competitor-monitor

name

competitor-monitor

description

Set up ongoing competitor monitoring — captures per-competitor baselines across content, pricing, ads, social, SEO, and SERP features, saves them via competitor-tracker.py, configures per-dimension scan schedules and alert rules, and produces an initial competitive intelligence brief with threat rankings. Triggers on "/digital-marketing-pro:competitor-monitor", "track our competitors over time", "watch competitor pricing pages", "set up competitor tracking", "detect when competitors change their site". Baselines power /digital-marketing-pro:competitor-alerts and share-of-voice trends. Reads the brand profile for competitive landscape context.

/digital-marketing-pro:competitor-monitor

Purpose

Set up and manage ongoing competitor monitoring. Define which competitors to track, what to monitor (content changes, pricing updates, ad activity, social mentions, SEO rankings, SERP feature ownership), how often to scan each dimension, and what alerts to trigger when significant changes are detected. This command establishes competitive intelligence baselines by capturing the current state of each competitor across all monitored dimensions, then configures recurring scans to detect and surface changes over time. Baselines serve as the reference point for all future change detection — without them, alerts have no context for what constitutes a meaningful shift versus normal fluctuation. Supports per-dimension scan frequencies so high-velocity dimensions like pricing and ads can be checked daily while slower-moving dimensions like content strategy and SEO authority are reviewed weekly or monthly. The monitoring configuration persists across sessions and powers both the competitor-alerts notification system and the share-of-voice trend tracking.

Input Required

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

• Competitors to track: A list of competitor names with their primary website URLs — e.g., "Acme Corp (acme.com), Beta Inc (beta.io), Gamma Labs (gammalabs.com)". Each competitor becomes a monitored entity with its own baseline profile and independent scan schedule. Minimum one competitor required, no upper limit but recommend 3-8 for manageable monitoring volume and meaningful competitive context without signal overload
• Monitoring dimensions: Which competitive aspects to track for each competitor — content (new pages, blog posts, significant page edits, messaging changes on key pages), pricing (pricing page changes, plan restructuring, discount offers, free trial modifications), ads (new Google Ads campaigns, Meta Ad Library activity, ad copy and creative changes, new platform presence), social (mention volume, sentiment shifts, follower growth, posting frequency and engagement rates), seo (organic keyword rankings on tracked terms, domain authority changes, backlink profile shifts, new content indexation), serp (featured snippet ownership, People Also Ask presence, knowledge panel changes, AI overview citations). Select all for comprehensive coverage or choose a subset per competitor based on competitive relevance
• Scan frequency per dimension: How often to check each dimension — daily, weekly, or monthly. Recommended defaults: daily for pricing and ads (high-velocity, time-sensitive competitive signals requiring fast response), weekly for content and SEO (meaningful changes accumulate over days, not hours), monthly for full strategic review (positioning, messaging, market stance, and competitive narrative evolution). Custom frequencies can be set per competitor per dimension for asymmetric monitoring
• Alert thresholds and notification channel: What constitutes a significant change worth alerting on per dimension, and where to send alerts — Slack channel name (e.g., #competitor-intel) or email address. Thresholds can be qualitative ("any pricing change") or quantitative ("ranking drop of more than 5 positions on any tracked keyword", "social mention volume exceeding 2x baseline"). If not specified, sensible defaults are applied per dimension based on typical competitive volatility patterns
• Tracked keywords (optional): Specific keywords to monitor for SEO and SERP dimension tracking — brand terms, category head terms, product-specific terms, and high-intent commercial queries where competitor visibility matters most. If omitted, keywords are inferred from brand context, competitor content overlap analysis, and any existing keyword research data

