Aggregated Cost Per Action Prediction Using Decay Calculators
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Solution Overview
Problem
In the context of content serving platforms, predicting the cost per action for content items is challenging due to sparse conversion events, delays between content item viewing and conversion, frequent campaign changes, and other factors, making it difficult for entities to optimize their bidding strategies effectively.
Innovation Solution
A cost per action aggregator system that utilizes multiple decay calculators with different strategies, half-lives, and decay intervals to generate weighted cost per action predictions, while also employing anomaly detection to adjust data relevance, thereby optimizing bidding for higher conversion probabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional single-model cost per action prediction is used, then the system is simple to operate, but the prediction accuracy is insufficient due to sparse conversion events and delays
Solution Approach 1:
The prediction system is segmented into multiple independent decay calculators, each handling different decay strategies (exponential, linear, logarithmic) and time intervals. This segmentation allows the system to process sparse conversion data through multiple specialized models rather than a single general model, improving prediction accuracy while maintaining manageable complexity through modular architecture
Solution Approach 2:
Multiple cost per action predictions from different decay calculators are merged through a weighted aggregation function. The aggregation combines predictions from various decay strategies and time intervals, producing a final aggregated cost per action prediction that leverages the strengths of each individual calculator to overcome data sparsity and timing delays
2Measurement precision
If multiple decay calculators with different strategies are used, then the prediction accuracy improves, but the computational resources and system complexity increase
Solution Approach 1:
The system implements partial action by allowing entities to select only the decay calculators and strategies relevant to their specific needs. Not all decay calculators must be executed for every prediction request, enabling selective computation that reduces resource consumption while maintaining accuracy where it matters most
Solution Approach 2:
The system dynamically adjusts parameters such as decay intervals, half-lives, and weighting factors based on the specific content item, campaign characteristics, and available data quality. This parameter optimization allows the system to achieve high prediction accuracy with minimal computational overhead by tuning the model to match the data characteristics
3Quantity of substance
If historical cost and conversion data is used for prediction, then more data is available for analysis, but the data becomes stale and less relevant over time due to campaign changes and settings updates
Solution Approach 1:
The decay calculators apply time-decay functions in advance to historical data, progressively reducing the weight of older conversions before they are used in predictions. This preliminary decay action ensures that historical data is automatically adjusted for relevance, with more recent conversions having greater impact on cost per action predictions while still incorporating historical patterns
Solution Approach 2:
The system dynamically adjusts the decay rate and data window based on campaign characteristics, conversion types, and observed data patterns. This dynamic adaptation allows the system to optimize the balance between utilizing sufficient historical data and maintaining relevance, adjusting the effective data horizon based on the specific context of each prediction
Data Source
AI summary
One or more computing devices, systems, and/or methods for aggregated cost per action prediction are provided. Cost and conversion count data, comprising costs for a set of content items to be displayed to users and a count of conversions corresponding to actions performed by users in response to being provided with the set of content items, is tracked. The cost and conversion count data is inputted into a set of decay calculators that utilize different decay strategies. Cost per action predictions by the set of decay calculators for a content item are weighted to create an aggregated cost per action prediction, wherein the weights are based upon whether decay calculators correctly or incorrectly predicted cost per actions for the set of content items.