Overview

Revenue Forecast builds a real growth model for brands with D2C sales history — projecting revenue, spend and P&L over a future period, with cohort and LTV logic under the hood. Ask for a 12-month forecast or a spend what-if and get a defensible model, not a guess.

What it does

• Revenue projection — over any future window, grounded in your history.

• Spend scenarios — what-if analysis as you scale or cut budget.

• Cohort & LTV logic — repeat behavior baked into the model.

• P&L view — revenue, spend and margin together.

• Defensible math — assumptions shown, not a black box.
  
  

How to use it

1. Connect Shopify and your ad platforms.

2. Ask for a forecast — name the horizon or a spend scenario.

3. Shadow models revenue from your history and cohorts.

4. Review the model — adjust assumptions and re-run.

Run it in-app or from Claude / ChatGPT via the Shadow MCP.

What it needs

Existing D2C sales history (Shopify) and, ideally, ad-spend data. New brands with no history → use Pre-Revenue Forecast.

What you get

A revenue/P&L model over your horizon with spend scenarios, cohort logic and the assumptions laid out.

Not the right skill when…

• You're pre-revenue / launching → use Pre-Revenue Forecast, instead

• You want a past-period recap → use Performance Report, instead

• You want to know why a metric moved → use Root-Cause Analysis, instead