Adaptive Lead Scoring Model Refresh for Real-Time Data Shifts
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Solution Overview
Problem
Conventional computer technologies for data insights generation are often inaccurate and inefficient, particularly in customer acquisition and relationship management, leading to suboptimal lead generation and advertising strategies.
Innovation Solution
An adaptive real-time modeling and scoring system (ARTEMIS) that includes a trigger component to determine when to execute an automated modeling engine, an automated modeling engine with sub-components for data preparation, model generation, and translation, and a look-alike audience creator for personalized advertising, using self-learning algorithms to adapt to changing data trends and generate accurate lead scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional computer technology is used for data insights generation, then the system is simple and easy to implement, but the accuracy and efficiency of lead scoring and customer acquisition are insufficient
Solution Approach 1:
The system divides the lead scoring process into distinct modular components: data collection module, data preparation module, model generation module, and model execution module. Each module performs a specific function and can be independently configured and maintained, allowing high accuracy through specialized processing while managing complexity through modular architecture.
Solution Approach 2:
The system implements dynamic model refreshing where scoring models are automatically updated based on changing data patterns and performance metrics. The trigger component monitors data changes and automatically initiates model regeneration when improvement opportunities are detected, enabling the system to adapt to evolving customer behavior without manual intervention.
2Measurement precision
If real-time data processing is implemented to improve lead scoring accuracy, then the scoring precision improves, but the computational time and processing speed increase
Solution Approach 1:
The system performs data preparation and feature engineering in advance before model training is needed. Data is collected, cleaned, transformed, and validated beforehand so that when model generation is triggered, the processing time is minimized because the foundational data work is already complete and ready for rapid model iteration.
Solution Approach 2:
The system implements periodic model refreshing rather than continuous retraining. The trigger component monitors data changes and activates model regeneration only when significant patterns emerge or performance degradation is detected, balancing accuracy improvements with computational efficiency by avoiding unnecessary processing cycles.
3Productivity
If automated modeling engine is deployed to improve productivity, then the lead generation efficiency increases, but the system complexity and automation extent increase
Solution Approach 1:
The system implements self-service automation where the modeling engine automatically performs data collection, preparation, model generation, validation, and deployment without requiring manual intervention. The trigger component autonomously monitors performance metrics and data changes, initiating model refreshes when improvement opportunities are detected, thereby increasing productivity while managing complexity through autonomous operation.
Solution Approach 2:
The system incorporates feedback loops where model performance is continuously monitored and fed back to the trigger component. This feedback mechanism enables the system to automatically adjust and improve lead scoring accuracy over time by detecting performance degradation or data pattern changes and triggering appropriate model regeneration, maintaining high productivity through adaptive automation.
Data Source
AI summary
Systems, methods and media for adaptive real time modeling and scoring are provided. In one example, a system for automatically generating predictive scoring models comprises a trigger component to determine, based on a threshold or trigger, such as a detection of new significant relationships, whether a predictive scoring model is ready for a refresh or regeneration. An automated modeling sufficiency checker receives and transforms user-selectable system input data. The user-selectable system input data may comprise at least one of email, display or social media traffic. An adaptive modeling engine operably connected to the trigger component and modeling sufficiency checker is configured to monitor and identify a change in the input data and, based on an identified change in the input data, automatically refresh or regenerate the scoring model for calculating new lead scores. A refreshed or regenerated predictive scoring model is output.