Research and Measurement Standards Measure the decision—not just the prompt.
Traditional search often centered on the keyword. Early AI-search measurement often centers on the prompt.
Hendricks centers on the commercial intent context.
Intent Context What a unit of measurement is made of.
- Customer Need
- Customer Profile
- Use Case
- Constraints
- Geography
- Decision Stage
- Commercial Value
= Intent Context
Context Panels Each panel answers a different question.
- 01
Neutral baseline
A defined question without substantial supplied customer context.
Question answeredWhat happens under standardized conditions?
- 02
Customer cohort
Representative industry, demographic, use-case, budget, geographic, or business constraints.
Question answeredWhich customer profiles change the outcome?
- 03
Decision journey
Multi-step conversations that become progressively more specific as the customer approaches a decision.
Question answeredDoes the brand survive as the decision narrows?
- 04
Platform and time panel
Repeated observations across relevant search environments and time periods.
Question answeredHow stable is the observed outcome?
Optional
First-party human research
With consent, real participants may compare controlled findings with live user experiences.
Outcome Classification Classify each brand outcome as one or more of:
Define classifier rules and human-review thresholds.
- Absent
- Referenced
- Cited
- Considered
- Compared
- Recommended
- Preferred
- Inaccurately represented
- Contradicted
- Uncertain
Weighting High-value customer decisions receive more weight than low-value informational questions.
A transparent weighting model can consider:
- Demand
- Commercial intent
- Expected customer value
- Strategic fit
- Eligibility
- Evidence confidence
Evidence Grades Every conclusion carries the grade of evidence behind it.
| Grade | Evidence |
|---|---|
| A | Controlled experiment combined with first-party CRM or revenue data |
| B | Strong first-party exposure, behavior, and commercial time-series evidence |
| C | Repeated controlled context-panel observations and consistent source patterns |
| D | Directional API, synthetic, or isolated observation |
Methodology statement
Hendricks does not claim to reverse-engineer hidden model logic. We observe the information environment, test representative customer contexts, analyze sources and evidence, engineer the conditions a brand controls, and measure what changes.
Reproducibility Requirements What is stored for each run.
Store for each run where legally and technically permitted:
- Exact question
- Supplied context
- Platform
- Model or search experience
- Date and time
- Location
- Session type
- Response
- Cited sources
- Classifier output
- Confidence
- Human-review status
Limitations What this methodology cannot do.
- Personal memory cannot be reproduced universally.
- Model and search behavior changes over time.
- APIs may not reproduce consumer interfaces exactly.
- Not every AI impression is observable.
- Citation does not prove influence.
- Correlation does not prove causation.
- Offline selection may not be attributable.
Keep Reading Where to go next.
Sources and updates
These are the Hendricks research and measurement standards. They describe the firm’s own practice and are revised as platform behavior changes. Last reviewed .
This definition is applied in Selection Intelligence, Search Impact Measurement and the Diagnostic.