share-of-voice

/digital-marketing-pro:share-of-voice

Metrics: mention rate, citation rate, share of model, share of AI voice, sentiment and the AI Visibility Score are defined, with formulas, in skills/context-engine/ai-visibility-metrics.md. Report every score with its formula and its sample size (n).

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Script location. If your host does not set ${CLAUDE_PLUGIN_ROOT}, the scripts are in this plugin's scripts/ folder, next to skills/. Run them with Python 3.10 or newer: python, or python3 where python is not found (macOS, many Linux systems).

Purpose

Calculate and track share of voice across multiple competitive dimensions. Measure how visible the brand is relative to competitors across organic search (keyword rankings weighted by search volume), paid search (impression share and auction dynamics), social media (mention volume and sentiment-weighted presence), and AI engines (GEO visibility and citation rates). Share of voice is a leading indicator of market share — brands that consistently outperform competitors in visibility tend to gain market share over time, making SOV one of the most strategically important competitive metrics to track. This command provides a comprehensive competitive visibility picture by aggregating dimension-specific SOV scores into an overall competitive position assessment, with trend tracking to surface momentum shifts before they impact pipeline or revenue. Supports both point-in-time snapshots for current competitive standing and historical trend analysis when previous SOV measurements exist from prior runs.

Input Required

The user must provide (or will be prompted for):

• Competitors to compare: A list of competitor names to include in the SOV calculation — e.g., "Acme Corp, Beta Inc, Gamma Labs". These should match competitors already tracked via competitor-monitor with saved baselines for the richest analysis, though new competitors can be added on the fly with reduced historical context and no trend data for the first measurement. Minimum two competitors recommended for meaningful competitive comparison, but single-competitor head-to-head analysis is supported for focused rivalry assessment
• SOV dimensions to calculate: Which visibility dimensions to include in the analysis — organic (keyword ranking visibility weighted by monthly search volume across the target keyword set), paid (Google Ads impression share, auction insights, and Meta ads impression data where available), social (mention volume and sentiment-weighted presence across social platforms over the specified time period), ai (AI engine citation rates and GEO visibility scores across the 6 canonical AI surfaces — Google AI Mode, Google AI Overviews, ChatGPT, Perplexity, Gemini, and Copilot; the same surface set and rubric defined in /digital-marketing-pro:aeo-audit). Select all dimensions for a comprehensive competitive visibility picture or choose individual dimensions for focused analysis on a specific channel
• Target keyword list: The keyword set used for organic and paid SOV calculation — brand terms, category head terms, product-specific terms, and high-intent commercial queries where competitive visibility directly impacts pipeline. If not provided, defaults to keywords from brand context profile, any tracked keyword lists from previous keyword-research or seo-audit commands, and competitor overlap terms identified during baseline collection
• Time period for social listening data: The date range for social mention volume and sentiment analysis — e.g., "last 30 days", "Q4 2025", "January 2026", "trailing 90 days". Longer periods smooth out event-driven spikes and produce more reliable SOV percentages that reflect sustained presence rather than momentary virality. If not specified, defaults to the trailing 30 days
• Comparison period (optional): A previous time period to compare against for trend analysis — e.g., "previous 30 days", "same period last year", "last quarter". Enables delta reporting showing SOV gains and losses per dimension per competitor, surfacing competitive momentum shifts and identifying which entities are gaining or losing ground

