Abstract: Certain aspects of the present disclosure provide techniques for training and using a machine learning model to extract relevant textual content for custom fields in a software application from freeform text samples. An example method generally includes generating, via a natural language processing pipeline, a training data set from a data set of freeform text samples and field entries for a plurality of custom fields defined in a software application. A first machine learning model is trained to identify custom fields for which relevant data is included in freeform text. A second machine learning model is trained to extract content from the freeform text into one or more custom fields of the plurality of custom fields defined in the software application and identified by the first machine learning model as custom fields for which relevant data is included in the freeform text.

Abstract: Certain aspects of the present disclosure provide techniques for training and using a machine learning model to extract relevant textual content for custom fields in a software application from freeform text samples. An example method generally includes generating, via a natural language processing pipeline, a training data set from a data set of freeform text samples and field entries for a plurality of custom fields defined in a software application. A first machine learning model is trained to identify custom fields for which relevant data is included in freeform text. A second machine learning model is trained to extract content from the freeform text into one or more custom fields of the plurality of custom fields defined in the software application and identified by the first machine learning model as custom fields for which relevant data is included in the freeform text.

Abstract: A method predicts custom fields from text. Transaction text is normalized from transaction data to generate normalized text. A field prediction and a type prediction are selected using prediction data and the normalized text. The prediction data is generated using a machine learning model trained to identify field predictions from free form text. The field prediction and the type prediction are presented to a client device. In response to user input from the client device, the transaction data is updated with the field prediction.

Abstract: Systems and methods for real-time churn prediction using a flexible and robust machine learning system to predict user churn propensities in real-time using user activity data. In addition to churn propensity prediction, systems and methods may provide insights based on user activity data intelligence.

Abstract: A method predicts custom fields from text. Transaction text is normalized from transaction data to generate normalized text. A field prediction and a type prediction are selected using prediction data and the normalized text. The prediction data is generated using a machine learning model trained to identify field predictions from free form text. The field prediction and the type prediction are presented to a client device. In response to user input from the client device, the transaction data is updated with the field prediction.