Viewability Range Optimization via Cost-Conversion Correlation
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Solution Overview
Problem
Marketers face challenges in determining optimal viewability ranges for digital marketing content due to the programmatic nature of bidding, where high demand for viewable locations drives up costs, making it difficult to balance viewability and cost-effectiveness.
Innovation Solution
A system that correlates cost data from a bidding platform with viewability data from a content server on a per-content impression basis, allowing for the computation of cost-per-conversion for different viewability ranges and providing user interfaces to display this information, enabling marketers to identify optimal viewability levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If marketers bid on viewable locations to increase viewability, then viewability increases, but cost increases exponentially
Solution Approach 1:
The system changes the parameter of viewability definition from a single threshold to multiple granular ranges (e.g., 1-10%, 11-20%, etc.). This allows marketers to select optimal viewability levels that balance effectiveness with cost efficiency, avoiding the exponential cost increase associated with bidding on highly viewable locations alone.
2Reliability
If marketers optimize bidding for high viewability, then content is more likely to be seen, but the supply of viewable locations decreases
Solution Approach 1:
The system segments the viewability metric into multiple discrete ranges rather than treating it as a single binary state. This segmentation creates a spectrum of options from low to high viewability, allowing the market to allocate content across different viewability levels, thereby increasing the overall supply of acceptable locations while still providing high viewability options for those willing to pay premium rates.
3Reliability
If marketers increase bidding on viewable locations, then viewability improves, but determining optimal viewability level becomes difficult
Solution Approach 1:
The system implements feedback by providing marketers with cost-per-conversion data across different viewability ranges. This feedback loop enables marketers to observe which viewability levels deliver the best return on investment and adjust their bidding strategies accordingly, simplifying the decision-making process through data-driven insights rather than guesswork.
Data Source
AI summary
Systems and methods provide for determining optimal viewability ranges for content impressions. During content impressions, a content server receives content requests via an ad tag that is enhanced with a content impression identifier or cost data from a bidding platform. The content server serves a pixel tag with content in order to collect viewability data for each content impression. The content server stores the viewability data with the content impression identifier or cost data from the bidding platform for each content impression. This allows for the correlation of cost data and viewability data on a per-content impression basis. The cost data and viewability data are used to compute cost-per-conversion information for each of a number of different viewability ranges.