Aggregated Media Value Scoring for Secure Ad Performance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing digital advertising systems lack a standard, accurate, and data-secure performance measurement scale that allows online publishers to understand their media value, influence, and interaction with website visitors, and provide insights into performance improvements and comparisons across market groups without violating data security.
Innovation Solution
A computer-implemented system that generates performance analysis by identifying conversion rate, average order value, and incremental lift rate scores, and aggregates them into a media value score, which can be displayed or compared with third-party data after excluding outliers and considering seasonal factors, while ensuring data security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a comprehensive performance measurement system is implemented to provide accurate media value assessment, then measurement precision is improved, but data security risks increase
Solution Approach 1:
The system segments performance measurement into multiple independent score components (conversion rate score, average order value score, incremental lift rate score) that are calculated separately and then aggregated. This segmentation allows each component to be measured and validated independently, improving overall measurement precision while maintaining data security through controlled data access at each segment level.
Solution Approach 2:
The system introduces an intermediary aggregation layer that processes raw performance data through standardized scoring functions before presenting results. This intermediary layer (the score aggregation mechanism) acts as a mediator between raw data and final measurements, ensuring data security protocols are applied at each transformation stage while maintaining measurement accuracy.
2Measurement precision
If multiple performance metrics are aggregated into a comprehensive score, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system employs a universal score aggregation framework that can handle multiple different performance metrics (conversion rate, average order value, incremental lift rate) through a common scoring mechanism. This multi-functional approach improves measurement precision by considering multiple factors while managing complexity through a standardized processing template that can be applied consistently across different metric types.
Solution Approach 2:
The system transforms various performance parameters into standardized score values through predefined scoring functions. By changing the parameter representation from raw diverse metrics to normalized scores, the system achieves comprehensive measurement precision while reducing the complexity of directly processing heterogeneous data types.
3Measurement precision
If detailed client data is collected for accurate performance analysis, then measurement precision is improved, but data security concerns increase
Solution Approach 1:
The system extracts only the essential performance components needed for measurement (conversion rate, average order value, incremental lift rate) from the broader client data set. By taking out and processing only these specific elements through standardized scoring functions, the system achieves accurate performance measurement while minimizing data security exposure by not collecting or storing unnecessary detailed information.
4Ease of operation
If performance data is made accessible for market group comparisons, then ease of operation is improved, but data security risks increase
Solution Approach 1:
The system creates aggregated score representations that copy the essential performance characteristics without exposing the underlying detailed client data. By providing access to these derived score copies rather than the original detailed data, the system improves ease of operation for market group comparisons while maintaining data security by preventing direct access to sensitive source information.
Data Source
AI summary
A computer implemented system for generation of performance analysis of digital advertising carried out on a global communications network that may be accessible by consumers is provided. The system may include a processor and one or more of a client database. The client database may include client data stored thereon. The client data may include a conversion rate, an average order value, and/or an incremental lift rate. The processor may be configured to identify a conversion rate score, identify an average order value score, and identify an incremental lift rate score. The processor may further be configured to generate an aggregated media value score that may be based on the conversion rate score, the average order value score, and the incremental lift rate score. The processor may also be configured to broadcast the aggregated media value score to a receiver.