Creative Performance Prediction Before Launch

import { Mixpeek } from 'mixpeek';

const client = new Mixpeek({ apiKey: process.env.MIXPEEK_API_KEY });

// Ingest historical creatives with performance data
await client.buckets.connect({
  collection_id: 'creative-performance',
  bucket_uri: 's3://creatives/historical/',
  extractors: [
    'image-embedding',       // Visual features
    'text-extraction',       // Copy analysis
    'color-analysis',        // Color palette
    'composition-analysis',  // Layout features
    'object-detection'       // Elements present
  ],
  settings: {
    performance_fields: ['ctr', 'cvr', 'engagement_rate', 'view_rate'],
    segment_by: ['platform', 'audience', 'objective']
  }
});

// Predict performance for new creatives
async function predictCreativePerformance(creativeUrl: string, context: {
  platform: string;
  audience: string;
  objective: string;
}) {
  const analysis = await client.extract({
    url: creativeUrl,
    extractors: ['image-embedding', 'text-extraction', 'composition-analysis']
  });

  const prediction = await client.predict({
    collection_id: 'creative-performance',
    query: {
      type: 'embedding',
      embedding: analysis.embedding
    },
    context: context,
    return_similar: 5  // Find similar past creatives
  });

  return {
    predicted_ctr: prediction.ctr,
    predicted_engagement: prediction.engagement_rate,
    confidence: prediction.confidence,
    similar_performers: prediction.similar,
    optimization_suggestions: prediction.suggestions
  };
}

// Batch score creative variations
async function prioritizeCreatives(creatives: string[], context: object) {
  const predictions = await Promise.all(
    creatives.map(c => predictCreativePerformance(c, context))
  );

  return predictions
    .map((p, i) => ({ creative: creatives[i], ...p }))
    .sort((a, b) => b.predicted_ctr - a.predicted_ctr)
    .map((c, i) => ({ ...c, priority: i + 1 }));
}

// Update model with live performance data
async function recordPerformance(creativeId: string, metrics: {
  impressions: number;
  clicks: number;
  conversions: number;
}) {
  await client.collections.update({
    collection_id: 'creative-performance',
    document_id: creativeId,
    data: {
      actual_ctr: metrics.clicks / metrics.impressions,
      actual_cvr: metrics.conversions / metrics.clicks,
      recorded_at: new Date().toISOString()
    }
  });

  // Trigger model refinement if needed
  await client.models.retrain({
    collection_id: 'creative-performance',
    trigger: 'performance_update'
  });
}