Attribution Modeling Using Withheld Content Item Impressions
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Solution Overview
Problem
Attribution modeling faces challenges in accurately estimating the causal impact of content items, such as advertisements, due to the difficulty in selecting relevant activity stream data, which affects the accuracy of attributing conversions to marketing events.
Innovation Solution
The method involves identifying a competing content item that participated in a content auction, either withheld from presentation or nearly selected, to generate alternative paths of user interactions, allowing for the comparison of conversion metrics and determination of attribution credit for the original content item impression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional attribution modeling methods are used to estimate causal impact of content items, then the process is simpler to implement, but the accuracy of attribution results deteriorates due to estimation bias
Solution Approach 1:
The patent creates counterfactual paths by copying actual user interaction paths and modifying them to represent alternative scenarios where competing content items were shown instead of the original content item. This allows estimation of what would have happened without the original content impression, enabling more accurate causal impact measurement while maintaining manageable complexity through systematic path replication and modification.
2Measurement precision
If activity stream data is collected to improve attribution accuracy, then measurement precision improves, but the difficulty of detecting and measuring relevant data increases
Solution Approach 1:
The patent extracts and isolates specific data elements needed for counterfactual path generation, such as competing content item impressions and user interaction sequences. By focusing only on the essential data components required for attribution modeling rather than processing all available activity stream data, the system improves measurement precision while reducing the complexity of data detection and measurement.
3Measurement precision
If counterfactual paths are generated by substituting competing content items, then attribution accuracy improves, but the computational resources and time required increase
Solution Approach 1:
The patent performs preliminary actions by pre-identifying competing content items and pre-structuring user interaction paths before generating counterfactual scenarios. By preparing the foundational data elements and path structures in advance, the system reduces the computational burden during actual attribution calculations, thereby improving causal impact estimation accuracy while minimizing additional processing time and resource requirements.
Data Source
AI summary
Systems, methods, and computer-readable storage media for attribution modeling using withheld or near impression data are provided. One method involves determining, for a first content item impression, withheld or near impressions for a competing content item within a content auction. The method further involves identifying a first set of paths including a sequence of events that includes an interaction with the first content item impression. The method further involves identifying a second set of paths, each including the sequence of events with the competing content item impression replacing the first content item impression. The method compares conversion metrics for the first and second paths to determine attribution credit for the first content item impression.