Conversion Determination Network for Lost Demand Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Electronic platforms face challenges in accurately predicting demand in undersupply scenarios, leading to inefficient manual adjustments that can result in oversupply or undersupply of ordering options, which negatively impacts user experience and revenue.
Innovation Solution
A system utilizing a machine learning architecture, specifically a conversion determination network, to analyze order placement information and generate predicted conversion information, allowing for the calculation of lost demand and enabling dynamic adjustments to ordering options based on real-time data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual adjustment of supply is used to test demand, then demand prediction can be performed, but the process becomes tedious and inefficient
Solution Approach 1:
The system uses machine learning models to automatically predict demand and determine optimal supply levels without requiring manual intervention. The conversion determination network autonomously analyzes historical data, predicts conversion rates, and calculates lost demand, eliminating the need for manual testing and adjustment while maintaining accurate demand prediction.
Solution Approach 2:
The patent replaces manual mechanical adjustment processes with automated machine learning algorithms. The conversion determination network uses computational models to simulate demand scenarios and calculate optimal supply levels, substituting the manual trial-and-error process with automated data-driven decision making.
2Measurement precision
If manual adjustment of supply is performed, then demand testing is possible, but it results in oversupply of ordering options
Solution Approach 1:
The system continuously monitors actual conversion rates and compares them against predicted conversion rates from the machine learning model. This feedback mechanism allows the system to adjust supply levels dynamically, ensuring optimal alignment between supply and actual demand while avoiding both oversupply and undersupply scenarios.
Solution Approach 2:
The patent dynamically adjusts supply parameters based on predicted conversion rates and lost demand calculations. The system changes supply levels from fixed manual adjustments to variable, data-driven parameters that adapt to changing demand patterns, ensuring optimal supply quantities are maintained.
3Ease of operation
If sufficient ordering options are provided to satisfy demand, then user experience improves, but the system complexity increases
Solution Approach 1:
The system performs preliminary demand prediction and supply optimization before actual ordering periods begin. By using historical data to train machine learning models and pre-calculate optimal supply levels, the system prepares appropriate ordering options in advance, ensuring good user experience while managing complexity through automated pre-computation.
Solution Approach 2:
The patent creates virtual copies of real-world ordering scenarios through machine learning simulations. The conversion determination network replicates demand patterns and conversion behaviors in a controlled computational environment, allowing the system to test and optimize supply strategies without affecting actual user interactions, thus managing system complexity.
Data Source
AI summary
Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of: providing, via an electronic platform, access to one or more order placement user interfaces; collecting order placement information associated with the one or more order placement user interfaces; analyzing, by a conversion determination network of a machine learning architecture, the order placement information; generating actual conversion information for client sessions based on the actual availability of the order placement options during the client sessions; generating predicted conversion information for the client sessions based on a full availability of all of the order placement options during the client sessions; and generating lost demand information based, at least in part, on the actual conversion information and the predicted conversion information. Other embodiments are disclosed herein.