# AI search attribution planner Human Guide

## What This Is For
Designs measurement for AI mentions, delayed brand visits, assisted conversions, and business outcomes beyond direct referral clicks. It gives the agent a clearer input/output frame for AI search attribution planner: what context to ask for, what decisions to make, and what usable artifact to return.

Use this as a human-readable version of the AI search attribution planner agent skill. It is meant for marketers, operators, founders, and other non-coders who want the workflow without reading agent-specific implementation instructions.

## When To Use This
- Use this when you need a repeatable process for AI search attribution planner.
- Use this when the task needs judgment, examples, constraints, or a clear output format rather than a one-off prompt.
- Use this when you want to hand an AI assistant enough context to produce a usable marketing artifact.

## When Not To Use This
- Do not use this when you only need a quick factual answer.
- Do not use this when the work depends on private data you cannot share with the assistant.
- Do not use this as a replacement for legal, compliance, financial, or medical review.

## What You Need Before Starting
- The goal or business outcome you want.
- The audience, customer segment, or market context.
- Any source material the assistant should respect, such as notes, briefs, examples, URLs, or brand guidance.
- Constraints such as tone, length, channel, deadline, region, or approval requirements.
- A clear definition of what a good final answer should look like.

## Step-By-Step Workflow
1. State the job clearly: "Use the AI search attribution planner guide to help me with..."
2. Add context: audience, goal, offer, channel, source material, and constraints.
3. Ask the assistant to identify missing inputs before producing the final output.
4. Have the assistant follow the skill-specific guidance below.
5. Review the result against the final checklist and ask for revisions where needed.

## Skill-Specific Guidance
- Profound joined more than two million privacy-safe AI conversations with downstream browsing behavior.
- After an AI assistant introduced a brand, users visited its site above their forecast baseline during the following seven days.
- Only 42% of downstream visits occurred within 24 hours; a same-session window misses much of the behavior.
- More than 97% of measured downstream visits lacked a trackable AI-referral parameter, even after ChatGPT made links more clickable.
- **Exposure:** brand mention, recommendation, prominence, sentiment, and citation.
- **Immediate behavior:** direct referral, click, landing page, and engaged session.
- **Delayed behavior:** branded search, direct visit, return visit, and cross-session conversion.
- **Business outcome:** signup, lead, qualified pipeline, purchase, revenue, retention, or support deflection.
- Define the decision the measurement must support.
- Specify exposure by engine, prompt cohort, market, language, and date.
- Audit current referral tagging and analytics classification.
- Preserve known AI referrals, landing pages, query parameters, and referrers.

## Decision Points And Nuance
The original skill emphasizes: Research basis, Goal, Measurement layers, Workflow, Output.

Use these questions to steer the work:
- What is the intended audience or buyer?
- What source material must be preserved?
- What should the assistant optimize for: clarity, persuasion, accuracy, speed, creativity, or conversion?
- What examples represent the desired quality bar?
- What should the assistant avoid?

## Common Mistakes
- The study was observational, US-only, and measured site visits rather than purchases. Do not present its aggregate lift as a causal forecast for an individual brand.
- Define the decision the measurement must support.

## Copy-And-Paste Prompt
```text
Use the AI search attribution planner human guide.

My goal:
[Describe the business outcome]

Audience:
[Describe who this is for]

Context and source material:
[Paste notes, examples, links, or existing copy]

Constraints:
[Tone, length, channel, timeline, must-include items, must-avoid items]

Before producing the final output, ask me for any missing information that would materially improve the result.
```

## Final Checklist
- [ ] The output matches the original goal.
- [ ] The audience and context are reflected in the answer.
- [ ] Important constraints and source material were preserved.
- [ ] The assistant made the relevant decisions explicit.
- [ ] The final artifact is ready to use, review, or hand to the next person.

## Source
This guide was generated from the Profound skill entry for `ai-search-attribution-planner`.

## Source Skill Notes
These notes preserve the nuance from the original skill. Use them as supporting reference when the workflow above feels too generic.

# AI Search Attribution Planner

## Research basis

This skill operationalizes Profound's research, [The AI mention effect](https://www.tryprofound.com/blog/the-ai-mention-effect), published July 1, 2026.

Key takeaways from the study:

- Profound joined more than two million privacy-safe AI conversations with downstream browsing behavior.
- After an AI assistant introduced a brand, users visited its site above their forecast baseline during the following seven days.
- Only 42% of downstream visits occurred within 24 hours; a same-session window misses much of the behavior.
- More than 97% of measured downstream visits lacked a trackable AI-referral parameter, even after ChatGPT made links more clickable.

The study was observational, US-only, and measured site visits rather than purchases. Do not present its aggregate lift as a causal forecast for an individual brand.

## Goal

Measure AI search as an influence channel using direct, assisted, delayed, and self-reported evidence while keeping causal claims proportional to the design.

## Measurement layers

- **Exposure:** brand mention, recommendation, prominence, sentiment, and citation.
- **Immediate behavior:** direct referral, click, landing page, and engaged session.
- **Delayed behavior:** branded search, direct visit, return visit, and cross-session conversion.
- **Business outcome:** signup, lead, qualified pipeline, purchase, revenue, retention, or support deflection.

Keep these layers separate. A visibility increase is not automatically a revenue increase.

## Workflow

1. Define the decision the measurement must support.
2. Specify exposure by engine, prompt cohort, market, language, and date.
3. Audit current referral tagging and analytics classification.
4. Preserve known AI referrals, landing pages, query parameters, and referrers.
5. Add assisted indicators:
   - branded search movement
   - direct and organic visits to exposed pages
   - one-hour, 24-hour, and seven-day windows
   - self-reported attribution
   - CRM notes and sales-call evidence
6. Choose an evaluation design:
   - pre/post with stable cohort
   - matched market or page comparison
   - holdout when feasible
   - interrupted time series
   - exposure panel or incrementality study
7. Identify confounders such as campaigns, seasonality, launches, press, and platform changes.
8. Connect metrics to business outcomes without double counting.
9. Document privacy, consent, retention, and minimum aggregation requirements.
10. Report uncertainty and alternative explanations.

## Output

Return:

1. Measurement objective and causal confidence level.
2. Funnel from AI exposure to business outcome.
3. Event and property specification.
4. Attribution-window recommendation.
5. Dashboard metrics split into exposure, immediate, delayed, and business layers.
6. Experiment or quasi-experiment design.
7. Confounder and privacy checklist.
8. Reporting language that distinguishes observation, association, and causation.