# AI citation mix strategist Human Guide

## What This Is For
Analyzes which owned, earned, institutional, competitive, and social sources shape AI answers, benchmarks a brand against its category, and recommends channel investment. It gives the agent a clearer input/output frame for AI citation mix strategist: 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 citation mix strategist 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 citation mix strategist.
- 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 citation mix strategist 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 analyzed 11.84 billion citations across eight models, 29 industries, and 8,061 categories.
- About 57% of citations went to company-operated sites overall, but the mix varied sharply by model and industry.
- In 24 of 29 industries, the median Profound customer received more citations from brand sites than from earned or social sources.
- A universal prescription such as "do more PR" or "post on Reddit" ignores the category's actual citation market.
- competitor or adjacent company site
- institutional or primary authority
- social or user-generated content
- marketplace, directory, or review platform
- Define prompt clusters, engines, models or surfaces, language, geography, and date range.
- Collect cited domains and URLs across a stable sample.
- Normalize domains and classify every source.
- Calculate citation share by source class, engine, and prompt cluster.

## Decision Points And Nuance
The original skill emphasizes: Research basis, Goal, Source taxonomy, Workflow, Decision rules, 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 is an observational benchmark. Do not transfer its percentages to a brand forecast without matching industry, engine, language, geography, and time period.
- Do not recommend manufactured mentions, fake reviews, undisclosed promotion, or irrelevant channel activity.

## Copy-And-Paste Prompt
```text
Use the AI citation mix strategist 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-citation-mix-strategist`.

## 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 Citation Mix Strategist

## Research basis

This skill operationalizes Profound's research, [Where do AI citations come from?](https://www.tryprofound.com/blog/where-do-ai-citations-come-from), published July 30, 2026.

Key takeaways from the study:

- Profound analyzed 11.84 billion citations across eight models, 29 industries, and 8,061 categories.
- About 57% of citations went to company-operated sites overall, but the mix varied sharply by model and industry.
- In 24 of 29 industries, the median Profound customer received more citations from brand sites than from earned or social sources.
- A universal prescription such as "do more PR" or "post on Reddit" ignores the category's actual citation market.

The study is an observational benchmark. Do not transfer its percentages to a brand forecast without matching industry, engine, language, geography, and time period.

## Goal

Determine which source classes influence the target answer market, identify where the brand underperforms, and allocate effort to the smallest credible channel intervention.

## Source taxonomy

Use granular classes for diagnosis:

- owned brand site
- competitor or adjacent company site
- earned editorial media
- institutional or primary authority
- PR wire
- social or user-generated content
- marketplace, directory, or review platform
- other or unknown

For executive reporting, roll these up into brand-operated, earned/institutional, and social/UGC.

## Workflow

1. Define prompt clusters, engines, models or surfaces, language, geography, and date range.
2. Collect cited domains and URLs across a stable sample.
3. Normalize domains and classify every source.
4. Calculate citation share by source class, engine, and prompt cluster.
5. Separate citations to the target brand from citations that merely shape the surrounding answer.
6. Compare the brand's mix with relevant category benchmarks when available.
7. Identify addressable gaps:
   - weak owned evidence
   - missing third-party corroboration
   - absent institutional authority
   - relevant community gap
   - competitor-controlled source pressure
8. Map recurring citation neighbors and the role each source plays.
9. Recommend channel investment based on reach, evidence, feasibility, durability, and risk.
10. Define a longitudinal measurement plan.

Do not recommend manufactured mentions, fake reviews, undisclosed promotion, or irrelevant channel activity.

## Decision rules

- Strengthen owned content when models already rely on company sites and the brand lacks useful facts or evidence.
- Pursue earned coverage when trusted editorial or institutional sources dominate the target cluster.
- Participate in communities only when those communities already contribute materially and participation can be authentic.
- Treat competitor citations as intelligence about missing facts and framing, not as permission to copy.
- Segment every recommendation by engine; aggregate figures can hide opposite platform behavior.

## Output

Return:

1. Citation-market summary.
2. Source-mix breakdown by engine and cluster.
3. Citation-neighbor map with domain, class, role, frequency, and reachability.
4. Gap diagnosis distinguishing owned, earned, institutional, social, and competitive gaps.
5. Prioritized channel plan with expected impact, confidence, effort, owner, and evidence.
6. Measurement design with baseline, stable cohort, cadence, and review date.