Add "Impressions" (i.e. _experience.decisioning.propositionEventType.display ) as a Normalizing Metric in the CJA Experimentation Panel

Summary: Allow a standard or custom event-based metric — specifically Target display impressions (_experience.decisioning.propositionEventType = display) — to be selected directly as the Normalizing metric in the Experimentation panel, matching the built-in Impressions option that already exists in the Adobe Analytics Analytics for Target (A4T) panel.

Problem / Use case: The CJA Experimentation panel currently exposes only People, Sessions, and Events as native normalizing-metric options. For many experimentation use cases — particularly server-side and experience-level tests — the correct denominator for conversion rate and lift is impressions (how many times a variant was actually rendered), not people or sessions. People/Sessions can significantly over- or under-count true exposure, which distorts lift and confidence.

The A4T panel in Adobe Analytics already supports Visitors / Visits / Impressions as the normalizing metric (the "counting methodology" that becomes the lift denominator). CJA does not offer the Impressions equivalent natively.

Why it matters — A4T migration parity: Customers migrating from Adobe Analytics + A4T to CJA lose a normalizing option they relied on. Today the only workaround in CJA is to hand-build an Experimentation-compatible calculated metric that counts display proposition events — extra setup, easy to get wrong (several calculated-metric constructs silently disqualify it from the panel), and a step most A4T users won't know to perform. Native Impressions normalization would make the A4T → CJA transition far more seamless and keep historical lift methodology consistent before and after migration.

Proposed solution: Add an Impressions option (sourced from proposition display events / propositionEventType = display) to the Normalizing metric dropdown in the CJA Experimentation panel, so it aligns 1:1 with A4T's Impressions normalizing metric. Or even grant the option to choose a custom normalizing metric.

Impact: Smoother A4T-to-CJA migrations, methodologically correct lift calculations for exposure-based tests, and reduced configuration burden/error risk from the current calculated-metric workaround.