Now that the spread of advertisements is controlled by algorithms and changes happen in real-time, we no longer only need to store and report the data. It can help us understand the audience, create a model, update bidding plans, ensure compliance with regulations, etc. To solve the problem of how to use data infrastructure to improve the efficiency of targeted advertising, a closed-loop system has been built covering first-party data collection, unified identity resolution, timely provision of feature libraries, CTR/CVR prediction, budget bidding, attribution feedback, privacy management, etc. Offline Replay was carried out to compare the difference between the old batch processing and the new real-time feature service pipeline. Therefore, through the addition of identity graphs, feature libraries, streaming updates and feedback loops, the AUC has gradually increased from 0.763 to 0.821, the P95 response latency has dropped from 210ms to 98ms at 16kQPS, and ROAS has risen by 18.7%. Research shows that there are problems with the models of target advertising, and many people think these problems are due to low-quality data, outdated features, inconsistent engineering and privacy-constrained infrastructure.

Jinxin Li. Research on the Path and Efficiency of Empowering Accurate Advertising Delivery with Data Infrastructure. International Journal of Business Management and Economics and Trade (2026), Vol. 7, Issue 1: 178-188. https://doi.org/10.38007/IJBMET.2026.070120.

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