Client Report

/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 campaign-tracker.py --brand {slug} --action metrics to gather performance data across all active channels for the specified date range
3. Gather campaign history and execution log: Run execution-tracker.py --brand {slug} --action list --period {date_range} to compile all deliverables completed, campaigns launched, optimizations made, and tests concluded during the period
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

1. 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
2. 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
3. Format for delivery channel: Run report-generator.py --brand {slug} --format {channel} --type {report_type} to produce the channel-specific format (Slack blocks, email HTML, Google Sheets layout, or clean markdown)
4. Create approval checkpoint: Present the full report preview for review. Risk level: low. Require explicit approval before any external delivery — highlight any sensitive data, unexpected results, or negative findings that may need pre-briefing with the client
5. Deliver via MCP if approved: On approval, send via the appropriate MCP integration (Slack MCP, email MCP, Google Sheets MCP) if a delivery channel was specified. Handle delivery errors gracefully with retry guidance
6. Log delivery and archive: Record the report delivery in the execution log with timestamp, recipients, delivery confirmation status, report version, and a reference to the archived report for future comparison

Output

A structured client report containing:

• Executive summary: 3-5 sentence overview of the period — headline result, key wins, areas of focus, outlook for next period, and one recommended action for the client
• KPI scorecard: Actuals vs targets vs comparison period in a scannable table with color-coded status indicators (exceeded, on track, at risk, missed) and trend arrows showing directional momentum
• Channel performance breakdown: Per-channel metrics with spend, results, efficiency metrics (CPC, CPA, ROAS, CTR), contribution percentage to overall goals, and channel health assessment
• Campaign highlights with attribution: Top-performing campaigns with what drove success, creative and audience insights, measured impact, and replication recommendations for future campaigns
• Underperformance analysis: Honest assessment of any misses with root cause categorization (external vs internal), impact quantification, corrective actions taken, and preventive measures for next period
• Strategic recommendations (3-5): Data-backed next steps with expected impact, investment required, implementation timeline, priority ranking, and connection to the client's stated business objectives
• Budget efficiency analysis: Spend vs return summary by channel, cost trend lines over the period, budget utilization rate, and efficiency comparison to prior periods with improvement/decline indicators
• Upcoming deliverables and timeline: What the agency will deliver next period with dates, milestones, dependencies, and any client actions required to keep the plan on track
• Appendix (if requested): Raw data tables, campaign-level breakdowns, full metric exports, creative performance data, and supporting calculations for detailed review
• Delivery confirmation: Channel, timestamp, recipients, delivery status, and report version — or markdown artifact if no external delivery was requested

Agents Used

• agency-operations — Report voice and tone (agency professional, not brand personality), client context awareness, approval workflow management, white-label formatting, and delivery coordination
• analytics-analyst — Metrics analysis, KPI calculations, channel breakdowns, trend analysis, comparison computations, attribution modeling, statistical significance checks, and recommendation data support
• execution-coordinator — Report formatting for delivery channels, MCP integration delivery, execution logging, delivery error handling, and archival

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: indranilbanerjee
• Source: indranilbanerjee/digital-marketing-pro
• License: MIT
• Homepage: https://indranil.in

Install and usage instructions live in the source repository linked above.