Comparison of Random Forest and XGBoost for Lead Conversion Prediction to Improve Sales Forecasting Accuracy in CRM Systems

Muhammad Adreo Novriansyah; Ahmad Syazili

doi:10.70356/jafotik.v4i2.132

Authors

Muhammad Adreo Novriansyah Program Studi Teknik Informatika, Universitas Bina Darma
Ahmad Syazili Program Studi Teknik Informatika, Universitas Bina Darma

DOI:

https://doi.org/10.70356/jafotik.v4i2.132

Keywords:

lead scoring, random forest, xgboost, machine learning, crm

Abstract

This study aims to compare the performance of Random Forest and XGBoost algorithms for customer lead scoring classification within a Customer Relationship Management (CRM) system for a CCTV business in Palembang City. The dataset consisted of 500 customer records collected from 2023 to 2025 and classified into three lead-scoring categories: Hot, Warm, and Cold. The research process involved data preprocessing, an 80:20 training–testing split, model development, and performance evaluation using Accuracy, Precision, Recall, and F1-Score. The results showed that Random Forest achieved an Accuracy of 88.00%, Precision of 89.31%, Recall of 88.00%, and F1-Score of 87.68%. In comparison, XGBoost achieved superior performance, with an Accuracy of 90.00%, Precision of 90.48%, Recall of 90.00%, and F1-Score of 89.89%. These results indicate that XGBoost outperformed Random Forest in classifying customer lead scores and was therefore identified as the best-performing model for the dataset. In addition, product prediction analysis indicated that CCTV Outdoor had the highest predicted sales potential for 2026, with 112 predicted occurrences (22.4%). Overall, the findings demonstrate that machine learning can support customer prioritization, product potential analysis, and data-driven sales strategies within CRM systems.

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