Beyond Winning: Spotify’s Experiments with Learning Framework
Sep 1, 2025Michael Bellato (Senior Data Scientist), Mårten Schultzberg (Staff Data Scientist), and Sebastian Ankargren (Senior Data Scientist)

Summary
This post introduces the Experiments with Learning (EwL) metric as a new way to measure the success of experiments within Spotify's Confidence platform. It defines EwL as experiments that produce valid and decision-ready results, categorizing outcomes into 'Ship', 'Abort due to regression', and 'Powered, no recommendation'. The post details the importance of valid experiments (no sample ratio mismatches, proper metric measurement) and sufficiently powered experiments across all relevant metrics. It also discusses the reasons for experiments not yielding learning, such as failed health checks or insufficient power. The learning rate is highlighted as approximately 64%, emphasizing the value of learning from negative or neutral results.
More from the Experimentation Platform trail
- Why Spotify Is Not Using Bayesian A/B Testing | Spotify Engineering
- When Can LLMs Replace Humans in A/B Tests? | Spotify Engineering
- Better Experiments with LLM Evals — A funnel, not a fork | Spotify Engineering
- Why We Use Separate Tech Stacks for Personalization and Experimentation
- Fixed-Power Designs: It’s Not IF You Peek, It’s WHAT You Peek at