Process

1. Load brand context: Read ~/.claude-marketing/brands/_active-brand.json for the active slug, then load ~/.claude-marketing/brands/{slug}/profile.json. Apply brand positioning, competitive landscape context, target market definitions, and industry vertical. Also check for guidelines at ~/.claude-marketing/brands/{slug}/guidelines/_manifest.json — if present, load competitive sensitivity rules and any competitor-specific monitoring preferences. 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. Validate competitor URLs and collect initial baseline data: For each competitor, verify the provided URL resolves and identify the correct root domain with any relevant subdomains. Capture the current state across all selected monitoring dimensions — website key pages (homepage, pricing, product, about, blog) with full meta tag snapshots, page titles, H1 headings, and core messaging blocks; pricing page structure including plan names, price points, feature lists, and tier differentiation; social media profiles across LinkedIn, Twitter/X, Facebook, Instagram, and YouTube with current follower counts, posting frequency, and recent engagement metrics; current ad activity from Google Ads Transparency Center and Meta Ad Library including active campaigns, ad copy samples, and creative formats; and organic search visibility on tracked keywords with current positions, estimated traffic, and SERP feature ownership. This comprehensive baseline snapshot becomes the reference point against which all future changes are measured.
3. Save baselines via competitor-tracker.py: Execute python "${CLAUDE_PLUGIN_ROOT}/scripts/competitor-tracker.py" --brand {slug} --action save-baseline --competitor {name} --url {url} --data '{baseline_json}' for each competitor. Each baseline record includes the competitor name, primary URL, dimension-specific data snapshots with structured fields for comparison, collection timestamp, data source references, and data completeness indicators (marking any dimensions where data collection was partial or unavailable). Baselines are stored per-brand at ~/.claude-marketing/brands/{slug}/competitors/ so multiple brands can track overlapping competitors with fully independent monitoring contexts and separate change detection histories.
4. Configure monitoring schedule per dimension: Set up the recurring scan schedule based on user-specified or default frequencies. Daily scans for pricing and ad activity — changes in these dimensions are time-sensitive and may require immediate competitive response such as counter-offers or bid adjustments. Weekly scans for content changes and SEO rankings — meaningful shifts accumulate over several days and weekly cadence provides sufficient detection speed without excessive scanning overhead. Monthly full strategic reviews covering competitive positioning analysis, messaging evolution, market stance assessment, and competitive narrative trajectory. Each schedule entry records the competitor name, dimension, frequency, next scan date, data collection method, and expected scan duration. Schedule persists across sessions and powers automated scanning when integrated with cron or scheduling services.

Output

A structured competitor monitoring setup containing:

• Competitor profiles with baselines saved: Per-competitor profile cards showing name, URL, all captured baseline metrics across every monitored dimension, data completeness indicators, data collection timestamp, and storage confirmation with file path from competitor-tracker.py
• Monitoring schedule configuration: Table of all scheduled scans — competitor name, dimension, frequency (daily/weekly/monthly), next scheduled scan date, data collection method, and estimated scan duration for each entry. Total scan volume summary showing scans per day, per week, and per month across all competitors
• Alert rules defined: Per-dimension alert trigger conditions with specific thresholds (quantitative where applicable, qualitative otherwise), urgency tier assignment (critical/warning/info), notification channel and routing configuration, and estimated alert frequency per dimension based on historical competitive activity patterns observed during baseline collection
• Initial competitive intelligence brief: Structured narrative summary per competitor covering market positioning, pricing strategy, content approach, social presence, SEO authority, and competitive overlap areas — with competitive threat ranking (primary/secondary/emerging), strategic watch recommendations, and key questions the monitoring should help answer over time
• Next scan dates per competitor per dimension: Calendar view of all upcoming scans organized by date, so the user knows exactly when the first change detection pass will run for each competitor-dimension combination and when to expect the first monitoring results

Agents Used

• competitive-intel — Competitor analysis and research across all monitoring dimensions including website auditing, pricing intelligence, ad activity scanning, and social profile benchmarking. Initial baseline data collection from public websites, ad libraries, and social platforms. Monitoring schedule configuration with per-dimension frequency optimization based on competitive volatility assessment. Alert rule definition with threshold calibration informed by competitive activity patterns and dimension-specific noise levels. Change detection framework setup with significance criteria tuned to minimize false positives while catching meaningful competitive shifts. Competitive intelligence brief synthesis with threat assessment, strategic watch recommendations, and competitive narrative interpretation