Leveraging Random Forest Algorithm for Enhanced Lead Conversion and Customer Retention

This paper presents the development, implementation, and evaluation of a Random Forest-based Social Lead Scoring Model to address key business challenges in lead generation, customer retention, and optimization of lead management processes. The main goal is to create a strong, data-driven tool to precisely estimate lead conversion probabilities to guide better marketing and sales strategy decision-making. The model uses social metrics and past lead data to estimate conversion probabilities for every lead. The Tkinter library created a user-friendly interface that allows straightforward usage for non-technical business professionals. This model includes two fundamental functions calculate_probability and predict_conversion– which offer practical and pragmatic insights. During the development phase, a thorough cross-valuation was conducted by the model trained on a large dataset, including several lead characteristics, to decrease overfitting and improve the model's predictive performance. Thus, the model scored 89.46%, which is higher than that of conventional lead-scoring techniques. However, there is still room for development, specifically in enhancing its predictive power and reducing overfitting risks on different datasets, although this model has great accuracy. The results highlight the need for data-driven strategies in raising conversion rates and show the possibilities of machine learning in lead management optimization. Including extra data sources and investigating advanced technologies such as deep learning should be conducted by future studies to improve model performance further. The increased accuracy in predictive analytics will give companies a competitive edge in their operations.