Prompt

Design a SaaS analytics utility that helps founders and operators monitor business performance.

The system should:
1) Define the core SaaS metrics the tool would track
   (examples: MRR, churn, CAC, LTV, retention cohorts, expansion revenue)
2) Explain how the analytics engine would project revenue growth
   using inputs such as:
   - customer acquisition rate
   - churn rate
   - pricing tiers
   - expansion revenue
3) Identify the most important business risks the system should detect early,
   such as:
   - churn spikes
   - declining activation rates
   - CAC payback deterioration
   - revenue concentration risk
4) Provide a simple Python example showing how the system could calculate
   Monthly Recurring Revenue (MRR), churn rate, and LTV from raw customer data.
5) Generate a visual concept of the SaaS analytics dashboard that founders
   would use to monitor these metrics.

Output requirements:
Produce the result using EXACTLY these sections:
A) Core SaaS Metrics Framework  
(8–10 bullets defining key SaaS metrics)
B) Revenue Projection Model  
(2–3 paragraphs explaining the model, including formula-style explanations)
C) Early Warning Risk Signals  
(exactly 6 bullets describing business risks the system should detect)
D) Product Feature Blueprint  
(exactly 5 bullets describing how the SaaS analytics utility should work)
E) Example Analytics Code  
(a short Python code snippet demonstrating MRR / churn calculations)

Formatting rules:
- Keep writing concise and operator-focused.
- Do not add extra headings.
- Do not omit any required section.

IMAGE:
A cinematic visualization of a modern SaaS analytics control room with large dashboard screens displaying revenue charts, churn graphs, cohort analysis, and KPI metrics. Blue lighting, sleek futuristic interface, glass displays, ultra detailed, photorealistic, 8K.