Predict the VAS (Viewability Attention Score) a specific creative would achieve at a given moment, based on historical data and causal modeling.
Uses the CausalPredictionService which:
1. Embeds the moment description to find historically similar moments
2. If >= 5 similar moments exist with the same creative, uses weighted-average prediction
3. If insufficient data, falls back to Gemini generative prediction
4. Always decomposes the prediction into causal factors
WHEN TO USE:
- Evaluating whether a creative will perform well in a specific context
- A/B testing creative placement hypotheses before committing budget
- Understanding which causal factors drive VAS for a creative
- Comparing expected performance across different moment types
RETURNS:
- prediction: { predictedVAS (0-1), confidence (0-1), method ('historical'|'model'), sampleSize }
- causal_factors: { audienceMatch, contextMatch, attentionState, socialPotential } (each 0-1)
- metadata: { creative_id, moment_description }
- suggested_next_queries: Follow-up queries
EXAMPLE:
User: "How would a coffee ad perform at a transit station during morning rush?"
predict_moment_quality({
moment_description: "transit venue, morning commute, 12 viewers, high attention, mostly 25-34 age range",
creative_id: "coffee-brand-morning-30s"
})