Customer Lifetime Value Intelligence Builder

Spreadsheets and generic LLM prompts often mistake revenue for profit or ignore the reality of acquisition costs. This skill enforces strict financial rigor to calculate Customer Lifetime Value (LTV) and Customer Acquisition Cost (CAC) based on actual cohort behavior. It prevents the common trap of using active customers instead of acquired counts, ensuring your unit economics are grounded in reality rather than optimistic projections.

What it does

• Cohort Analysis builds a ledger by freezing specific acquisition windows and tracking performance over defined horizons.
• Contribution Modeling calculates LTV by normalizing net revenue and subtracting gross margins and servicing costs.
• CAC Comparison measures acquisition spend against the total acquired cohort to determine true payback ratios.
• Experimental Design generates segment-specific experiments with holdout groups and stop rules based on behavioral evidence.
• Rigorous Auditing reviews existing drafts to identify P1-P3 defects like revenue-contribution confusion or misaligned retention intervals.

How it works

1. Creation Mode starts by ingesting cohort definitions, revenue, margins, and acquisition costs to build a contribution bridge.
2. Standardization applies finite-horizon or retention models to ensure period-consistency across all calculations.
3. Calculation outputs a structured table comparing LTV and CAC while highlighting payback limitations.
4. Audit Mode extracts claims from an existing report, rebuilds the denominators, and generates a defect register with corrections.

Frameworks & tools

This skill requires no external integrations. It processes raw purchase data, retention tables, or redacted documents provided within the chat context.

Why this beats prompting it yourself

Generic prompts often fail to subtract servicing costs or align monthly retention rates with annual horizons. This skill follows a specific six-step workflow that prevents "revenue-as-contribution" errors and forces the labeling of all assumptions to protect against misleading financial claims.

Use cases

• Unit Economics Audit to verify if a marketing report is accidentally inflating LTV/CAC ratios.
• Retention Planning to design evidence-bounded experiments for specific high-value customer segments.
• Profitability Mapping to determine if a specific acquisition channel covers its own servicing costs over a one-year horizon.
• Data Cleanup to normalize messy purchase extracts into a standardized cohort ledger.

Known limitations

This tool provides planning estimates only and is not a financial valuation or investment recommendation. It cannot predict individual behavior or guarantee future acquisition returns.