A Discretized Recurrent Deep Learning Classifier based on Stochastic Gradient ChatGPT to Improve Lead Conversion Rate

Authors

  • Raghavendra M. Ichangi Visvesvaraya Technological University, Belagavi-590018, Karnataka, India | Data Science, Sphoorthy Engineering College Hyderabad, Telangana, India
  • Shrinivasrao B. Kulkarni Department of Computer Science and Engineering, SDM College of Engineering and Technology, Dharwad Affiliated to Visvesvaraya Technological University, Belagavi-590018 Karnataka, India
Volume: 15 | Issue: 3 | Pages: 22712-22717 | June 2025 | https://doi.org/10.48084/etasr.9549

Received: 9 November 2024 | Revised: 7 December 2024 and 19 December 2024 | Accepted: 21 December 2024 | Online: 25 April 2025


Corresponding author: Raghavendra M. Ichangi

Abstract

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.

Keywords:

digital marketing, deep learning, stochastic gradient, accuracy, precision, recall

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How to Cite

[1]
Ichangi, R.M. and Kulkarni, S.B. 2025. A Discretized Recurrent Deep Learning Classifier based on Stochastic Gradient ChatGPT to Improve Lead Conversion Rate. Engineering, Technology & Applied Science Research. 15, 3 (Jun. 2025), 22712–22717. DOI:https://doi.org/10.48084/etasr.9549.

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