Iterative Predictive Modeling for Lead Scoring with Sparse Data
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Solution Overview
Problem
Businesses face challenges in effectively scoring leads due to limited data availability, which hinders the building of valid predictive models for lead conversion and action propensity, and existing methods lack an iterative feedback mechanism for optimizing lead scoring processes.
Innovation Solution
A multi-step iterative predictive modeling technique involving variable selection, feature set selection, training data selection, model development, model validation, and process optimization, using a feedback-driven mechanism that includes random sampling and True Positive Rate (TPR) and True Negative Rate (TNR) optimization to refine lead scoring models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional lead scoring methods are used with limited data, then model development can proceed, but model accuracy and reliability deteriorate
Solution Approach 1:
The patent implements a feedback-driven mechanism where model predictions are continuously compared with actual outcomes, and the results feed back into the model training process. This iterative feedback loop allows the model to improve its accuracy over time even when starting with limited data, as each cycle refines the predictions based on real performance data.
Solution Approach 2:
The patent performs preliminary data sampling and model training with available data before full-scale deployment. By conducting initial model development and validation with the limited data that exists, the system establishes a baseline model that can then be progressively improved as more data becomes available through feedback loops.
2Measurement precision
If comprehensive data collection is performed to improve model accuracy, then prediction precision improves, but processing time and complexity increase
Solution Approach 1:
The patent employs sampling techniques where only a representative subset of data is used for model training and validation rather than processing the entire dataset. This partial action approach maintains prediction precision by using carefully selected samples while significantly reducing processing time and computational resources required.
Solution Approach 2:
The patent segments the lead scoring process into distinct phases: initial model training with sampled data, validation phase, and deployment phase. Each segment uses appropriate data subsets and processing intensity, allowing the system to achieve high precision without requiring all data to be processed simultaneously, thus reducing overall processing time.
3Reliability
If manual model optimization is performed to maximize TPR and TNR, then model performance improves, but operational complexity increases
Solution Approach 1:
The patent implements automated feedback-driven optimization where the system self-adjusts model parameters based on performance metrics. The model automatically identifies opportunities to improve True Positive Rate and True Negative Rate through iterative training cycles, reducing the need for manual intervention while maintaining high performance.
Solution Approach 2:
The patent systematically varies model parameters and thresholds during the feedback-driven optimization process to find the optimal balance between TPR and TNR. By automated parameter tuning based on performance feedback, the system achieves improved model performance without requiring complex manual optimization procedures.
4Measurement precision
If iterative feedback-driven optimization is implemented, then model accuracy improves, but computational resources and processing steps increase
Solution Approach 1:
The patent implements periodic model retraining and validation cycles where the model is updated at regular intervals based on accumulated feedback data. This periodic action allows the system to progressively improve accuracy over time while managing computational resources by not continuously retraining, but rather at structured intervals when sufficient new data has accumulated.
Data Source
AI summary
Embodiments of the present invention disclose system to determine the best model to perform lead scoring for a given data set. The system can perform a multi-step iterative procedure including variable selection, feature set selection, training data selection, model development, model validation and process optimization. The system also performs local and global optimizations iteratively to determine the best possible model for a given scenario.