/roi-calculator

name: roi-calculator
description: "Compute campaign ROI from spend, conversion, and revenue inputs — channel-level ROI/ROAS/CPA/CPL, blended totals, five-model attribution comparison (last-touch, first-touch, linear, time-decay, position-based), LTV payback periods, industry benchmark ratings, and 2-3 modeled budget-reallocation scenarios, packaged as an executive-ready report. Triggers on \"/digital-marketing-pro:roi-calculator\", \"what's the ROI on this campaign\", \"compare ROAS across channels\", \"is our CAC sustainable against LTV\", \"where should we shift budget\". Runs roi-calculator.py, reads industry benchmarks for the brand's vertical, and logs results to the campaign tracker for period-over-period trend comparison."
argument-hint: "[campaign-name]"

/digital-marketing-pro:roi-calculator

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"` with spend, revenue, and conversion data 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