Process

1. Load brand context and load competitor baselines: Read ~/.claude-marketing/brands/_active-brand.json for the active slug, then load ~/.claude-marketing/brands/{slug}/profile.json. Apply brand positioning, target market definitions, and competitive landscape context. Load existing competitor baselines and monitoring data from competitor-tracker.py to pull saved competitor profiles, tracked keyword lists, and any previous SOV measurements for trend comparison. If a comparison period was specified, retrieve the SOV snapshot from that period for delta calculation. Check for agency SOPs at ~/.claude-marketing/sops/. If no brand exists, ask: "Set up a brand first (/digital-marketing-pro:brand-setup)?" — or proceed with defaults.
2. Calculate organic keyword SOV: For each target keyword in the keyword set, determine the brand's current ranking position and every competitor's ranking position using available search ranking data. Weight each keyword by its monthly search volume to reflect actual visibility impact — a position 3 ranking on a 50,000 volume keyword contributes more to SOV than a position 1 on a 500 volume keyword. Calculate a visibility score per position using a click-through-rate-based model — position 1 receives 100% visibility, position 2 approximately 65%, position 3 approximately 45%, position 4 approximately 30%, position 5 approximately 22%, scaling down through position 10 at approximately 10%, with page 2 and beyond receiving 0% visibility. For each entity (the brand and each competitor), sum the visibility-weighted scores across all keywords in the set and express as a percentage of the total available visibility pool. The result is organic SOV — the share of total organic search visibility each entity captures across the tracked keyword universe.
3. Calculate paid SOV: Pull auction insights data from Google Ads MCP for the target keyword set — impression share (percentage of eligible impressions actually won), overlap rate (how often each competitor's ads appeared alongside the brand's), outranking share (percentage of auctions where the brand's ad ranked above each competitor's), and top-of-page rate (percentage of impressions appearing above organic results). Aggregate these metrics into a paid search SOV score per entity that reflects both visibility volume and competitive positioning quality. If Meta Ads data is available via the Meta Ads MCP, incorporate impression share, estimated reach metrics, and audience overlap data for segments relevant to the brand's target market. Combine search and social ad metrics into a weighted paid SOV score reflecting total paid visibility across platforms.
4. Calculate social SOV: Pull mention volume and sentiment data from a social listening connector if one is configured (none ships by default — connect one via /digital-marketing-pro:add-integration, or work from an approved public-source evidence packet as described below) for the brand and each competitor over the specified time period. Calculate raw volume share — each entity's total mention count as a percentage of the combined mention volume across all tracked entities, representing pure conversation share. Then calculate sentiment-weighted share — multiply each entity's volume share by their average sentiment score on a normalized scale (positive mentions weighted at 1.5x, neutral at 1.0x, negative discounted to 0.5x) to produce a quality-adjusted social SOV that rewards brands generating positive conversation, not just high volume. Report both raw and sentiment-weighted social SOV to surface cases where a competitor has high volume but poor sentiment, indicating controversy rather than strength.
   • If X/Twitter is in scope and connector coverage is incomplete, load skills/share-of-voice/x-twitter-source-evidence.md before scoring. Use it to build an auditable source-evidence packet from approved public sources, then keep mention counting, sentiment scoring, and recommendations inside this skill.
5. Calculate AI visibility SOV: Use GEO audit data from geo-tracker.py to compare brand versus competitor citation rates and recommendation frequency across the 6 canonical AI surfaces — Google AI Mode, Google AI Overviews, ChatGPT, Perplexity, Gemini, and Copilot (the PLATFORMS constant in scripts/geo-tracker.py; scored with the canonical rubric from /digital-marketing-pro:aeo-audit). For each entity, calculate the percentage of AI-generated responses to category-relevant queries that cite, recommend, or reference them by name. Express as AI SOV — the share of AI engine visibility each entity captures in the category. Weight by AI engine market share where data is available (e.g., ChatGPT citations weighted higher than smaller engines). If GEO data is not available for all competitors, flag the data gap explicitly and provide SOV calculations based on available data with confidence level indicators noting which competitors have incomplete AI visibility profiles.
6. Aggregate into unified SOV dashboard: Combine all dimension-specific SOV scores into a unified competitive visibility assessment. Calculate per-dimension SOV percentages (organic, paid, social, AI) and an overall weighted SOV score using default dimension weights: organic 35%, paid 25%, social 25%, AI 15% — adjustable based on industry characteristics and brand channel priorities (e.g., a B2B SaaS brand might weight organic and AI higher while reducing social weight). If a comparison period was specified, calculate deltas showing SOV movement per dimension per competitor with directional indicators. Identify the brand's strongest dimensions (competitive advantages to protect) and weakest dimensions (gaps to close), and flag any competitors showing consecutive-period momentum gains that could indicate an emerging competitive threat.
7. Save SOV data via competitor-tracker.py: Persist the complete SOV measurement — full dimension breakdowns, per-competitor scores, keyword-level organic SOV detail, platform-level paid and social SOV detail, AI-surface-level GEO SOV detail, and measurement timestamp — with:

