A Discretized Recurrent Deep Learning Classifier based on Stochastic Gradient ChatGPT to Improve Lead Conversion Rate
Raghavendra M. Ichangi, S. B. Kulkarni · Engineering Technology & Applied Science Research · 2025
In the vast domain of digital marketing, lead generation forms the foundation for business development. Business strategies depend on converting the leads into customers. It has become very crucial and challenging to choose an appropriate digital platform for marketing. The proposed method, called Stochastic Gradient ChatGPT-based Discretized Recurrent Deep Learning Classification (SG-CDRDLC), employs an efficient way for lead conversion based on influencing feature keywords. The DL classifier with two hidden layers allows companies to determine the popularity of the keywords in the first layer. The second layer measures the keyword density based on a variety of user queries to evaluate and enhance the conversion rate. The proposed model was trained and tested on three datasets and compared against existing methods using accuracy, precision, recall, and training time.