competitive-intel

Competitive Intelligence

Competitor keyword tracking, traffic estimation, ad creative monitoring, and market positioning.

Objective

Provide systematic competitive intelligence: competitor keyword ranking overlap, estimated traffic share, ad creative and messaging monitoring, share-of-voice aggregation across channels, and pricing intelligence. Synthesize competitive signals into actionable strategic recommendations grounded in specific data points.

Competitor Keyword Overlap & Gap Analysis

Identify keyword opportunities by comparing your rankings against each defined competitor.

Gap types

Gap Type	Definition	Action
Missing	Competitor ranks, you do not	Evaluate for content creation
Weak	Both rank, competitor significantly higher	Optimize existing content
Strong	Both rank, you significantly higher	Defend position
Shared	Both rank in similar positions	Monitor for movement
Unique	You rank, competitor does not	Leverage as competitive advantage

Opportunity scoring

Score each gap opportunity using:

opportunity_score = search_volume * (1 - keyword_difficulty) * business_relevance

Where:

• search_volume — monthly average from SEO tool data
• keyword_difficulty — normalized 0-1 scale from SEO tool
• business_relevance — 0-1 score based on keyword-to-product/service alignment

Track competitor keyword position changes and new keyword targeting over time to detect strategy shifts.

Use scripts/keyword_gap.py for computation.

Traffic Estimation

Estimate competitor traffic share using available data sources.

Methods

1. Third-party estimates — SimilarWeb, Semrush Traffic Analytics, or DataForSEO rank tracker data provide directional traffic estimates.
2. CTR-curve proxy — Multiply keyword search volume by position-based CTR curves to estimate organic traffic per keyword, then aggregate.
3. Relative indexing — When absolute numbers are unreliable, index all competitors relative to your own verified traffic to produce a ratio-based comparison.

Always label traffic estimates with methodology and confidence level. Never present third-party estimates as precise figures.

Ad Creative Monitoring

Track competitor advertising activity across search and social channels.

Tracking scope

• Ad copy — Headlines, descriptions, display URLs, and extensions
• Offers — Promotional pricing, discounts, free trials, lead magnets
• Landing pages — URL destinations, page structure, key messaging
• Messaging themes — Value propositions, CTAs, emotional appeals

Analysis outputs

• Identify seasonal patterns in competitor advertising intensity.
• Detect ad copy rotations and new offer launches.
• Compare competitor messaging positioning against your own.
• Flag competitors making potentially non-compliant performance claims (especially relevant for financial services).

Data sources include Meta Ad Library, Google Ads Transparency Center, SpyFu, and manual monitoring exports placed in workspace/raw/competitor_data.csv.

Share of Voice Aggregation

Combine signals from organic search, paid search, social media, and earned media into a unified share-of-voice metric.

Channel components

Channel	Signal Source	Weight Method
Organic search	Keyword rankings weighted by volume	CTR-curve weighted visibility
Paid search	Impression share, ad position data	Spend-weighted or impression share
Social media	Engagement, follower growth, post volume	Engagement-weighted share
Earned media	Mentions, backlinks, PR coverage	Volume-weighted share

Aggregation

Aggregate channel-level SOV into a composite score using configurable channel weights that reflect business priorities. Default equal weighting unless configured otherwise.

See references/competitive_methodology.md for full calculation details.

Use scripts/share_of_voice.py for computation.

Pricing Intelligence

Monitor competitor pricing, offers, and promotional patterns.

Scope

• Track competitor product/service pricing from public sources.
• Detect promotional patterns: frequency, depth, timing, and triggers.
• Compare pricing positioning: premium, parity, or discount relative to market.
• Monitor offer structure changes (packaging, bundling, feature gating).

Constraints

• Use only publicly available pricing information.
• Do not scrape competitor sites in violation of terms of service.
• Flag pricing data freshness — stale data must be labeled with last-verified date.

Strategic Synthesis

Translate raw competitive data into strategic recommendations.

Competitive scorecard

Aggregate all signals into a per-competitor scorecard:

Dimension	Metrics
Search presence	Organic SOV, keyword overlap, content velocity
Paid media	Ad impression share, creative freshness, offer depth
Social presence	Engagement SOV, audience growth rate
Pricing position	Price index, promotional frequency
Overall trajectory	Accelerating / stable / decelerating investment

Recommendation requirements

• Every recommendation must link to at least one specific data point.
• Recommendations must be framed as actionable next steps, not generic advice.
• Include estimated impact where data supports it.
• Prioritize recommendations by expected impact and implementation effort.

