ecommerce growth control tower
by nowrich
An ecommerce auditor that converts store data into a prioritized growth roadmap across six business pillars.
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Works with the AI tools you already use
See it in action
You say
Analyze my Shopify store for the last 30 days. CAC is $45, AOV is $85, CR is 1.2%. Ad spend was $10k on Meta. Repeat purchase rate is 8%. Shipping costs rose by 12% this month. Generate a plan.
Your agent does
Executive Summary: Your 1.2% CR is a critical leak compared to your $45 CAC. Prioritized Action Plan:
- Optimize checkout flow (Quick Win): Impact high, Effort S.
- Review shipping zones (Quick Win): Offset 12% cost spike.
- Win-back email sequence (Big Bet): Increase 8% repeat rate.
What you get
About this skill
The problem
Ecommerce founders often drown in siloed data, struggling to identify whether their growth plateaus are caused by high CAC, poor conversion, or hidden operational leaks. Without a unified view, teams waste time on low-impact tasks while overlooking critical profit-draining issues in fulfillment or retention.
What it does
- Analyzes store health across six key dimensions: acquisition, conversion, operations, fulfillment, retention, and profitability.
- Categorizes findings into critical blocks, clear opportunities, and items to watch.
- Calculates unit economics including LTV/CAC ratios, contribution margins, and payback periods.
- Generates a prioritized 2x2 matrix of actions based on quantified impact and effort estimates.
- Produces a structured report with an executive summary, dimension-based scorecard, and ranked action plan.
Frameworks & tools
Compatible with data from Shopify, WooCommerce, Magento, BigCommerce, and custom storefronts. Processes inputs from Google Analytics, Meta Ads, Klaviyo, and ERP systems.
Why this beats prompting it yourself
General prompts often result in generic advice like "improve SEO." This skill enforces a rigorous six-pillar audit and an Impact vs. Effort prioritization logic, ensuring recommendations are based on hard math and operational reality rather than platitudes.
Use cases
- Conducting a monthly performance audit to re-align team priorities.
- Diagnosing the root cause of a sudden drop in net profitability.
- Preparing for peak seasons by identifying fulfillment and inventory bottlenecks.
- Evaluating the trade-off between increasing AOV and maintaining conversion rates.
Known limitations
Requires accurate historical data for high-confidence results. If metrics like CAC or COGS are missing, the agent uses estimates which must be verified before execution.
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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