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ohsonerdy
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paid-ltv-optimization

Compute channel-specific cohort LTV, CAC, and payback periods for paid acquisition decisions.

Updated Aug 27, 2026
Search Tags:#attribution#payback period#ecommerce#media optimization#cac#paid ads#cohort ltv
One-click install
npx skills add https://github.com/ohsonerdy/openclaw-frontier-stack --skill paid-ltv-optimization
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: paid-ltv-optimization
Source: https://github.com/ohsonerdy/openclaw-frontier-stack/tree/main/skills/paid-ltv-optimization
Command: npx skills add https://github.com/ohsonerdy/openclaw-frontier-stack --skill paid-ltv-optimization

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill helps you decide whether to scale, optimize, pause, or restructure each paid acquisition channel by evaluating cohort LTV versus CAC and payback time, using margin-adjusted economics rather than first-purchase ROAS or blended CAC alone.

Core Features & Use Cases

  • Channel-level CAC and cohort LTV (30/90/180d): Compares acquisition channels by how long customers survive and how much value they generate over time.
  • Payback period with margin awareness: Converts CAC into “how fast you get your contribution-margin back,” enabling cash-flow-safe decisions.
  • Attribution sensitivity checks: Runs or requests attribution model context (e.g., last-click vs multi-touch) to avoid acting on misleading channel credit.
  • Decision matrix + channel playbooks: Outputs a four-channel verdict (Scale / Cash-constrained scale / Optimize / Pause-or-restructure) and provides concrete actions and tests.

Quick Start

Use the paid-ltv-optimization skill to evaluate Meta, Google, and TikTok using your last 90 days of spend and cohort LTV so you can decide exactly what to scale this week.

Frequently Asked Questions about paid-ltv-optimization

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I optimize paid ads by cohort LTV and payback period instead of first-purchase ROAS?▼

You can optimize paid ads by computing channel-specific cohort LTV at 30, 90, and 180-day windows, then comparing it against CAC and margin-adjusted payback periods to generate a scale, optimize, or pause verdict.

What is the best way to decide whether to scale or pause Meta, Google, or TikTok paid acquisition channels?▼

The best way to decide is to evaluate each channel using a decision matrix based on margin-adjusted economics, explicit payback periods, and attribution stability signals to produce a concrete verdict.

How do I calculate margin-adjusted payback periods for ecommerce paid acquisition?▼

Calculate margin-adjusted payback periods by converting your channel-specific CAC into the time required to recover the contribution margin, enabling cash-flow-safe scaling decisions for your ecommerce channels.

Does attribution model sensitivity affect paid media optimization decisions?▼

Attribution model sensitivity significantly affects paid media optimization decisions, so you must run attribution context checks like last-click versus multi-touch to avoid acting on misleading channel credit.

Can I use cohort LTV and CAC to restructure underperforming ecommerce ad channels?▼

Yes, you can use cohort LTV and CAC comparisons to restructure underperforming ecommerce ad channels by applying a grid-based verdict that outputs concrete actions and tests for pause or restructure scenarios.

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