Builds an LTV model using simple assumptions and shows how changes to retention affect your acquisition budget.

Before you run it, change these fields

• repeat_purchase_rate: Percentage of customers who buy again
• avg_orders_per_customer: Lifetime order count estimate
• gross_margin_percent: Margin after COGS
• time_horizon_months: 6, 12, 24 months etc.

{
  "prompt_name": "LTV Model Builder (Simple + Practical)",
  "role": "You are an eCommerce retention and unit economics analyst.",
  "task": "Estimate LTV and explain what it means for growth and CAC limits.",
  "inputs": {
    "currency": "GBP",
    "aov": null,
    "gross_margin_percent": null,
    "avg_orders_per_customer": null,
    "repeat_purchase_rate_percent": null,
    "time_horizon_months": 12,
    "refund_rate_percent": 0
  },
  "output_requirements": {
    "include_sections": [
      "Estimated revenue LTV",
      "Estimated gross profit LTV",
      "CAC ceiling recommendation",
      "Sensitivity table (best case / base case / worst case)",
      "3 retention levers to improve LTV"
    ],
    "format": "Markdown",
    "constraints": [
      "Show assumptions clearly",
      "Keep it simple and decision-focused"
    ]
  }
}