performance-report
The performance-report command generates a comprehensive marketing analysis by ingesting raw performance data from any source and producing a structured report with KPI calculations, period-over-period trend analysis, anomaly detection with root cause identification, and channel benchmarking. Use this command when you need to transform marketing data into actionable insights for stakeholders, whether preparing an executive summary or detailed tactical breakdown for specific channels.
mkdir -p ~/.claude/commands && curl -fsSL https://raw.githubusercontent.com/indranilbanerjee/digital-marketing-pro/HEAD/commands/performance-report.md -o ~/.claude/commands/performance-report.mdperformance-report.md
# Performance Report > If you see unfamiliar placeholders or need to check which tools are connected, see [CONNECTORS.md](../CONNECTORS.md). Generate a structured marketing performance report that transforms raw data into actionable insights. Covers KPI tracking, period-over-period trends, anomaly detection, channel benchmarking, and prioritized optimization recommendations. ## Trigger User runs `/digital-marketing-pro:performance-report` or asks for a marketing report, performance review, channel analysis, or campaign results summary. ## Inputs Gather the following from the user. If not provided, ask before proceeding: 1. **Reporting period** — date range for the report (e.g., "last 30 days", "Q1 2026", "Feb 1-28") 2. **Channels to cover** — which marketing channels to include: - All channels (default) - Specific: organic search, paid search, social media, email, direct, referral 3. **Data source** — raw performance data in any of these forms: - Pasted directly into the conversation - CSV or spreadsheet file - Connected platform (if analytics or advertising connectors are available) 4. **Comparison period** — previous period, year-over-year, or custom benchmark 5. **Report audience** — who will read this: - **Executive** — high-level summary with headline metrics and strategic recommendations - **Tactical** — detailed breakdown with granular data and specific optimizations 6. **KPIs of interest** (optional) — specific metrics to focus on, or use defaults: - Traffic: sessions, users, pageviews, bounce rate - Conversions: conversion rate, leads, revenue, ROAS - Engagement: time on site, pages per session, social engagement - Email: open rate, click rate, unsubscribe rate ## Process 1. **Load brand context** — apply brand goals, targets, and industry benchmarks from the active profile 2. **Ingest data** — validate and normalize the provided performance data 3. **Calculate core KPIs** — per channel: traffic, conversions, revenue, ROAS, CPA, engagement metrics, growth rates 4. **Trend analysis** — period-over-period changes, trajectory direction, seasonality adjustments 5. **Anomaly detection** — flag significant spikes or drops with likely root causes: - Traffic anomalies (algorithm update, viral content, technical issue) - Conversion anomalies (landing page change, pricing update, competitive action) - Cost anomalies (bid changes, new competition, quality score shifts) 6. **Benchmark comparison** — compare against industry averages and brand-specific targets 7. **Generate insights** — what worked, what underperformed, and hypotheses for why 8. **Produce recommendations** — prioritized optimizations for the next period, ranked by expected impact ## Output Format ### Executive Summary - 3-5 headline metrics with period-over-period change and trend direction - Overall assessment: on track, needs attention, or urgent action required - Top win and top concern with context ### Channel Performance Dashboard | Channel | Sessions | Conversions | Revenue | ROAS | vs. Prior | Trend | |---------|----------|-------------|---------|------|-----------|-------| ### Trend Analysis - Period-over-period comparison with percentage changes - Trajectory indicators (accelerating, stable, decelerating) - Seasonality context where relevant ### Anomaly Alerts | Metric | Change | Likely Cause | Recommended Action | |--------|--------|--------------|-------------------| Flag any metric that moved more than 2 standard deviations from its trend. ### Top Wins - 3-5 areas of strongest performance with context and contributing factors ### Areas Needing Attention - 3-5 underperforming areas with root cause hypotheses and fix recommendations ### Recommendations **Immediate Actions (this week):** - Quick optimizations with expected impact **Strategic Adjustments (this month):** - Larger changes to channel mix, budget allocation, or content strategy **Next Period Goals:** - Recommended targets based on current trajectory and optimization potential ## After the Report Ask: "Would you like me to: - Dive deeper into any specific channel? (`/digital-marketing-pro:analytics-insights`) - Investigate an anomaly in detail? (`/digital-marketing-pro:anomaly-scan`) - Adjust budget allocation based on these results? (`/digital-marketing-pro:budget-optimizer`) - Generate a client-ready version of this report? (`/digital-marketing-pro:client-report`) - Create a live dashboard to track these KPIs? (`/digital-marketing-pro:live-dashboard`) - Export this data to a spreadsheet? (`/digital-marketing-pro:data-export`)"
Design a statistically rigorous A/B or multivariate test plan — If/Then/Because hypothesis, control and variant specs, required sample size per variant (absolute vs relative MDE via sample-size-calculator.py), test duration, guardrail metrics, stopping rules, and go/no-go decision criteria. Triggers on \"/digital-marketing-pro:ab-test-plan\", \"set up an A/B test\", \"how long should my test run\", \"calculate sample size for an experiment\", \"is this test result significant\". Reads the brand profile and past campaign-tracker results to avoid re-testing validated hypotheses; finished tests are evaluated with significance-tester.py by the cro-specialist agent.
