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Experimentation & Analytics for Recommendations

Experimentation and analytics for recommendations use the same testing and attribution discipline as search to measure and optimize recommendation performance.

Overview​

Apply the same rigorous testing and analytics approaches used for search to recommendations, ensuring recommendations are continuously optimized. The Marqo pixel automatically collects recommendation interaction data (impressions, clicks, conversions), so all analytics and testing metrics are available automatically.

Testing Recommendations​

A/B test recommendation algorithms:

{
  "test_type": "recommendation_algorithm",
  "variants": [
    {
      "name": "collaborative_filtering",
      "algorithm": "collaborative"
    },
    {
      "name": "content_based",
      "algorithm": "content_based"
    }
  ],
  "metrics": [
    "click_through_rate",
    "add_to_cart_rate",
    "conversion_rate",
    "revenue_per_impression"
  ]
}

Recommendation Analytics​

Track recommendation performance:

{
  "analytics": {
    "metrics": [
      "impressions",
      "clicks",
      "add_to_cart",
      "purchases",
      "revenue"
    ],
    "attribution": "last_touch"
  }
}

Best Practices​

  1. Test continuously: Regularly test recommendation improvements
  2. Measure incrementality: Focus on incremental impact
  3. Track cannibalization: Understand trade-offs
  4. Segment analysis: Analyze performance by segment
  5. Iterate: Continuously optimize based on results