paper proposed a novel framework to scrutinize the SMA features’ impact on Customer Purchase Intention (CPI) using the Black Widow Optimization-based Deep Artificial Neural Network (BWO-DANN). Initially, the questionnaires are given to the various customer and the collected answers will be uploaded and are converted into numerical format into the system. The Chicken Swarm Genetic Algorithm-based K-Means (CSGA-KM) is utilized for clustering the questionnaires based on personal information. Then the BWO-DANN is utilized to train the converted questionnaire set. Then, the system is tested by utilizing K-Fold Cross Validation (KFCV). Finally, through the mean model, CPI is found out. The experimentation’s outcomes illustrated the system's effectiveness.