Shadow Ad Campaign Simulator for Performance Prediction
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Solution Overview
Problem
Advertisers face challenges in assessing the performance of proposed online advertising campaigns without actual implementation, as they need to gauge potential impressions, clicks, and conversions, and identify suitable ad placement and search results, which is time-consuming and costly.
Innovation Solution
A system and method that utilize shadow advertising campaigns to simulate and evaluate proposed campaigns by analyzing campaign parameters, generating ad rankings, and selecting ads based on criteria such as performance factors, allowing real-time or near real-time assessment of advertising strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If advertisers run actual advertising campaigns to evaluate performance, then accurate performance data is obtained, but time and cost increase significantly
Solution Approach 1:
The system performs preliminary simulations of advertising campaigns before actual deployment. By pre-running shadow campaigns that replicate actual campaign mechanics, advertisers can evaluate potential performance metrics (impressions, clicks, conversions) without committing to full-scale execution, thus reducing evaluation time while maintaining measurement accuracy
Solution Approach 2:
The invention creates shadow advertising campaigns that are copies of actual campaigns but run in parallel without consuming real ad inventory. These shadow campaigns replicate the same targeting, bidding, and creative elements, allowing performance prediction through simulated data that mirrors actual campaign outcomes without the time and cost overhead of running multiple real campaigns
2Loss of information
If advertisers conduct comprehensive campaign evaluations, then detailed performance insights are obtained, but human costs and complexity increase
Solution Approach 1:
The shadow advertising system performs self-service evaluation by automatically generating performance predictions, comparing them against actual campaign results, and identifying optimization opportunities without requiring extensive human analysis. The system autonomously processes campaign data, runs simulations, and produces actionable insights, reducing human labor while maintaining comprehensive performance evaluation
Solution Approach 2:
The system implements feedback loops where shadow campaign results are continuously compared with actual campaign performance, and the insights feed back into campaign optimization. This automated feedback mechanism provides complete performance insights by iteratively refining predictions and recommendations, reducing the need for manual analysis while maintaining evaluation comprehensiveness
3Adaptability or versatility
If advertisers test multiple ad placements and strategies, then optimization opportunities are identified, but time for testing increases
Solution Approach 1:
The system performs preliminary testing of multiple ad placements and strategies through shadow campaigns before actual deployment. By pre-simulating various placement scenarios (in-feed ads, story ads, search ads) and measuring their projected performance, advertisers can identify optimal strategies without requiring extended real-world testing periods, thus maintaining adaptability while reducing testing time
Data Source
AI summary
A shadow ad can be evaluated by receiving an ad request, identifying at least one shadow ad and at least one actual ad based on the received ad request, generating an ad ranking by analyzing one or more criteria associated with the identified at least one shadow ad and the at least one actual ad, and selecting one or more of the identified at least one shadow ad and the at least one actual ad based on the ad ranking. Further, the at least one shadow ad can be associated with one or more campaign parameters corresponding to a shadow ad campaign. Additionally, the ad request can be received from an online advertising system.