Enhancing Lead Conversion Prediction Using a Hybrid Data Analysis and Sine-Cosine Optimized Neural Network

Authors

  • Paril Ghori  Lecturer, Department of Computer Engineering, IRT polytechnic college, Chennai, India

Keywords:

Lead Conversion, Predictive Analytics, Sine-Cosine Optimization, Neural Networks, Sentiment Analysis, VADER, TF-IDF, Machine Learning.

Abstract

Lead conversion is a critical metric in the marketing industry, directly impacting efficiency and profitability. Advancements in data mining and machine learning have introduced innovative approaches to enhance predictive analytics for lead conversion. This paper presents a hybrid methodology that combines structured and unstructured data analysis to predict the probability of lead conversion. Unstructured data is processed using Vader sentiment analysis and TF-IDF vectorization for feature extraction, while structured data is utilized for binary classification. The extracted features are integrated into a Sine-Cosine Optimized neural network classifier to improve classification accuracy and performance. The proposed methodology achieves superior predictive accuracy, enabling marketers to identify high-potential leads effectively. Experimental results demonstrate the practical utility of this approach, empowering businesses to make data-driven decisions, enhance customer engagement strategies, and optimize marketing efforts.

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2018-06-30

Issue

Volume 4, Issue 9, July-August-2018

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Paril Ghori "Enhancing Lead Conversion Prediction Using a Hybrid Data Analysis and Sine-Cosine Optimized Neural Network" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 4, Issue 9, pp.698-709 , July-August-2018.