Use scripts/competitive_synthesis.py for scorecard aggregation.

Change detection and alerting

Monitor competitor metrics for significant changes using percentage-based thresholds (not absolute) to handle competitors of different sizes.

Alert categories:

• New keyword targeting detected
• Ad copy or offer change
• Traffic share shift beyond threshold
• Pricing change
• Social activity spike

Use scripts/competitive_alerting.py for change detection.

Input / Output Data Contracts

Inputs

File pattern	Description
workspace/analysis/keyword_performance.json	Your keyword data from seo-content skill
workspace/analysis/social_benchmarks.json	Social share of voice from social-analytics
workspace/raw/competitor_data.csv	Third-party competitive data (Semrush, SimilarWeb, SpyFu exports)

Outputs

File	Description
workspace/analysis/competitive_landscape.json	Aggregated competitive intelligence across all channels
workspace/analysis/keyword_gap.json	Competitive keyword opportunities with volume and difficulty
workspace/analysis/competitive_alerts.json	New competitor activities and strategy shifts detected
workspace/reports/competitive_briefing.html	Executive competitive intelligence briefing

Competitive landscape schema

Field	Type	Description
competitor_name	string	Competitor identifier
competitor_domain	string	Primary domain
organic_sov	decimal	Organic search share of voice (0-1)
paid_sov	decimal	Paid search share of voice (0-1)
social_sov	decimal	Social media share of voice (0-1)
composite_sov	decimal	Weighted composite share of voice (0-1)
keyword_overlap	integer	Count of shared ranking keywords
keyword_gap_count	integer	Keywords where competitor ranks and you do not
trajectory	string	accelerating / stable / decelerating
last_updated	date	Analysis date (YYYY-MM-DD)

Keyword gap schema

Field	Type	Description
keyword	string	Search term
search_volume	integer	Monthly search volume
keyword_difficulty	decimal	Difficulty score (0-1)
your_position	integer	Your ranking position (null if not ranking)
competitor	string	Competitor name
competitor_position	integer	Competitor ranking position
gap_type	string	missing / weak / strong / shared / unique
opportunity_score	decimal	Composite opportunity score
business_relevance	decimal	Relevance to business (0-1)

Cross-Skill Integration

Skill	Relationship
seo-content	Upstream: provides keyword performance data for gap analysis
social-analytics	Upstream: provides social engagement benchmarks for social SOV
paid-media	Upstream: provides ad performance baselines for paid SOV comparison
attribution-analysis	Downstream: competitor activity levels serve as control variables in MMM models — when a competitor increases spend, your conversion rate may change independently
reporting	Downstream: competitive trends feed executive dashboards

Financial Services Considerations

When performing competitive intelligence for financial services clients:

• Use only publicly available information. Do not use non-public or proprietary competitor data.
• Competitor performance claims (returns, AUM growth) sourced from public filings (SEC EDGAR, FINRA BrokerCheck) are acceptable; internal estimates are not.
• Ad creative monitoring should flag competitors making potentially non-compliant performance claims (e.g., guaranteed returns, missing risk disclosures) for internal compliance awareness.
• Do not make investment performance comparisons without proper disclaimers and time-period matching.

Development Guidelines

1. Design for graceful degradation: some data sources require paid subscriptions (Semrush, Ahrefs, SimilarWeb). The skill must function with whatever data is available and clearly state what is missing.
2. Keyword gap opportunity scoring must use: search_volume * (1 - keyword_difficulty) * business_relevance.
3. Share of voice calculations must include methodology notes and data source limitations in every output.
4. Change detection for competitive alerting must use percentage change, not absolute values, to handle competitors of different sizes.
5. Strategic recommendations must be grounded in specific data points — never produce generic "best practices" advice.
6. Support manual competitor list definition. Do not auto-discover competitors to avoid scope creep.
7. All monetary calculations must use decimal.Decimal (Python) to avoid floating-point rounding errors.
8. Reference files in references/ contain methodology details. Keep this file focused on instructions and contracts.
9. Scripts in scripts/ handle deterministic computation. The LLM handles interpretation, insight generation, and recommendation framing.