Experiment prioritization and orchestration
User Yield gives every experiment a reason to run next.
An orchestration agent feeds research, funnel evidence and delivery constraints into a shared testing backlog, carrying clear hypotheses, owners and decisions through every experiment.
The impact
58%
less time preparing the test backlog
Experiment prioritization and orchestration
Case study facts
- Industry
- SaaS
- Team
- Marketing
- Systems connected
- 4
- Time to live
- 2 weeks
- Engagement
- AI implementation
The impact
Results for the team
- less time preparing the test backlog
- 58%
- less time preparing the test backlog
- Evidence aggregation and consistent scoring reduce the time the team spends preparing and ranking the test backlog.
- more experiments reaching launch
- 2.6×
- more experiments reaching launch
- Resolving dependencies and overlapping audiences helps more experiments move from the backlog to launch.
- of tests linked to a decision record
- 100%
- of tests linked to a decision record
- Every test retains its hypothesis, owner and decision record, so the team can trace why it ran and what was decided.
Situation
The backlog grew faster than capacity.
A list of test ideas grows faster than the team's capacity to run them. The loudest suggestion can displace a stronger opportunity, and concurrent tests can interfere with one another.
Challenge
The loudest idea was not always the next test.
Ideas needed a consistent basis for comparison, with effort, evidence and learning value made visible. Dependencies and overlapping audiences also had to be resolved before a promising proposal could become a valid experiment.
What we built
An evidence-led experiment workflow.
The orchestration agent turns evidence into structured proposals and scores them against agreed criteria: potential value, confidence in the problem, implementation effort and learning value.
It checks dependencies and audience collisions before proposing a sequence. Each experiment retains its hypothesis, guardrails, owner and decision record through completion.
- 01
Aggregate evidence
Bring together research, funnel findings and prior experiment learning.
- 02
Write the hypothesis
Define the expected mechanism and the outcome to measure.
- 03
Score the proposal
The agent applies agreed criteria with every assumption visible.
- 04
Resolve dependencies
Flag engineering needs, audience collisions and instrumentation gaps.
- 05
Sequence the roadmap
- Human in the loop
Propose a schedule with owners and approval checkpoints.
- 06
Close the loop
Record each decision and feed what was learned back into the backlog.
Evals and guardrails
Check priorities. Approve test schedules.
Scoring evals
Compare proposal scores with expert-reviewed examples using agreed criteria. Test missing evidence, inflated impact and inconsistent scores.
Planning evals
Test whether proposed schedules catch dependencies and overlapping audiences, respect team capacity and follow agreed rules for stopping tests.
Guardrails
Check required proposal fields, dependencies and traffic limits. Require owner approval before scheduling tests or changing when they stop.
Results
Every test keeps its reason and result.
Evidence aggregation and consistent scoring reduce the time the team spends preparing and ranking the test backlog. Resolving dependencies and overlapping audiences helps more experiments move from the backlog to launch.
Every test retains its hypothesis, owner and decision record, so the team can trace why it ran and what was decided.
- less time preparing the test backlog
- 58%less time preparing the test backlog
- more experiments reaching launch
- 2.6×more experiments reaching launch
- of tests linked to a decision record
- 100%of tests linked to a decision record
From User Yield
“We needed our optimization tooling in-house rather than rented. Now we build and test client funnels on our own system, and the testing keeps running between projects.”
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