Measure what matters. Ignore the rest. Scale with clarity.
North Star metric, weekly dashboards, revenue breakdowns
Centralized inbox with categorization and sentiment
Track every stage of your user journey
Hypothesis-driven A/B testing with result logging
Spot churn before it happens
| Field | Your Answer |
|---|---|
| Metric | |
| Current Value | |
| Target (30 days) | |
| Target (90 days) | |
| How it's measured | |
| Why this metric |
| Metric | This Week | Last Week | ฮ Change | Target | Status |
|---|---|---|---|---|---|
| Revenue (MRR) | ๐ข ๐ก ๐ด | ||||
| New Signups | |||||
| Active Users (WAU) | |||||
| Churn Rate | |||||
| Activation Rate | |||||
| NPS Score | |||||
| Support Tickets | |||||
| Avg. Response Time |
| Month | MRR | New MRR | Churned MRR | Net New | Customers | ARPU |
|---|---|---|---|---|---|---|
| Cohort | Week 0 | Week 1 | Week 2 | Week 4 | Week 8 | Week 12 |
|---|---|---|---|---|---|---|
| 100% | ||||||
| 100% | ||||||
| 100% | ||||||
| 100% |
If you can only track one number, track Net Revenue Retention. It tells you if your existing customers are getting more value over time.
| # | Date | User | Channel | Feedback | Category | Sentiment | Status |
|---|---|---|---|---|---|---|---|
| 1 | Feature Request | ๐ | Open | ||||
| 2 | Chat | Bug | ๐ | Open | |||
| 3 | Praise | ๐ | Open | ||||
| 4 | |||||||
| 5 | |||||||
| 6 | |||||||
| 7 | |||||||
| 8 |
| Feature | # Requests | Revenue Impact | Effort | Decision |
|---|---|---|---|---|
| Build / Defer / Decline | ||||
"[Exact quote from user]". Name, Date
Review this log weekly. Patterns emerge after 10+ entries. One complaint is an anecdote, five complaints about the same thing is a feature.
| Channel | Visitors | Signups | Conversion | CAC | Notes |
|---|---|---|---|---|---|
| Organic Search | |||||
| Twitter/X | |||||
| Product Hunt | |||||
| Referral | |||||
| Direct | |||||
| Paid Ads | |||||
| Total |
| Step | Users | Drop-off % | Notes |
|---|---|---|---|
| Signed up | - | ||
| Completed onboarding | |||
| Performed core action | |||
| Invited teammate | |||
| Activation rate | Target: 40%+ |
| Timeframe | Retention Rate | Target | Status |
|---|---|---|---|
| Day 1 | 60% | ||
| Day 7 | 30% | ||
| Day 30 | 15% | ||
| Day 90 | 10% |
| Metric | Value | Target |
|---|---|---|
| Trial โ Paid conversion | 5-10% | |
| Free โ Paid conversion | 2-5% | |
| Average Revenue Per User | ||
| Lifetime Value (LTV) | ||
| LTV:CAC Ratio | >3:1 |
| Metric | Value | Target |
|---|---|---|
| Referral rate | 10%+ | |
| Viral coefficient (K) | >0.5 | |
| Avg. referrals per user |
| ID | Name | Hypothesis | Primary Metric | Start | End | Result |
|---|---|---|---|---|---|---|
| EXP-001 | If we [change], then [metric] will [improve] because [reason] | |||||
| EXP-002 | ||||||
| EXP-003 |
| Field | Details |
|---|---|
| Hypothesis | If we [do X], then [metric Y] will [change Z] because [reason] |
| Primary Metric | |
| Secondary Metrics | |
| Traffic Split | 50/50 |
| Min. Sample Size | |
| Duration | |
| Confidence Level | 95% |
| Metric | Control | Variant | ฮ | Confidence | Winner |
|---|---|---|---|---|---|
| Priority | Experiment | Expected Impact | Effort | Status |
|---|---|---|---|---|
| 1 | Queued | |||
| 2 | ||||
| 3 | ||||
| 4 | ||||
| 5 |
Run one experiment at a time. Minimum 2 weeks per test. If you can't reach statistical significance, the difference probably doesn't matter.
| Factor | Weight | Scoring (1-10) |
|---|---|---|
| Usage Frequency | 30% | Daily=10, Weekly=7, Monthly=4, Rare=1 |
| Feature Adoption | 20% | 80%+ features=10, 50%+=7, Core only=4, Minimal=1 |
| Support Interactions | 15% | Positive=10, Neutral=5, Frequent complaints=2 |
| Growth Signal | 15% | Added seats=10, Upgraded=8, Static=5, Downgraded=2 |
| Engagement Trend | 10% | Increasing=10, Stable=6, Declining=2 |
| Payment Health | 10% | On time=10, Late once=5, Multiple failures=1 |
| Customer | Plan | MRR | Score | Health |
|---|---|---|---|---|
| /100 | ๐ข ๐ก ๐ด | |||
| /100 | ||||
| /100 | ||||
| /100 | ||||
| /100 |
Candidate for upsell, case study, referral ask.
Schedule check-in. Investigate usage drop.
Immediate outreach. Offer help or discount.
Curated stacks for every stage of building.