/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
- 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.
- Check campaign history: Run
python campaign-tracker.py --brand {slug} --action list-campaigns to pull historical campaign data for trend comparison and period-over-period analysis.
- Run ROI calculator: Execute
scripts/roi-calculator.py with spend, revenue, and conversion data to compute channel-level and blended metrics.
- 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.
- 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.
- 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.
- 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.
- 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).
- 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.
- 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.
- 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.
- 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