In this study, we introduce the Dynamic Behavioral Feature Predictor (DBFP), a machine learning model designed to predict feature demands for software products based on consumer behavioral analytics. The model leverages advanced techniques such as Deep Convolutional Neural Networks (1D CNN) and Long Short-Term Memory (LSTM) networks to identify complex user behavior patterns. We demonstrate that DBFP achieves a high accuracy of 99%, outperforming established models such as LSTM and 1D CNN in extensive comparative experiments. Although the model shows promising performance, we note that its accuracy may be sensitive to data quality, and further research is needed to evaluate its robustness in real-world applications. DBFP excels at identifying diverse user behavior types, offering tailored recommendations for software features that align with individual user preferences. This work highlights the potential of behavioral analytics in personalizing software development, enhancing the user experience, and improving the efficiency of feature prioritization. By bridging the fields of machine learning, big data (BD), and software personalization, DBFP lays the foundation for future advancements in user-centric software engineering. This study not only contributes to the field of predictive analytics but also opens new avenues for applying behavioral insights to software design and development.