Event-Driven Service Demand Prediction for Pricing and Capacity
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems fail to efficiently predict high periods of service demand, leading to missed revenue opportunities for service providers due to delayed price adjustments and capacity changes.
Innovation Solution
A computer-implemented method using machine learning to analyze historical data, characterize events, and predict future service demand outliers by correlating metadata tags with extraordinary demand, enabling proactive adjustments in service pricing and capacity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data review and approximation methods are used, then data processing can be performed with simple tools, but the accuracy and timeliness of demand prediction deteriorates
Solution Approach 1:
The patent replaces manual mechanical data review processes with automated machine learning systems. The ML model automatically processes historical service demand data, event data, and metadata to generate predictions, eliminating the need for human operators to manually analyze large datasets and make approximations.
Solution Approach 2:
The system enables self-service through automated data processing where the machine learning model independently analyzes data patterns, identifies correlations between events and demand, and generates predictions without requiring human intervention. The system serves itself by automatically updating models with new data and refining predictions over time.
2Loss of energy
If reactive price adjustment based on observed demand spikes is used, then price changes can be made with simple monitoring, but revenue optimization deteriorates due to delayed response
Solution Approach 1:
The patent implements preliminary action by predicting service demand outliers before they occur. The machine learning model analyzes historical data and event characteristics to forecast future demand spikes, allowing service providers to adjust prices and capacity in advance rather than reacting after demand is already observed.
Solution Approach 2:
The system incorporates feedback mechanisms where prediction results are continuously monitored and fed back into the model. The model learns from actual demand outcomes versus predicted demand, refining its algorithms to improve future predictions and reduce both revenue loss and response time delays.
3Productivity
If automated machine learning prediction systems are implemented, then demand prediction accuracy and response time improve, but system complexity and implementation cost increases
Solution Approach 1:
The patent applies segmentation by dividing the complex prediction system into distinct modular components: data collection modules, preprocessing modules, machine learning model modules, and output generation modules. Each component handles specific tasks independently, making the overall complex system manageable through functional segmentation.
Solution Approach 2:
The system implements universality by designing a multi-functional machine learning platform that can handle various types of service demand predictions across different industries. The same core architecture processes diverse data types (event data, historical demand, metadata) and generates predictions for different service types, reducing overall system complexity through standardized universal components.
Data Source
AI summary
Techniques for predicting an impact of one or more events on service demand are disclosed. Some embodiments include first and second sets of data characterising properties of historic events using metadata tags, and demand for services that are then filtered to distinguish ordinary demand from extra-ordinary demand. Machine learning is used to determine correlations between metadata tags and extra-ordinary demand to produce a third data set operable for predictive determinations of future event impact on service demand.