Generate 3-5 ad copy variations per platform — headlines, descriptions, and CTAs formatted to Google, Meta, LinkedIn, TikTok, X, and Pinterest specs — each scored 1-10 with policy-compliance flags, A/B testing groupings, and a message-match check against the landing page. Triggers on \"/digital-marketing-pro:ad-creative\", \"write ad copy for Meta\", \"give me RSA headline variations\", \"we need LinkedIn ad copy\", \"draft TikTok ad creative\". Reads the brand profile, guidelines, and compliance rules; routes video ad scripts to /digital-marketing-pro:video-script and gates AI-generated visuals for EU campaigns through /digital-marketing-pro:c2pa-metadata and /digital-marketing-pro:check.
Walk through adding a custom MCP server integration to the plugin — searches npm for an existing MCP package (or scaffolds a custom server from the plugin's guide), generates the exact .mcp.json entry, sets up environment-variable credentials, tests connectivity, and documents the tools the new server exposes. Triggers on \"/digital-marketing-pro:add-integration\", \"connect Ahrefs to the plugin\", \"add a new MCP server\", \"integrate our internal API\", \"hook up Stripe data\". Reads the brand profile and agency credential profiles at ~/.claude-marketing/credentials/ to map client-specific keys; custom builds follow skills/context-engine/custom-mcp-guide.md.
Audit how a brand appears across the 6 canonical AI answer surfaces — ChatGPT, Perplexity, Google AI Mode, AI Overviews, Gemini, Copilot — probing 10-25 queries into a numbered output bundle with per-platform visibility scorecards, citation-accuracy checks, a competitor matrix, content gaps, and an optimization playbook behind a four-gate quality scorecard. Triggers on \"/digital-marketing-pro:aeo-audit\", \"does ChatGPT know about our brand\", \"check our AI search visibility\", \"how does Perplexity describe us\", \"are we showing up in AI Overviews\". Reads the brand profile; reconciles probes against GSC actuals via /digital-marketing-pro:gsc-ai-performance and defines the AI-visibility scoring standard reused by geo-monitor and share-of-voice.
Strategy module for Answer Engine / Generative Engine Optimization — audits AI visibility, restructures content for citation, runs entity-consistency checks across Knowledge Graph, Wikidata, Wikipedia, Crunchbase, and LinkedIn, and produces JSON-LD schema specs, monitoring frameworks, and a 90-day LLM content strategy. Triggers on \"/digital-marketing-pro:aeo-geo\", \"how do we get cited by AI\", \"optimize for AI Overviews\", \"fix our entity consistency\", \"do we need llms.txt\". Reads the brand profile, compliance rules, and industry benchmarks; its measurement counterpart is /digital-marketing-pro:aeo-audit, with GSC actuals via /digital-marketing-pro:gsc-ai-performance.
Generate a portfolio-level dashboard across ALL client brands — per-client RAG health scores, campaign activity, budget pacing, aggregate KPIs, team utilization, pending approvals, upcoming deadlines, and an alerts panel — built for agency standups and weekly reviews. Triggers on \"/digital-marketing-pro:agency-dashboard\", \"how are all our clients doing\", \"portfolio health check\", \"budget pacing across accounts\", \"which accounts are at risk\". Enumerates every brand under ~/.claude-marketing/brands/ and pulls data via campaign-tracker.py, execution-tracker.py, and team-manager.py; drill into a single client with /digital-marketing-pro:performance-report or /digital-marketing-pro:client-report.
Invoke when the user needs to manage multiple client brands, view portfolio-level dashboards, generate client reports, manage SOPs, switch credential profiles, assign team tasks, configure regions, or generate executive summaries. Triggers on requests involving multi-client management, agency workflows, client onboarding, or portfolio oversight.
Invoke when the user needs help with marketing measurement, KPI definition, dashboard design, attribution reporting, performance analysis, competitive benchmarking, or translating data into marketing decisions. Triggers on requests involving metrics, reporting, analytics setup, or data interpretation.