Feature: Default Experiment Metric Auto‑Apply

Automatically apply a predefined default metric to every new experiment created in Optimizely, ensuring that all tests are consistently configured with a core measurement from the outset.

Today, metrics must be manually added during experiment setup, which introduces variability and increases the risk of inconsistent or incomplete measurement across teams. This feature removes that dependency by embedding a standard metric directly into the experiment creation flow.

1. Improves experiment quality and consistency
   By ensuring every experiment starts with a core metric, teams align on how success is measured. This directly addresses known challenges around inconsistent metric selection and unclear measurement standards across teams.

2. Reduces setup effort and friction
   Removes a repetitive manual step in experiment creation, making setup faster and more scalable, particularly valuable as experiment volume grows.

3. Increases decision confidence
   A consistent baseline metric ensures all experiments produce comparable outputs, improving trust in results and enabling clearer, faster decision-making. This aligns with the goal of making outputs easier to compare and act on across the programme.

4. Prevents measurement gaps and errors
   Eliminates the risk of experiments being launched without a meaningful primary or guardrail metric, one of the common causes of weak or inconclusive results.

5. Enables stronger governance at scale
   Supports standardised experimentation practices by embedding metric frameworks directly into tooling, rather than relying on process or training alone.