ks582/ai-video-ad-simulation-core
A synthetic pretest platform that simulates video ad delivery to AI agents (synthetic viewers) using Chain of Thought (CoT) reasoning to model how target audiences process and respond to ad exposure — enabling advertisers to estimate brand lift metrics before real-world deployment.
SUMMARY AI要約 by gpt-5-mini
A synthetic pretest system that simulates video-ad exposure on AI “synthetic viewers” to estimate causal brand-lift before buying inventory. Users are ad/creative teams, planners, and measurement engineers who want early, low-cost signals on creative performance. Key features: - Multimodal LLM agents initialized from structured personas sampled from public stats (ACS, NTIA). - Twin-world paired design (treatment vs. control) sharing baseline calls to yield causal deltas. - Seven funnel-oriented metrics (awareness, recall, message association, favorability, consideration, purchase intent, search intent) plus Headroom Lift and CIs. - Pipeline: canonical ad schema, persona generation, simulation loop, paired estimation, statistical analysis, root-cause attribution, and deterministic 4‑paragraph synthesis and integration outputs. - Outputs: per-metric lifts, confidence intervals, diagnosis and actionable hypotheses.
使用言語(バイト数比)
オーナー情報
日付
| GitHub作成日 | 2026-05-09 |
| 最終Push | 2026-05-09 |
| 当サイト初検出 | 2026-05-09 |
| 最終取得 | 2026-05-09 16:11 |
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