/digital-marketing-pro:roi-calculator

Script location. If your host does not set ${CLAUDE_PLUGIN_ROOT}, the scripts are in this plugin's scripts/ folder, next to skills/. Run them with Python 3.10 or newer: python, or python3 where python is not found (macOS, many Linux systems).

Purpose

Campaign ROI calculator with multi-touch attribution models. Produces a comprehensive ROI analysis across channels for budget justification, optimization recommendations, and executive reporting.

Input Required

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

• Campaign spend by channel: Dollar amounts invested per channel (paid search, paid social, email, SEO, content, events, etc.)
• Conversions and revenue by channel: Number of conversions and total revenue attributed to each channel
• Time period: The date range for the analysis (week, month, quarter, year)
• Attribution model preference: Last-touch, first-touch, linear, time-decay, or position-based (or compare all models)
• Customer LTV: Optional -- average customer lifetime value for long-term ROI projection
• Industry vertical: For benchmark comparison context
• Conversion definitions: What counts as a conversion (purchase, lead, signup, demo request, trial start, etc.)
• Cost inputs beyond ad spend: Optional -- agency fees, tool costs, creative production costs, team time

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 voice, compliance, industry context. Check guidelines/_manifest.json for restrictions, messaging, channel styles, voice-and-tone rules, and templates. If a template matching this command exists in ~/.claude-marketing/brands/{slug}/templates/, apply its format. If no brand exists, prompt for /digital-marketing-pro:brand-setup or proceed with defaults.
2. Check campaign history: Run python "${CLAUDE_PLUGIN_ROOT}/scripts/campaign-tracker.py" --brand {slug} --action list-campaigns to pull historical campaign data for trend comparison and period-over-period analysis.
3. Run ROI calculator: Execute python "${CLAUDE_PLUGIN_ROOT}/scripts/roi-calculator.py" --channels '[{"name":"google_ads","spend":10000,"conversions":120,"revenue":42000}]' (each channel needs spend, conversions and revenue) to compute channel-level and blended metrics.
4. Calculate channel-level ROI and ROAS: For each channel, compute ROI ((revenue - cost) / cost), ROAS (revenue / cost), CPA (cost / conversions), CPL (cost / leads), and contribution margin percentage.
5. Apply attribution model: Redistribute credit across channels using the selected attribution model. If the user wants a comparison, run all five models (last-touch, first-touch, linear, time-decay, position-based) and show how each model shifts credit between channels.
6. Calculate blended ROI: Aggregate all channels into a total campaign ROI, blended ROAS, and overall CPA. Factor in LTV if provided to project short-term vs long-term ROI and payback period.
7. Compare against industry benchmarks: Reference skills/context-engine/industry-profiles.md to contextualize whether channel performance is above, at, or below industry averages for the brand's vertical.
8. Identify efficiency opportunities: Flag channels with declining marginal returns, channels where increased spend could yield disproportionate gains, and channels where CPA exceeds LTV (unsustainable spend).
9. Calculate payback period: If LTV data is provided, compute the months to break even on customer acquisition cost per channel, identifying which channels pay back fastest and which require patience for long-term value.
10. Model budget reallocation scenarios: Generate 2-3 reallocation scenarios shifting budget from underperformers to high-performers, with projected impact on total ROI, total conversions, and blended CPA.
11. Log results to campaign tracker: Record the ROI analysis in campaign-tracker.py so future analyses can compare period-over-period trends and validate whether recommended reallocations improved performance.
12. Compile executive report: Format the analysis for stakeholder presentation with clear takeaways, data tables ready for visualization, and actionable next steps.

Output

A structured ROI analysis report containing:

• Channel-by-channel performance table (spend, revenue, conversions, ROI, ROAS, CPA, CPL)
• Blended campaign ROI and overall ROAS with total spend and revenue summary
• Attribution model comparison showing credit distribution shifts across models
• LTV-adjusted ROI projection and payback period analysis (if customer LTV was provided)
• Industry benchmark comparison with above/at/below performance ratings per channel
• Efficiency analysis identifying diminishing returns and scaling opportunities
• Budget reallocation recommendations with 2-3 modeled scenarios and projected outcomes
• Underperforming channel diagnosis with specific improvement actions
• Period-over-period trend comparison (if historical data is available from campaign tracker)
• Executive summary with top 3 insights and recommended next steps
• Visualization-ready data tables formatted for Google Sheets or slide deck export

Agents Used

• analytics-analyst -- ROI computation, attribution modeling, benchmark comparison, efficiency analysis, payback period calculation, and data-driven recommendations
• marketing-strategist -- Budget optimization strategy, channel mix recommendations, reallocation scenario design, and executive-level insight framing for stakeholder communication