Abstract
Systems and methods are disclosed for consumer-centric marketplace intelligence in a digital advertising platform. Event data and social graph data are used to maintain a continuously updated consumer state vector per user including RFM scoring, lifecycle stage, predicted 13-month lifetime value tier, purchase propensity, and shopping rhythm. A per-user, per-category lifecycle model maintains probabilistic stage membership across stages including Unaware, Discovery, Research, Intent, Active, Champion, and Lapsed, and may represent cross-category migration sequences. A global product catalog knowledge graph normalizes products across multiple advertisers and stores cross-merchant relationships including complements, substitutes, and upgrade paths. An auction service computes auction scores for candidate ads using predicted 13-month lifetime value, an LTV multiplier, and a quality score, and selects ads for delivery based on the auction scores.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Anonymous, "Consumer-Oriented Marketplace Intelligence and Auction Optimization Systems for Digital Advertising Platforms", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/10721