Tracking and Improving Share of Voice in AI Answers

TL;DR: AI answers are the new shelf space. Track how often LLMs recommend your brand vs. competitors across prompts, models, and geographies—then improve the sources those answers cite.

What "Share of Voice" Means in the LLM Era

Traditional SOV measures how visible your brand is across ads, search, or social. In LLM-generated answers, SOV reflects how frequently and prominently your brand appears when buyers ask AI for recommendations (e.g., “best SOC2 vendor for startups”).

With AgentMindShare, you can quantify this across:

Prompts (high‑intent questions buyers actually ask)
Models (ChatGPT, Claude, Gemini, Perplexity, etc.)
Markets (US, UK, AU, etc.)
Positions (first mention, list inclusion, excluded)

A Practical Measurement Framework

Use a weighted scoring model so the metric reflects real buyer impact.

1) Prompt Coverage (PC)
PC = (# prompts where brand appears) / (total prompts tracked)

2) Model Weighting (MW)
Assign weights by your audience usage (example below).
Weighted Presence = Σ(appearance_in_model × model_weight)

3) Position Bonus (PB)
Reward earlier mentions: first = +1.0, second = +0.5, list-only = +0.25.

4) Geo Weighting (GW)
Focus on revenue markets: US 0.4, UK 0.3, EU 0.2, AU 0.1 (example).

Composite LLM SOV
LLM_SOV = (PC × Σ(MW × PB × GW)) ÷ Normalizer

Tip: Start simple (coverage by model) and layer weights once you have baseline trending.

Example Weights (customize to your ICP)

Model	Weight
ChatGPT	0.40
Claude	0.25
Gemini	0.20
Perplexity	0.15

Instrumentation: What to Track Weekly

Visibility matrix: prompts × models with ✅/❌ and position (1st, 2nd, list)
Share of voice trend: 4–12 week time series by market
Citations driving answers: top domains and their changes
Competitor deltas: who replaced you, where, and when
Answer quality: sentiment/accuracy notes (optional)

How to Improve LLM SOV (Step‑by‑Step)

Map money prompts
Collect 15–50 high‑intent prompts (by segment and geo). Prioritize those closest to purchase.
Scan & baseline
Run multi‑LLM scans; record where you appear vs. competitors.
Diagnose citations
For each missed prompt, list the exact sources the LLM cites (G2 pages, docs, case studies, comparison posts, directories).
Create an influence plan
- Update / create pages that answer the prompt explicitly
- Acquire/refresh profiles on cited review sites
- Pitch or contribute to the specific blogs/directories being cited
- Add geo‑specific proof (local case studies, pricing, compliance)
Execute inside your AI tools (MCP)
Use AgentMindShare’s MCP support to pull scans and generate outreach briefs directly in Claude Code, then push tasks to Jira/Asana without tab‑switching.
Monitor & alert
Set alerts for drops in key prompts or geos; re‑scan after each content/PR change to confirm impact.

GEO Considerations (US, UK, AU and beyond)

Regional proof points: local customers, regulations, integrations
Language/terminology: match the phrasing buyers use in each market
Regional directories & press: prioritize sources LLMs already cite in that country

Benchmarks & Targets

Weeks 1–2: Baseline coverage across top 25 prompts and 4 models
Weeks 3–6: +25–50% coverage on missed prompts by fixing the top cited sources
Quarter 1: Be present in ≥70% of money prompts across your priority models

Common Pitfalls (and Fixes)

Chasing volume over intent → Track buying prompts, not generic queries
Optimizing without citations → Always work backwards from the sources the LLM used
One‑and‑done → Answers shift weekly; schedule scans and alerts

FAQ

How often should we scan?
Weekly for core prompts; daily for mission‑critical categories during launches.

Can we work entirely in Claude Code?
Yes. Via MCP, you can fetch scans, generate action plans, and create tickets from within Claude Code.

Can our data team analyze everything?
Yes. Use BigQuery export to join LLM visibility with web analytics, CRM, or MMM models.

CTA: Track and Grow Your AI Share of Voice

Start a scan for your top prompts, see where you’re missing, and get the exact sources to influence. Then execute directly via MCP and analyze trends via BigQuery.