Real-Time Ad Selection via Gaussian Bayesian Network
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Solution Overview
Problem
Current methods for analyzing the impact of advertising campaigns on sales are limited, as they do not accurately represent long-term effects, fail to account for competitor campaigns, and cannot modify ad campaigns in real-time based on analysis.
Innovation Solution
A system using machine learning algorithms to analyze marketing and advertisement data, identifying persistent media with long-term impact through Gaussian Bayesian networks and predicting sales values to optimize advertising strategies, including real-time rendering of advertisements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If consumer surveys are conducted to analyze advertising impact, then consumer feedback is gathered, but the dataset does not represent accurate sales data and only projects short term impacts
Solution Approach 1:
The patent replaces the mechanical survey-based data collection system with an automated web scraping system that extracts sales data directly from e-commerce platforms. This substitution enables accurate, real-time capture of both short-term and long-term advertising impacts without relying on consumer recall or manual surveys.
Solution Approach 2:
The patent introduces an intermediary automated data extraction system that acts as a bridge between advertising campaigns and sales data analysis. This intermediary continuously monitors and captures sales data from multiple sources, enabling comprehensive long-term impact analysis while maintaining measurement accuracy.
2Loss of information
If manual consumer survey data collection is used, then consumer feedback is obtained, but the process is tedious and time-consuming
Solution Approach 1:
The patent implements a self-service automated data extraction system that independently collects, processes, and analyzes advertising impact data without human intervention. The system automatically scrapes sales data from e-commerce platforms, processes it through machine learning models, and generates insights, eliminating the tedious manual survey collection process while maintaining data completeness.
Solution Approach 2:
The patent replaces manual survey administration and data processing with automated web scraping and machine learning systems. This mechanical substitution enables continuous, comprehensive data collection across multiple platforms without the time constraints and human effort required for manual consumer surveys.
3Measurement precision
If traditional analysis methods are used, then short term advertising impact is measured, but long term impact and competitor campaign impact cannot be analyzed
Solution Approach 1:
The patent implements a universal analysis platform that performs multiple functions: measuring short-term advertising impact, analyzing long-term campaign effectiveness, and evaluating competitor campaign impacts. The machine learning model is designed to handle diverse data types and analysis requirements within a single system, enabling comprehensive multi-dimensional advertising impact assessment.
Solution Approach 2:
The patent introduces an intermediary data extraction and processing system that collects data from multiple sources including own advertising campaigns and competitor campaigns. This intermediary layer enables the analysis system to access and process diverse data types, expanding the scope of impact analysis to include long-term effects and competitor activities.
4Productivity
If real-time ad campaign modification is implemented, then advertising strategies can be optimized, but system complexity increases
Solution Approach 1:
The patent implements a feedback-driven automated system where machine learning models continuously analyze advertising impact data and generate real-time recommendations for campaign optimization. The system processes sales data, identifies effective advertising strategies, and provides actionable feedback to advertisers, enabling rapid strategy adjustment without requiring complex manual analysis processes.
Solution Approach 2:
The patent employs self-service machine learning models that automatically analyze advertising data, identify patterns, and generate optimization recommendations without human intervention. This automation reduces system complexity by replacing complex manual analysis and decision-making processes with autonomous algorithms that continuously optimize advertising strategies in real-time.
Data Source
AI summary
A system selects a set of advertisement media associated with a product. A set of persistent advertisement media from the set of advertisement media is identified based on one or more tests. A Gaussian Bayesian (GB) network based on the set of advertisement media. A net persistence rate for each of the set of persistent advertisement media is determined based on the GB network. a competitor factor associated with another vendor of the product is computed based on one or more marketing parameters associated with the another vendor. A predicted sales (PS) value associated with each of the set of persistent advertisement media is determined. A persistent advertisement medium is selected in real time based on corresponding PS value. An advertisement is rendered on the selected persistent advertisement medium in real time for marketing the product.