Synthetic buyer journey simulation agents
User Yield finds journey questions before a live experiment begins.
The team spends less time on initial journey review and starts live research with testable hypotheses. Simulated buyer agents work through different task perspectives, surfacing questions for real-user research and testing.
The impact
5×
more journey scenarios reviewed
Synthetic buyer journey simulation agents
Case study facts
- Industry
- SaaS
- Team
- Marketing
- Systems connected
- 2
- Time to live
- 2 weeks
- Engagement
- AI implementation
The impact
Results for the team
- more journey scenarios reviewed
- 5×
- more journey scenarios reviewed
- Simulated buyer tasks let the team review more journey scenarios before deciding what to test with real users.
- less time on the initial journey review
- 71%
- less time on the initial journey review
- Repeatable browser runs reduce the time spent on the first review of a journey's pages and interactions.
- testable hypotheses per review cycle
- 36
- testable hypotheses per review cycle
- Each review cycle produces journey hypotheses for validation through user research or live experiments.
Situation
The team knew its own journey too well.
Teams can become too familiar with their own flow to notice unclear language or missing information. Live research time is valuable, so arriving with better questions matters.
Challenge
Assumptions needed a structured challenge.
Simulated journeys needed clear tasks and grounded context. Findings had to distinguish directly observed interface issues from an agent’s imagined reaction, so a simulation could guide research without being presented as buyer evidence.
What we built
Repeatable simulations of buyer tasks.
We defined bounded buyer tasks and evidence-based context, then had agents attempt the journey while recording observations. Different runs test how the flow supports distinct information needs and constraints.
Reports separate an observed interface problem from a simulated reaction; synthetic behavior generates hypotheses, not measured customer sentiment or conversion evidence.
AI tech stack
Azure FoundryAI platform
BrowserbaseBrowser execution
- Define buyer tasks
Specify what each simulated journey is trying to accomplish.
- Ground the context
Build on existing research rather than invented personas.
- Exercise the flow
Agents work through the relevant pages, controls and decision points.
- Record observations
Capture the interface evidence behind every difficulty.
- Compare runs
Surface recurring questions and differences across task contexts.
- Validate with people
Prioritize hypotheses for real-user research or live experiments.
Evals and guardrails
Verify observations. Keep simulation in scope.
Browser-task evals
Repeat fixed buyer tasks and check completion against recorded browser actions. Include blocked steps and actions the interface cannot support.
Evidence evals
Check reported problems against captured screens and actions. Detect invented page details and separate observed failures from simulated reactions.
Guardrails
Limit browser actions and step counts to the test scope. Label simulated findings and prevent their use as real customer quotes or results.
Results
Live research starts with sharper questions.
Simulated buyer tasks let the team review more journey scenarios before deciding what to test with real users. Repeatable browser runs reduce the time spent on the first review of a journey's pages and interactions.
Each review cycle produces journey hypotheses for validation through user research or live experiments.
- more journey scenarios reviewed
- 5×more journey scenarios reviewed
- less time on the initial journey review
- 71%less time on the initial journey review
- testable hypotheses per review cycle
- 36testable hypotheses per review cycle
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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