How might experimentation be credited with contributing to a best-in-class measurement approach?

Experimentation is crucial for a best-in-class measurement approach because it allows you to validate and test insights derived from your data. By running controlled experiments (like A/B tests), you can confirm whether a particular change or hypothesis actually leads to the expected outcome, rather than just assuming correlation. This provides empirical evidence to support your strategies and optimize performance. "It can help assign credit across touchpoints" refers to attribution modeling, which is distinct from experimentation's primary role of testing hypotheses. "It can assist in the collection of multiple data points" is a general benefit of any measurement system, not specific to experimentation's unique contribution. "It can be used to drive a macro budget strategy" is a high-level outcome that might be informed by experimentation, but it's not the direct way experimentation contributes to measurement itself.