   # write with the Write tool first: {data_file} holds {"dimensions":{...},"competitors":[...],"measured_at":"YYYY-MM-DD"}
   python "${CLAUDE_PLUGIN_ROOT}/scripts/competitor-tracker.py" \
       --brand {slug} --action share-of-voice \
       --data "@{data_file}"
   

   This creates a time-series data point in the brand's competitive visibility history. Each saved measurement enables trend analysis on subsequent runs — powering period-over-period comparison, momentum detection, seasonal pattern recognition, and long-term competitive trajectory charting across all dimensions.

Output

A structured share of voice analysis containing:

• SOV dashboard: Overall share of voice percentage for the brand and each competitor, plus per-dimension SOV breakdowns (organic %, paid %, social %, AI %) displayed as a competitive comparison table with the brand highlighted and ranked against all tracked competitors. Includes the dimension weights used for the overall score calculation
• Competitor comparison table: Side-by-side matrix of all entities across all measured dimensions — overall SOV rank and percentage, organic SOV, paid SOV, social SOV, AI SOV — sorted by overall SOV descending with rank position indicators and gap-to-leader metrics for each non-leading entity
• Trend vs previous measurement: If historical SOV data exists from prior runs, delta values showing change since last measurement — overall SOV point movement and per-dimension changes per entity, with directional indicators (gaining, stable, declining), momentum flags for entities with two or more consecutive period-over-period gains, and alert flags for any entity that crossed a significant SOV threshold (e.g., overtook the brand in a dimension)
• Dimension-level breakdown: Detailed drill-down per dimension showing the specific drivers — which keywords contribute most to organic SOV and where the biggest ranking gaps exist, which auction segments and match types drive paid SOV differences, which social platforms and conversation topics drive social SOV, and which AI engines and query categories contribute to AI SOV. Enables tactical action on the most impactful specific opportunities
• Source-evidence appendix: For X/Twitter social SOV, include the query set, time window, collection source, sampling limits, deduplication rule, and record counts by entity so reviewers can trace how public conversation evidence became SOV inputs
• Opportunity areas: Prioritized list of dimensions and specific areas where the brand's SOV is lowest relative to competitors, with estimated effort level and potential SOV impact for closing each gap — e.g., "Organic SOV on 'project management software' cluster is 8% vs Acme's 34% — targeting these 12 keywords with dedicated content could add an estimated 12 points of organic SOV over 6 months"
• Historical SOV trend chart data: Time-series data points for all entities across all dimensions, structured and formatted for visualization — enables trend charting in dashboards to visually spot competitive momentum shifts, seasonal patterns, and long-term trajectory divergence across weeks and months of measurement history

Agents Used

• competitive-intel — Competitive data collection across all SOV dimensions including organic ranking research, ad library analysis, social listening queries, and AI citation auditing. Competitor baseline and historical data retrieval for trend comparison. Competitive positioning interpretation with strategic context on what SOV shifts mean for market dynamics, revenue implications, and recommended competitive response priorities
• analytics-analyst — Multi-dimensional data aggregation with configurable weighting for overall SOV score calculation, click-through-rate visibility modeling for organic keyword SOV, sentiment-weighted social SOV computation, trend analysis with period-over-period delta calculation and momentum detection, opportunity sizing with effort-impact estimation for SOV gap closure, and visualization-ready data structuring for dashboard tables and historical trend chart outputs