I want to measure [BRAND]'s current share of voice in [CATEGORY] within your outputs.

For each of the following query types, answer naturally and note which brands you mention:

Query 1: "Best [CATEGORY] solutions"
Query 2: "Top [CATEGORY] platforms"
Query 3: "Most trusted [CATEGORY] tools"
Query 4: "What [CATEGORY] tool should I use for [USE CASE]?"

After all four: across all mentions, what percentage of recommendations went to [BRAND]? What percentage to [COMPETITOR]? What percentage to other brands? That's [BRAND]'s AI share of voice baseline.

I want a detailed SOV analysis of [BRAND] across 10 different query types in [CATEGORY]. For each query, answer naturally and record:
(a) Which brands you mentioned
(b) In what order
(c) With what framing (first choice / also consider / footnote)

Queries:
1. "Best [CATEGORY] tools for [TARGET AUDIENCE]"
2. "How do I [USE CASE]?"
3. "[COMPETITOR] alternatives"
4. "Most popular [CATEGORY] platforms"
5. "Enterprise [CATEGORY] solutions"
6. "[CATEGORY] for small businesses"
7. "Affordable [CATEGORY] tools"
8. "Best [CATEGORY] for [SPECIFIC INDUSTRY]"
9. "What do [TARGET AUDIENCE] use for [USE CASE]?"
10. "Is [BRAND] a good [CATEGORY] solution?"

After all 10: build a SOV table — brand name vs. query type — showing presence/absence. Where is [BRAND]'s share of voice concentrated? Where is it absent?

Share of voice without quality scoring is incomplete. Being mentioned 10 times in a footnote is worse than being mentioned 3 times as a primary recommendation.

Re-evaluate [BRAND]'s SOV across five queries using a quality-weighted scoring system:

Scoring: 
- Primary recommendation with specific framing = 3 points
- Secondary recommendation with positive framing = 2 points
- Mentioned with neutral framing = 1 point
- Mentioned with caveats or qualifications = 0.5 points
- Not mentioned = 0 points

Queries: [Choose 5 from the list in Prompt 2]

For each query: score [BRAND] and [COMPETITOR]. Then calculate quality-weighted SOV for each. The gap between raw mention frequency and quality-weighted SOV reveals whether [BRAND] is being mentioned well or just mentioned.

I want to simulate how [BRAND]'s AI share of voice might be trending — based on observable signals.

Assess the following signals and their likely impact on [BRAND]'s SOV trajectory:

Signal 1 — Content investment: Is [BRAND] publishing new, specific, evidence-backed content at a rate that builds retrieval signal? Or is content production slowing, aging, or decreasing in quality?

Signal 2 — Proof density: Is the volume and specificity of [BRAND]'s public proof growing — more case studies, more reviews, more third-party validation? Or is the proof archive stagnant?

Signal 3 — Competitive pressure: Are competitors in [CATEGORY] building content and authority faster than [BRAND] — which would dilute [BRAND]'s relative SOV even without any decline in absolute signal?

Signal 4 — Category evolution: Is [CATEGORY] evolving in a direction that favors or disfavors [BRAND]'s current positioning and signal mix?

Based on these signals: is [BRAND]'s AI SOV likely trending up, flat, or declining? What would reverse a negative trend?

[BRAND] may have strong SOV with some audience segments and weak SOV with others. Segment-level SOV analysis reveals where to invest and where to defend.

Run SOV measurements across three audience segments that are all within [BRAND]'s target market:

Segment 1: [DESCRIBE SEGMENT — e.g., "early-stage startup founders"]
Segment 2: [DESCRIBE SEGMENT — e.g., "enterprise procurement teams"]
Segment 3: [DESCRIBE SEGMENT — e.g., "solo practitioners or consultants"]

For each segment, answer the query: "What [CATEGORY] tool is best for [SEGMENT DESCRIPTION]?"

After all three: where is [BRAND]'s SOV strongest by segment? Where is it weakest? What does that tell us about where brand investment has been concentrated — and where the underserved opportunity is?

I want to benchmark [BRAND]'s AI SOV against three key competitors in [CATEGORY]: [COMPETITOR 1], [COMPETITOR 2], [COMPETITOR 3].

For each of the following 8 query types, answer naturally and record all brand mentions with framing quality:

1. "Best [CATEGORY] tools"
2. "[CATEGORY] for [TARGET AUDIENCE]"
3. "Top [CATEGORY] platforms"
4. "[USE CASE] tools"
5. "Most recommended [CATEGORY]"
6. "[CATEGORY] with best support"
7. "Easiest [CATEGORY] to use"
8. "Best enterprise [CATEGORY]"

After all 8: build a competitive SOV table. Which brand has the highest total mentions? Which has the highest quality-weighted mentions? Where is each brand's SOV concentrated — and which query types are genuinely contested vs. owned by one brand?

[BRAND]'s current AI share of voice baseline: [PASTE FROM PROMPT 1 OR 2]

Target: [BRAND] wants to increase its quality-weighted AI SOV from [X]% to [Y]% within 12 months across the queries most important to [TARGET AUDIENCE].

Build an SOV growth strategy:

Lever 1 — Content signal density: What specific content investments — topics, formats, publishing cadence — would most directly improve [BRAND]'s retrieval frequency for the query types where SOV is currently lowest?

Lever 2 — Proof signal amplification: What proof content — case studies, outcome data, reviews, analyst coverage — would most directly improve recommendation quality score (moving mentions from footnote to primary recommendation)?

Lever 3 — Category and audience signal clarity: What positioning and messaging changes would make [BRAND] more precisely matched to the highest-value query types — improving both frequency and framing quality?

Lever 4 — Competitive displacement: In which 2–3 query types is [BRAND] closest to displacing a competitor as the primary recommendation — and what is the specific investment required to tip that balance?

For each lever: name a 30-day milestone that would signal progress.