Category overview
Use cases differ mainly by (a) how measurable the question is, and (b) how much external benchmarking is available.
CPG & retail
Concept tests, packaging direction, pricing hypotheses, channel strategy.
fast iteration
validate key claims
UX / product research
Usability hypotheses, onboarding messaging, feature trade-offs, segmentation exploration.
rapid exploration
don’t replace usability tests
Finance & diligence
Category narratives, competitor moves, demand sensitivities, messaging for stakeholders.
scenario stress tests
benchmark assumptions
Public sector
Policy comprehension, service design hypotheses, communications testing (with safeguards).
strong governance
avoid high-stakes inference
Media & messaging
Claim testing, narrative diagnostics, message comprehension, tone calibration.
rapid A/B hypotheses
sensitivity + bias checks
Services & policy
Customer journey hypotheses, objection handling, service concept iteration.
hypothesis generation
confirm with field signals
Use-case playbook (a simple pattern)
Regardless of domain, use cases become credible when they follow a repeatable pattern.
1) Define the decision
What will change if this is true?
If the decision is high-stakes, plan for benchmarking and potential fieldwork before you start.
2) Choose the method
Concept test, message test, scenario
Prefer standardised protocols and fixed stimuli so results can be repeated and compared.
3) Validate & disclose
Stability + benchmark + limitations
Report variance, run sensitivity checks, and attach a disclosure label.
CPG & retail
High iteration value. Most effective when used to rank options, surface objections, and generate language to test in-market.
Best for
Concept & packaging direction
Compare multiple concepts, identify confusion, refine positioning.
Also useful for
Pricing hypotheses
Explore thresholds and fairness narratives; validate with benchmarks.
Avoid
Hard “% will buy” claims
Treat as directional unless validated against field signals.
Best-practice checklist
- Use fixed “concept cards” and keep wording identical across variants.
- Run two identical runs and report rank stability.
- Segment results (e.g., value seekers vs premium) and verify differences persist across runs.
- Use the output to prioritise what to test with real shoppers.
UX / product research
Useful for hypothesis generation and messaging iteration. Do not treat it as a substitute for real usability testing.
Best for
Onboarding + messaging
Clarity, tone, friction points, alternative explanations.
Also useful for
Feature trade-offs
Surface likely objections, preferences, and edge cases.
Avoid
Usability proof
For interaction behaviour, run actual user tests.
How to use it responsibly
- Use synthetic output to generate test scripts, edge cases, and hypotheses.
- Validate key claims with usability testing or behavioural telemetry.
- Be careful with “persona realism” narratives; require disclosure and stability.
Finance & diligence
Strong for scenario stress tests and narrative diagnostics. Weak for precise forecasts unless heavily benchmarked.
Best for
Scenario simulation
Competitor moves, pricing shocks, channel changes, economic shifts.
Also useful for
Demand sensitivity hypotheses
What assumptions matter most; what to verify with real data.
Avoid
Standalone forecasting
Do not present simulated outcomes as predictive truth.
Governance tip
Require disclosure of assumptions and a simple benchmark: compare the model’s “baseline scenario” against known
historical behaviour before running counterfactuals.
Public sector
Potentially valuable for comprehension testing and service design hypotheses - but requires strong safeguards and transparency.
Best for
Comprehension + messaging clarity
Identify where communications are confusing or misread.
Also useful for
Service design hypotheses
Journey friction points and likely failure modes.
Avoid
Individual-level inference
Do not use to decide outcomes for specific people.
Safeguard requirement
For government communications and services, pair synthetic work with clear disclosure, external validation,
and a plan for real-world measurement before acting on conclusions.
Media & messaging
Useful for rapid message iteration and diagnosing reactions. Requires sensitivity testing to avoid prompt bias.
Best for
Message variants
Comprehension, believability, differentiation, tone.
Also useful for
Objection discovery
Surface likely counterarguments and reputational risks.
Avoid
Micro-targeting misuse
Do not use to justify manipulative or discriminatory targeting.
Minimum test
Run sensitivity checks by changing only one element at a time (headline, claim, tone) and verify that conclusions are robust.
Services & policy
Strong for customer journey hypotheses, objection handling, and service concept iteration.
Best for
Journey friction hypotheses
Where users hesitate, misunderstand, or churn.
Also useful for
Objection handling
What stops adoption; what proof points are needed.
Avoid
Hard incidence claims
Validate adoption and conversion with real signals.
Operational tip
Use synthetic studies to prioritise what to test next, then use analytics, experiments, and targeted interviews to confirm.
Example studies (starter set)
Concrete example studies you can run in a week. Each should be executed with a disclosure label and stability check.
Concept ranking study
Compare 3–5 concepts using a fixed concept card format and a standard questionnaire. Run two identical runs and report rank stability.
Inputs
Concept cards + target segments
Outputs
Rank order + objections + language
Validation
Two-run stability + sensitivity check
Messaging clarity study
Test 3–5 headline/claim variants. Ask comprehension, believability, and differentiation questions.
Change only one element at a time and run sensitivity tests.
Inputs
Message variants + segments
Outputs
Misreadings + trust concerns
Validation
Sensitivity test + stability
Scenario stress test
Define 2–4 scenarios (e.g., price increase, competitor entry, economic downturn) and run consistent prompts.
Report variance and list assumptions explicitly.
Inputs
Scenario definitions + constraints
Outputs
Sensitivity map + key risks
Validation
Baseline benchmark + stability