/client-report

name: client-report
description: "Generate a white-labeled client report in agency voice — weekly pulse, monthly review, or QBR — with a KPI scorecard vs targets and comparison period, channel breakdowns, top wins with attribution, root-cause analysis of misses, 3-5 strategic recommendations, and budget efficiency. Requires explicit approval before any external send; only then can it deliver via connected Slack, email, or Google Sheets MCPs and log the delivery. Triggers on \"/digital-marketing-pro:client-report\", \"prepare the monthly report for the client\", \"build the QBR for this account\", \"send the weekly performance pulse\", \"white-labeled performance report\". Reads the brand profile and pulls data via campaign-tracker.py, execution-tracker.py, and connected platform MCPs; formats via report-generator.py."

/digital-marketing-pro:client-report

Purpose

Generate a professional, white-labeled client report for a specific brand. Uses agency voice (not brand voice), includes KPI performance, channel breakdowns, strategic recommendations, and next steps. Designed for external client delivery via Slack, email, Google Sheets, or markdown — with approval gating before any external send to prevent accidental disclosure or premature delivery of draft findings.

Input Required

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

• **Brand slug**: The brand this report covers — must match a configured brand in `~/.claude-marketing/brands/`
• **Report type**: One of:
• Weekly pulse: Quick KPI snapshot with 3-5 key metrics and brief commentary
• Monthly review: Full performance analysis with channel breakdowns and recommendations
• QBR: Quarterly deep-dive with strategic roadmap and forward plan
• **Date range**: Specific start and end dates for the reporting period — defines what data is pulled and analyzed
• **Delivery channel**: Where the report should be sent — slack, email, google-sheets, or markdown-only (no external delivery, just generate the artifact)
• **Custom sections (optional)**: Any additional sections the client has requested — competitive update, creative performance breakdown, audience insights, attribution deep-dive, or ad-hoc investigation topic
• **Comparison period**: What to compare against — prior period, same period last year, plan/target, or all three simultaneously
• **Recipient list (optional)**: Specific client contacts who should receive the report if delivering via email or Slack — names and handles/addresses
• **Narrative emphasis (optional)**: What the client cares most about this period — growth, efficiency, brand awareness, pipeline generation, or revenue — influences which metrics are highlighted first and how insights are framed
• **Include appendix**: Whether to attach raw data tables and campaign-level detail as an appendix — defaults to yes for monthly and QBR, no for weekly pulse
• **White-label settings (optional)**: Agency logo placement, color scheme, and disclaimer text — pulled from agency profile if configured, otherwise uses clean defaults

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 voice, compliance rules for target markets (`skills/context-engine/compliance-rules.md`), and industry context. Also check for guidelines at `~/.claude-marketing/brands/{slug}/guidelines/_manifest.json` — if present, load restrictions. 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. **Pull all metrics for the brand**: Query connected MCP servers and run `python "${CLAUDE_PLUGIN_ROOT}/scripts/campaign-tracker.py" --brand {slug} --action list-campaigns` (then `--action get-campaign --id {id}` per campaign) to gather performance data across all active channels; filter to the specified date range during analysis 3. **Gather campaign history and execution log**: Run `python "${CLAUDE_PLUGIN_ROOT}/scripts/execution-tracker.py" --brand {slug} --action get-history` to compile all deliverables completed, campaigns launched, optimizations made, and tests concluded, then filter to the reporting period during analysis 4. **Calculate KPIs vs targets and vs comparison period**: Compute actuals against the brand's stated KPI targets from `profile.json` and against the selected comparison period — calculate deltas, percentage changes, trend direction, and statistical significance where sample sizes allow 5. **Break down performance by channel**: Segment metrics by channel (paid search, paid social, organic search, email, display, video, affiliate, etc.) with per-channel KPIs, spend, efficiency metrics (CPC, CPA, ROAS, CTR), and contribution percentage to overall goals 6. **Identify top wins and attribution**: Select the 3-5 best-performing campaigns or initiatives from the period — document what was done, what drove the result, audience and creative insights, and how it connects to business outcomes 7. **Analyze underperformance with root causes**: For any KPI that missed target, identify root causes:

• External factors: market shifts, seasonality, competitive moves, platform algorithm changes
• Internal factors: budget constraints, creative fatigue, audience saturation, timing misalignment
• Corrective actions: what was already done and what is recommended for next period

8. **Generate strategic recommendations**: Based on performance data, formulate 3-5 actionable recommendations — what to scale, what to pause, what to test next, where budget should shift, and what new opportunities to explore 9. **Write report in agency voice**: Draft the full report using professional, third-person agency voice — NOT the brand's personality. Focus on clarity, data-backed insights, actionable next steps, and a confident but honest tone that builds client trust 10. **Format for delivery channel**: Run `py