Abstract
Advertisers may experience difficulty in defining effective targeting and configuration settings for digital promotional campaigns, which can result in performance that does not meet certain goals for scale and return on investment (ROI). A system can utilize an artificial intelligence agent to analyze historical campaign data. The system may correlate past campaign configuration changes, such as adjustments to bids, budgets, and targeting criteria, with corresponding performance metrics including, for example, impressions, redemptions, and cost-per-action. Based on this correlation analysis, the system can formulate tailored recommendations for campaign settings to help meet specific advertiser goals. This process can contribute to improved campaign performance by generating suggested parameters for new or existing campaigns, potentially reducing the need for manual analysis and adjustment.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Aggarwal, Ankit, "A System for Recommending Advertising Campaign Settings Based on Performance Data Analysis", Technical Disclosure Commons, (August 20, 2025)
https://www.tdcommons.org/dpubs_series/8486