A Deep Learning Framework for Predictive Feature Prioritization in Early-Stage Software Startups: Integrating Historical Delivery Data and Market Signals

Feature prioritization in early-stage software startups is a critical yet poorly structured challenge, as prevailing frameworks rely predominantly on expert intuition and fail to exploit patterns latent in historical delivery data and labor market dynamics. This study proposes a deep learning framework that explores labor-market signals as a reproducible proxy for market-driven feature prioritization. The framework encodes two complementary information sources: internal sprint delivery history processed by a Bidirectional Long Short-Term Memory network with attention, and external market signals from LinkedIn job postings processed by a Convolutional Neural Network encoder; the resulting representations are fused via a cross-modal layer to classify job postings as proxies for High or Low feature-priority market demand. The model is evaluated on the publicly accessible LinkedIn Job Postings dataset (2023–2024, approximately 124,000 records) and achieves an Area Under the Receiver Operating Characteristic Curve of 0.961 on the proxy classification task, outperforming classical baselines including gradient Boosted Trees, Random Forest, Support Vector Machine, and Logistic Regression. SHapley Additive exPlanations analysis identifies industry sector and geographic location as the two most influential market-signal predictors. These results suggest that jointly encoding internal delivery dynamics and external market signals offers a promising, scalable decision-support tool to assist startup product teams in data-driven roadmap prioritization, subject to further validation against direct expert priority labels.