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what-if

Run a quick what-if on 2-4 budget scenarios with revenue and ROI estimates. "compare two budget splits"

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# /digital-marketing-pro:what-if

## Purpose

Quick scenario comparison tool. Test 2-4 marketing scenarios against each other — different budget allocations, channel mixes, or strategic approaches — and see projected outcomes side-by-side. This is the lighter, faster alternative to full Monte Carlo simulation (`/digital-marketing-pro:simulate`). Where simulate runs thousands of iterations with full probability distributions, what-if uses point estimates with simple variance bands to give directional answers in minutes. Use it for rapid decision-making when you need a quick read on "should we do A or B?" without the statistical depth of a full simulation — team meetings, Slack discussions, quick planning calls, or narrowing down options before running a deeper analysis.

> **Simulated output — not a forecast.** what-if projections are directional point-estimates produced by `revenue-simulator.py` from your stated assumptions and historical benchmarks, not measured predictions. Treat every scenario number as a planning aid: validate the ROI assumptions against your own data before committing budget. All example dollar figures in this skill are SYNTHETIC (illustrative only — never reuse these numbers).

## Input Required

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

- **Scenarios to compare**: 2-4 named scenarios, each with channel-level budget allocations and expected ROI per channel. Examples: "Scenario A: Heavy paid — $50K Google Ads, $30K Meta, $10K email" vs "Scenario B: Content-led — $20K Google Ads, $15K Meta, $40K content, $15K SEO." Each scenario needs a descriptive name and channel budget breakdown. If the user provides only high-level descriptions ("more on paid, less on organic"), ask for specific dollar allocations or percentage splits
- **Current baseline**: The existing budget allocation and recent performance as the reference point for comparison — what the brand is doing right now so each scenario shows a clear delta. If not provided, pull from brand context historical data