Composite Revenue Forecasting via Segmented Distributed Models
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Solution Overview
Problem
Accurately predicting revenue is challenging for businesses due to rapidly changing economic conditions, supply chain disruptions, and complex product sales processes, which require consideration of diverse business segments and sales behaviors across territories.
Innovation Solution
A data science-based solution that utilizes conventional and distributed models to analyze historical revenue data, sales pipeline metrics, and deal characteristics, generating composite forecast data through a virtualized system that combines conventional and distributed prediction methods to minimize errors and provide actionable insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional single-model forecasting methods are used, then the system is simple and easy to operate, but the forecast accuracy is insufficient due to inability to handle diverse business segments and sales behaviors
Solution Approach 1:
The patent divides the forecasting system into multiple specialized models, each trained on specific subsets of data from different business segments, territories, or product categories. This segmentation allows each model to specialize in capturing the unique sales behaviors and patterns of its target segment, thereby improving overall forecast accuracy while maintaining manageable system complexity through modular architecture
Solution Approach 2:
The patent applies local quality by customizing forecasting models for specific local contexts such as particular geographic territories, business segments, or customer segments. Each local model is trained on region-specific data and adjusted to reflect local market conditions, sales cycles, and customer behaviors, enabling more accurate forecasts for each local area while the system as a whole integrates these specialized local insights
2Measurement precision
If multiple models are used to analyze diverse data, then forecast accuracy improves, but the difficulty of detecting and measuring increases
Solution Approach 1:
The patent introduces an intermediary layer in the form of a centralized forecasting platform or orchestrator that manages multiple models and consolidates their outputs. This intermediary component simplifies the detection and measurement process by providing a unified interface for data input, model selection, and forecast aggregation, thereby reducing the operational complexity of working with multiple specialized models
Solution Approach 2:
The patent employs parameter changes by dynamically adjusting model parameters, weights, and configurations based on the specific data subsets being analyzed and the particular business segment or territory being forecasted. This allows the system to optimize each model's performance for its specific context while maintaining overall system coherence through standardized parameter management frameworks
3Adaptability or versatility
If distributed models are used for different business segments, then the system becomes more adaptable to diverse conditions, but the device complexity increases
Solution Approach 1:
The patent segments the forecasting system into distributed models, each dedicated to specific business segments, territories, or product categories. This segmentation enables high adaptability to diverse conditions by allowing each model to be independently trained and optimized for its specific domain, while the distributed architecture facilitates scalability and flexibility in accommodating new segments without requiring system-wide redesign
Solution Approach 2:
The patent implements universality through a multi-functional forecasting platform that can accommodate and manage multiple distributed models serving different business segments. The platform provides universal capabilities for data ingestion, model training, forecast generation, and result aggregation across all segments, thereby achieving high adaptability while managing architecture complexity through standardized universal interfaces and protocols
Data Source
AI summary
A method for generating composite prediction data, the method that includes obtaining, by a computing device, conventional prediction data based on historical revenue data, generating first distributed prediction data, using a first distributed model, based on first sales pipeline data, and obtaining a composite prediction data by aggregating the conventional prediction data and the first distributed prediction data.