Leveraging LSTM Networks and Random Forest Algorithms for Enhanced Predictive AI in Customer Journey Mapping

Abstract

This research paper investigates the integration of Long Short-Term Memory (LSTM) networks and Random Forest algorithms to enhance predictive capabilities in customer journey mapping. The study addresses the limitations of traditional methods by proposing a hybrid approach that combines the strengths of LSTMs, known for their proficiency in handling sequential data and capturing temporal dependencies, with the robustness and interpretability of Random Forests, which excel in feature selection and managing non-linear relationships. The methodology involves preprocessing customer interaction data, followed by feature extraction using LSTM networks to learn sequential patterns. Subsequently, the Random Forest algorithm utilizes these features to perform classification and regression tasks, providing interpretable insights into customer behaviors and potential decision pathways. Experiments conducted on several customer journey datasets demonstrate the hybrid model's superior performance compared to standalone models, showing an increase in predictive accuracy and a reduction in error rates. Moreover, the model's ability to offer actionable insights for marketers and customer experience designers is assessed, highlighting its applicability in real-world scenarios. The paper also explores the implications of this approach for improving customer satisfaction and retention, suggesting pathways for future research into advanced AI-driven customer analysis tools.