An AI-driven analytical framework to calculate, benchmark, and reduce fully loaded customer acquisition costs across all marketing channels.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install afrexai-cac-optimizer
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install afrexai-cac-optimizer using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is CAC Optimizer?
The CAC Optimizer is a specialized framework designed for AI agents to master unit economics and marketing efficiency. By utilizing Openclaw Skills, this tool enables businesses to move beyond surface-level ad spend metrics and calculate a fully loaded CAC that includes sales compensation, software overhead, and agency fees. It serves as a strategic layer for growth teams, providing the logic needed to synthesize complex financial data into actionable insights.
This skill is particularly valuable for organizations looking to integrate high-level financial modeling into their automated workflows. Through Openclaw Skills, users can identify unsustainable spending patterns, model payback periods, and align their growth strategies with the latest 2026 industry benchmarks across B2B SaaS, Ecommerce, and Fintech sectors.
CAC Optimizer Use Cases
- Calculating fully loaded CAC for quarterly financial reporting and board presentations.
- Identifying the most cost-effective acquisition channels by comparing SEO, Paid Search, and Outbound performance.
- Modeling LTV:CAC ratios to determine if current marketing spend warrants further investment or immediate reduction.
- Generating automated 90-day roadmaps to trim acquisition overhead and improve lead scoring efficiency.
How CAC Optimizer Works
- The user provides raw data points including sales costs, marketing spend, overhead allocation, and new customer counts.
- The agent applies the CAC Optimizer logic to calculate the true, fully loaded acquisition cost per customer.
- The system segments the data by channel and cohort to identify specific performance drivers.
- It benchmarks the calculated metrics against 2026 industry standards to assess competitive health.
- The skill generates a prioritized reduction playbook with specific quick wins and long-term strategic actions.
CAC Optimizer Setup
To deploy the CAC Optimizer within your AI environment, ensure your agent can access the logic defined in the skill documentation. Use the following steps to integrate it via Openclaw Skills:
# Clone the business operations context packs
git clone https://github.com/afrexai-cto/context-packs.git
# Navigate to the optimizer directory
cd context-packs/cac-optimizer
# Provide the SKILL.md content to your AI agent as a system prompt or reference file
Once integrated, you can start querying your agent for channel breakdowns and payback period models using Openclaw Skills.
CAC Optimizer Data Schema & Taxonomy
The CAC Optimizer organizes marketing and financial data into a structured taxonomy for deep analysis. It primarily tracks the following data points:
| Component | Description |
|---|---|
| Cost Inputs | Includes Ad Spend, Content Production, Sales/Marketing Salaries, and Software Tools. |
| Performance Ratios | Focuses on LTV:CAC Ratio, Payback Period in months, and Gross Margin percentages. |
| Channel Metrics | Segmented data for Organic Search, Paid Social, Email, Referral, and Partner channels. |
| Cohort Tracking | Monthly customer groups analyzed by cumulative revenue at 1, 3, 6, and 12-month intervals. |
CAC Optimizer Advanced Features
- Multi-channel efficiency ranking that automatically identifies the top 20% and bottom 20% of acquisition paths.
- Industry-specific benchmarking for B2B SaaS (SMB to Enterprise), Fintech, and Healthcare sectors using Openclaw Skills.
- Automated cohort health checks that flag any user group where the LTV:CAC ratio falls below 2:1.
- Integrated payback period modeling to determine the exact month a customer becomes profitable based on gross margin.
SKILL.md
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