Online Ad Campaign Classification via Web Scanning
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Solution Overview
Problem
Existing online advertising systems face challenges in tracking and classifying online ads across multiple websites, determining ad placement effectiveness, and competitor analysis, as they struggle to accurately capture and categorize ad metrics and campaigns.
Innovation Solution
An online content evaluation system that uses a scanning server to collect and classify online ad metrics, grouping ads into campaigns based on identified beacons and metrics, and providing a user interface for clients to view reports and analyze ad performance and competitor activity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tracking of ad placement across websites is performed, then accuracy of ad metrics collection is improved, but labor cost and time consumption increase significantly
Solution Approach 1:
The patent uses web crawlers to automatically copy and scrape ad metrics data from multiple websites, replacing manual tracking methods. The system replicates the data collection process across numerous sites simultaneously, achieving high accuracy without proportional increases in time investment.
Solution Approach 2:
The patent replaces manual mechanical tracking with automated computer-based systems including web crawlers, databases, and processing algorithms. This substitution eliminates human labor while maintaining or improving measurement accuracy through systematic automated data collection and classification.
2Adaptability or versatility
If comprehensive ad campaign tracking across multiple websites is implemented, then ad performance analysis capability is improved, but system complexity increases
Solution Approach 1:
The patent segments the complex task of ad campaign tracking into distinct functional modules: web crawlers for data collection, databases for storage, classification algorithms for organization, and analysis components for interpretation. This segmentation manages system complexity by breaking down the overall system into manageable, specialized components.
Solution Approach 2:
The patent introduces intermediary components including standardized data formats, classification schemas, and processing protocols that mediate between diverse website sources and the analysis system. These intermediaries simplify integration complexity by providing uniform interfaces despite varied input sources.
3Productivity
If automated web crawling and data collection is performed, then productivity of ad metrics gathering is improved, but risk of missing or inaccurate data increases
Solution Approach 1:
The patent implements feedback mechanisms where collected ad metrics data is validated against expected patterns, completeness checks are performed, and errors are identified and corrected. The system continuously monitors data quality and adjusts crawling parameters based on feedback from data validation processes.
Solution Approach 2:
The patent performs preliminary actions including pre-defining data collection parameters, setting up validation rules before execution, and preparing error handling protocols in advance. This preliminary preparation ensures that automated crawling processes maintain high reliability by anticipating and preventing potential data quality issues.
Data Source
AI summary
A content evaluation system includes a scanning server to scan web sites to determine metrics for online ads. The content evaluation system may include a content evaluation server to classify the online ads into campaign groups based on the metrics, and each group is associated with a different ad campaign.