Advertising Prediction Model Using Segmented Components
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Solution Overview
Problem
Companies and advertisers face challenges in predicting and optimizing advertising-related data, such as impressions, clicks, and conversions, due to uncertainties in user traffic and competition on web pages, leading to inefficiencies in allocating advertising space and budget.
Innovation Solution
A system and method that uses a mathematical model incorporating control-signal-related and control-signal-independent components, along with an error component, to predict advertising-related data, allowing for adaptive updates and predictions of advertisement performance, enabling informed allocation of advertising space based on historical data and control signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If companies allocate advertising space without accurate prediction models, then advertising space can be placed quickly, but prediction accuracy of impressions, clicks, and conversions is poor
Solution Approach 1:
The patent segments the advertising prediction problem into three distinct mathematical components: a control-signal-related component that captures the impact of advertising decisions, a control-signal-independent component that accounts for external factors, and an error component that models uncertainty. This segmentation allows each component to be updated and optimized independently, improving prediction accuracy while managing model complexity through modular structure.
Solution Approach 2:
The patent implements dynamic updating of model components based on incoming data and control signals. The mathematical model is not static but continuously adapted as new advertising performance data becomes available, allowing the system to learn from historical patterns and improve predictions over time while maintaining computational efficiency through incremental updates rather than complete recalculations.
2Productivity
If companies use simple advertising allocation methods, then decision-making is fast and simple, but allocation effectiveness and revenue maximization are reduced
Solution Approach 1:
The patent incorporates feedback mechanisms where actual advertising performance data (impressions, clicks, conversions) is fed back into the mathematical model to update its components. This closed-loop feedback system allows the model to continuously refine its predictions and improve allocation effectiveness, while the automated nature of the feedback process minimizes the operational burden on decision-makers.
Solution Approach 2:
The mathematical model performs self-updating of its components based on incoming data without requiring manual intervention. The system automatically adjusts its predictions and recommendations by processing new advertising performance data and control signals, enabling effective allocation decisions while keeping the operational process simple and automated.
3Measurement precision
If companies gather more data about user traffic and competition, then prediction accuracy improves, but data processing complexity and time increase
Solution Approach 1:
By segmenting the data processing into three distinct mathematical components, the patent enables parallel and independent processing of different data types. The control-signal-related component processes advertising decision data, the control-signal-independent component processes external factors data, and the error component processes uncertainty data. This segmentation reduces overall processing time while maintaining comprehensive data analysis for accurate predictions.
Solution Approach 2:
The patent structure allows for preliminary processing and categorization of data into the three mathematical components before final prediction synthesis. By organizing data into predefined categories upfront, the system reduces the computational burden during actual prediction operations, enabling faster processing while maintaining high prediction accuracy through comprehensive data utilization.
Data Source
AI summary
In accordance with the invention, a system, method, and apparatus for analyzing advertisement-related data are presented, which may include receiving data related to an aspect of an advertisement and modeling the aspect of the advertisement with a mathematical model. The mathematical model may include a control-signal-related component, a control-signal-independent component, and an error component. Each component may be updated based on at least one of a control signal, the received data, and a previous state of at least one of the components. An updated model may be created base on the updated components. The system, method, and apparatus may also include predicting the aspect of the advertisement using the updated model. Exemplary aspects of and data related to the advertisement may include one or more of the following: a number of impressions, “clicks,” or “conversions” and/or the impression-to-conversion, impression-to-click, or click-to-conversion ratios.