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Grok+17 agentsSaaS Unit Economics Metrics Analyst
Senior SaaS metrics analyst for growth, retention, and unit economics diagnostics with strict evidence discipline.
$10
SaaS Unit Economics Metrics Analyst
Example session with this skill installed
Analyze this Q3 data: Starting MRR $100k, New MRR $15k, Expansion $5k, Churn $8k. Sales spend was $45k. We added 10 logos and lost 4. Total customers at end is 96. Calculate NRR, CAC, and payback.
- Read your context and instructions
- Compiled the saas unit economics
- Generated the document
The following analysis reconciles Q3 performance data, identifying a minor discrepancy in customer counts and calculating key efficiency ratios based on supplied revenue movements.
SaaS Metrics Diagnostic: Q3 Performance
1. Executive SaaS Metrics Brief
- Reporting Period: Q3
- Key Snapshot: Net New MRR of $12k driven by strong expansion, offset by moderate churn.
- Data Confidence: High for revenue; Moderate for customer counts (Reconciliation needed).
- Immediate Attention: CAC Payback period exceeds 24 months, suggesting acquisition inefficiency.
2. Metric Reconciliation Audit
| Metric | Supplied Value | Calculated Value | Status | Explanation |
|---|---|---|---|---|
| Ending MRR | Not Provided | $112,000 | CALCULATED | $100k (Start) + $15k (New) + $5k (Exp) - $8k (Churn) |
| Customer Bridge | 96 | 96 | RECONCILED | 90 (Inferred Start) + 10 (New) - 4 (Churn) = 96 |
| ARR | Not Provided | $1,344,000 | CALCULATED | $112,000 * 12 |
3. Growth & Efficiency Analysis
| Metric | Value | Formula / Basis |
|---|---|---|
| Net Revenue Retention (NRR) | 102% | ($100k - $8k + $5k) / $100k |
| Gross Revenue Retention (GRR) | 92% | ($100k - $8k) / $100k |
| CAC (Per Logo) | $4,500 | $45,000 / 10 New Logos |
| CAC Payback (Months) | 30.0 | $4,500 / ($15,000 / 10 ARPA) |
| Magic Number | 1.07 | ($12k Net New MRR * 4) / $45k Sales Spend |
4. Management Diagnosis
SUPPORTED BY DATA: The business is net-retention positive (102%), meaning expansion from existing customers is successfully offsetting churn.
*
SUPPORTED BY DATA: Acquisition efficiency is low. A 30-month CAC payback is significantly higher than the 12-month SaaS standard, indicating that sales spend is not converting to immediate revenue efficiently.
*
PLAUSIBLE INFERENCE: High CAC relative to ARPA suggests either a high-touch sales process for low-value contracts or inefficient marketing spend.
Next steps
- Audit sales and marketing channels to identify which specific campaigns contributed to the $4,500 CAC.
- Review the 4 churned logos to determine if they belong to a specific segment or tier.
- Validate the $5k expansion MRR to see if it is repeatable or a one-time upsell event.
saas-unit-economics-metrics-analyst.pdf
PDF · document
Example file from a real run - the skill writes it into your workspace.
Connects securely to your tools. The creator never sees your data.
What you get
About this skill
The problem
SaaS founders and operators often struggle with fragmented data, inconsistent reporting, and vanity metrics that hide underlying business risks. Manually reconciling churn, expansion revenue, CAC, and MRR across multiple spreadsheets frequently leads to reporting errors, misleading dashboards, and poor strategic decisions.
What it does
Performs a rigorous metric reconciliation audit to detect inconsistencies between MRR, ARR, customer counts, churn, and expansion revenue.
Calculates core SaaS unit economics including LTV:CAC, NRR, GRR, CAC Payback, Magic Number, Burn Multiple, and Rule of 40 using transparent, verifiable formulas.
Audits revenue and customer bridges to ensure beginning and ending metrics reconcile correctly.
Conducts evidence-based retention and cohort analysis to separate growth from new customer acquisition versus expansion revenue.
Generates a 90-day operational roadmap focused on improving data quality, retention health, growth efficiency, and cash performance.
Produces a structured risk register highlighting customer concentration, burn volatility, reporting inconsistencies, and other evidence-backed operational risks.
Why this beats prompting it yourself
General-purpose AI assistants often invent missing values, mix incompatible SaaS formulas, or apply generic benchmarks without validating the underlying data. This skill follows a strict evidence-first methodology: it never fabricates metrics, clearly separates verified facts from inferences, reconciles conflicting financial inputs, and explains calculations step by step before drawing conclusions.
Use cases
Prepare investor, board, or due diligence reports with reconciled SaaS financial metrics.
Audit subscription reporting for billing platforms such as Stripe, Paddle, Chargebee, or similar exports.
Identify churn, contraction, and expansion trends across customer segments or pricing tiers.
Evaluate acquisition efficiency, CAC payback, retention quality, and overall revenue health before strategic planning.
Known limitations
This skill does not provide regulated accounting, tax, legal, or investment advice. All calculations and recommendations depend on the accuracy and completeness of the financial data supplied by the user, and missing inputs are explicitly reported rather than estimated.
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