Abstract: Systems, methods, and devices generate predictive data segments using machine learning and natural language processing. The method receives training data with digital identifiers and binary classification labels indicating target outcomes. It accesses external data sources containing textual attribute descriptions, which are preprocessed by concatenating multiple descriptions per identifier for large language model input. The model outputs high-dimensional vector embeddings encoding semantic relationships. A neural network trains using these embeddings and classification labels to predict probability scores. For new data, the method preprocesses textual descriptions, generates embeddings via the language model, and applies the neural network to produce probability scores representing conversion propensity values. It selects digital identifiers with scores exceeding a configurable threshold and transmits them to a digital platform for prioritized resource allocation.

Abstract: A transmission platform can modify transmissions of outputs based on the impact of the outputs on various segments of users. The transmission platform can initiate a transmission of outputs to a non-control group of users and receive results generated by both the non-control group of users and a control group of users who did not receive the output. The transmission platform can determine segments of users based on certain characteristics and can compare the results, within each segment, from the control and non-control groups. The transmission platform can identify certain segments for which the difference in results is statistically significant, indicating that the output caused a positive impact for those segments. The transmission platform can modify the transmission to target these segments with future outputs and can prioritize those segments demonstrating the greatest impact (e.g., the greatest difference between the control and non-control groups).