Abstract: Systems and processes providing a tool for visualizing parallel dependency graph evaluation in computer animation are provided. Runtime evaluation data of a parallel dependency graph may be collected, including the start time and stop time for each node in the graph. The visualization tool may process the data to generate performance visualizations as well as other analysis features. Performance visualizations may illustrate the level of concurrency over time during parallel dependency graph evaluation. Performance visualizations may be generated by graphing node blocks according to node start time and stop time as well as the level of concurrency at a given time to illustrate parallelism. Performance visualizations may enable character technical directors, character riggers, programmers, and other users to evaluate how well parallelism is expressed in parallel dependency graphs in computer animation.

Abstract: Systems and processes providing a tool for visualizing parallel dependency graph evaluation in computer animation are provided. Runtime evaluation data of a parallel dependency graph may be collected, including the start time and stop time for each node in the graph. The visualization tool may process the data to generate performance visualizations as well as other analysis features. Performance visualizations may illustrate the level of concurrency over time during parallel dependency graph evaluation. Performance visualizations may be generated by graphing node blocks according to node start time and stop time as well as the level of concurrency at a given time to illustrate parallelism. Performance visualizations may enable character technical directors, character riggers, programmers, and other users to evaluate how well parallelism is expressed in parallel dependency graphs in computer animation.

Abstract: Systems and processes providing a tool for visualizing parallel dependency graph evaluation in computer animation are provided. Runtime evaluation data of a parallel dependency graph may be collected, including the start time and stop time for each node in the graph. The visualization tool may process the data to generate performance visualizations as well as other analysis features. Performance visualizations may illustrate the level of concurrency over time during parallel dependency graph evaluation. Performance visualizations may be generated by graphing node blocks according to node start time and stop time as well as the level of concurrency at a given time to illustrate parallelism. Performance visualizations may enable character technical directors, character riggers, programmers, and other users to evaluate how well parallelism is expressed in parallel dependency graphs in computer animation.

Abstract: A system and method for lead prioritization based on results from multiple modeling methods are disclosed.

Abstract: A system and method for using marketing automation activity data for lead prioritization and marketing campaign optimization are disclosed. A particular embodiment uses marketing activity data to predict whether or not the lead will be qualified by sales (lead conversion) and whether the lead will result in a successful sale. In order to reduce the feature dimensionality while maintaining key information about activity types and marketing campaigns, we perform topic modeling to represent activities as a mixture over topics. We then use random forest classification to predict the probability of lead conversion and successful sale. In addition, we map the topic importances assigned by the classifier, to a “Mean Topic Importance” (MTI) score. We confirm that the relative MTI scores of different activities are intuitive. These MTI scores can be used to give marketing teams information about which marketing campaigns and assets are more important for a lead prioritization model.

Abstract: A system and method for full funnel modeling for sales lead prioritization are disclosed. A particular embodiment includes two models, DQM (direct qualification model) and FFM (full funnel model), which can be used to rank sales leads based on probability of conversion to a sales opportunity, probability of successful sale, or expected revenue. These models can replace traditional, manually created lead scoring systems, which use hand-tuned scores and are therefore error-prone and non-probabilistic. The disclosed methods achieve high AUC (Area Under Curve) scores in our experiments, and we show that they can result in a substantial increase in conversion rate, a substantial increase in successful sale rate, as well as dramatic increases in total revenue. Unlike traditional lead-scoring, our methods provide an intuitive probabilistic score, and focus more on features that measure customer fit than customer behavior, meaning quality leads can be found earlier on in the sales process.

Abstract: Systems and processes providing a tool for visualizing parallel dependency graph evaluation in computer animation are provided. Runtime evaluation data of a parallel dependency graph may be collected, including the start time and stop time for each node in the graph. The visualization tool may process the data to generate performance visualizations as well as other analysis features. Performance visualizations may illustrate the level of concurrency over time during parallel dependency graph evaluation. Performance visualizations may be generated by graphing node blocks according to node start time and stop time as well as the level of concurrency at a given time to illustrate parallelism. Performance visualizations may enable character technical directors, character riggers, programmers, and other users to evaluate how well parallelism is expressed in parallel dependency graphs in computer animation.