Experiments and Impact
What was the measured effect of our work?
Example finding
A baseline window and a current window
The experiment stores both windows and the rule, then reports the change with caveats.
Quick answer: Experiments and Impact
Experiments and Impact stores a baseline, the change, a wait and a re-run with an explicit decision rule, and links outcomes to business impact where connected. It is coming in V3.
Coming in V3. Arrives with the third release. This page describes the intended capability so you can see where the product is going. It is not available today.
What you see
Before and after experiments with explicit windows and decision rules, and impact tied to outcomes, with the causal caveats stated.
Experiments
Baseline, change, wait and re-run, stored with the decision rule.
Outcome windows
Explicit time windows for the comparison.
Business impact
Actions linked to referrals, conversions and revenue where connected.
Learning
Which kinds of action moved which kinds of question.
How it works
- 1
Baseline
Capture the state before the change.
- 2
Compare
Compare baseline and current over stated windows.
- 3
Learn
Feed measured outcomes back into recommendations, not hidden reasoning.
How it is measured
Definitions come first, so numbers stay comparable from one audit to the next.
- Baseline and current
- The two windows compared.
- Decision rule
- The rule that decides the result, stored in advance.
- Outcome
- Measured change with sample size and caveats.
Good to know. Coming in V3, building on V2 GA4 and Search Console data. Impact is shown with evidence and uncertainty, never a claim of direct causality without support.
Common questions
Is this A/B testing?
It is a before and after comparison over explicit windows with a stored decision rule, with causality caveats. It is not a randomised trial.
Does the agent learn from my data?
Recommendations can reference measured outcomes of past actions. We do not claim opaque retraining.
Is it a randomised test?
No. It is a before and after comparison with stated caveats.
A short checklist
- Define the windows
- Store the decision rule
- Re-run and compare
- Record the outcome
- Feed lessons forward
Who uses it
- Growth lead
- What worked, with caveats.
- Analyst
- Stored windows and rules.
- Executive sponsor
- Cumulative impact.
Common mistakes
Changing the rule afterwards
Store it in advance.
Overstating impact
Report the range and sample.
Only logging wins
Null results save effort.
See Experiments and Impact on your own brand.
Start with a first audit. We show where you appear, who is named instead, and what to fix first.