How does experimentation contribute to a best-in-class measurement approach?

Experiments are crucial for a best-in-class measurement approach because they allow you to validate hypotheses and test the impact of changes before full-scale implementation. By running controlled experiments, you can isolate variables and confidently determine whether a specific change (e.g., a new bidding strategy, ad copy, or landing page) genuinely leads to improved performance metrics. This data-driven validation helps refine strategies and ensures that decisions are based on proven results, not just assumptions. While experiments can inform budget allocation, they don't directly drive a macro budget strategy, which involves broader financial planning. They also don't primarily assign credit across touchpoints (that's attribution modeling) or simply assist in collecting multiple data points (data collection is a prerequisite for experiments, not their primary contribution).