You've launched your beta. Your first 100 users are in.
Now what?
Do you track everything? Wait and see? Focus on acquisition or retention?
This is the question that keeps every founder awake at night. Get it right, and you learn exactly what to build next. Get it wrong, and you waste weeks chasing the wrong metrics.
After helping 50+ startups navigate their first 100 users, I've seen what works and what doesn't. Here's your complete guide to making those first 100 users count.
Why 100 Users Matter More Than You Think
Your first 100 users aren't just a number. They're your first real signal.
What 100 Users Tell You
Statistical significance: At 100 users, patterns start emerging. You're no longer looking at random noise. The way users behave at 100 users is a strong indicator of how they'll behave at 10,000.
Behavioral diversity: With 100 users, you're likely seeing 2-4 distinct user personas. The behavior of your ideal user starts separating from the noise.
Validation moment: 100 users means people actually care enough to try your product. That's validation in itself.
The Magic of 100
Here's why 100 is the magic number:
- 10 users: Too small for patterns
- 50 users: Starting to see trends
- 100 users: Statistical significance
- 500 users: Clear patterns emerge
- 1,000+ users: Scale testing
Your goal: Get to 100 users, then analyze deeply before pushing for 500.
The 4-Phase Framework for Your First 100 Users
Phase 1: Day 1-3 — The Chaos Phase
What's happening:
- Initial excitement from launch
- Early adopters trying everything
- Technical issues likely appearing
- First feedback coming in
What to track:
| Metric | What It Tells You | Target |
|---|---|---|
| Signup completion rate | Is onboarding working? | > 60% |
| Error rate | Is it broken? | < 2% |
| Activation rate | First value delivered? | > 40% |
| Time to first action | Is it too hard to start? | < 5 minutes |
What to do:
- Fix critical bugs immediately (within hours)
- Monitor support channels constantly
- Thank early users personally
- Don't change core features yet
Phase 2: Day 4-14 — The Learning Phase
What's happening:
- Initial excitement settles
- Real usage patterns emerge
- Feature requests start coming
- Some users drop off
What to track:
| Metric | What It Tells You | Target |
|---|---|---|
| Day 7 retention | Do they come back? | > 20% |
| Feature adoption | What do they actually use? | Varies |
| NPS score | Are they happy? | > 30 |
| Support tickets per user | Is it confusing? | < 0.5 |
What to do:
- Start qualitative user interviews
- Map user journeys
- Identify friction points
- Collect testimonials from happy users
Phase 3: Day 15-30 — The Analysis Phase
What's happening:
- Clear patterns emerge
- User segments defined
- Product-market fit signals appear
- Decisions need to be made
What to analyze:
Analysis Area | Key Questions | Action |
|---|---|---|
| User Segmentation | Who are power users vs casual? | Double down on power users |
| Feature Usage | What features drive retention? | Prioritize high-impact features |
| Drop-off Points | Where do users get stuck? | Fix friction points |
| Feedback Themes | What do users repeatedly ask for? | Build most requested features |
What to do:
- Conduct 10-15 user interviews
- Create user personas
- Document learnings
- Make your first pivot decision
Phase 4: Day 31-100 — The Optimization Phase
What's happening:
- Refined understanding of users
- Product improvements shipping
- Growth strategies testing
- Scale preparation begins
What to optimize:
| Area | Goal | Target |
|---|---|---|
| Retention | Improve stickiness | Day 30 retention > 30% |
| Activation | More users get value | > 50% activation |
| NPS | Increase satisfaction | > 50 NPS |
| Organic growth | Users refer others | > 10% organic |
How to Collect Feedback from Your First 100 Users
Feedback is more valuable than any metric. Here's how to get it.
1. In-App Feedback Triggers
Best moments to ask:
- After completing a core action
- After 3-5 sessions
- Before churn (detected by inactivity)
- After reaching a milestone
What to ask:
Question Type | Example | When to Ask |
|---|---|---|
| NPS | "How likely are you to recommend us?" | After 2-3 sessions |
| Quick poll | "What's your biggest frustration?" | After task completion |
| Open feedback | "What would make this 10x better?" | After 5+ sessions or before churn |
| Rating | "Rate your experience 1-5 stars" | After key actions |
2. User Interview Strategy
Who to interview:
- 5 happy users (understand what works)
- 5 struggling users (find friction)
- 5 churned users (know why they left)
Questions to ask:
- "What problem were you trying to solve when you signed up?"
- "What's the one thing that would make you stop using us?"
- "If you had to describe us to a friend, what would you say?"
- "What feature would you add if you could?"
- "How does this compare to alternatives you've tried?"
3. Support Channel Analysis
Track support tickets by category:
Ticket Category | What It Means | Action |
|---|---|---|
| How-to questions | Documentation gaps | Improve help content |
| Bug reports | Quality issues | Fix immediately |
| Feature requests | Missing functionality | Prioritize top requests |
| Account issues | Onboarding friction | Simplify flow |
The Metrics That Actually Matter for First 100 Users
Not all metrics are created equal. Focus on these:
1. Activation Rate
Definition: Percentage of users who complete your core "aha" action.
Why it matters: If users don't activate, they churn.
How to calculate:
Activation Rate = (Users who complete core action / Total signups) × 100
Benchmarks:
- Excellent: > 60%
- Good: 40-60%
- Needs work: < 40%
2. Day 7 Retention
Definition: Percentage of users who return 7 days after signup.
Why it matters: Shows if users find ongoing value.
How to calculate:
Day 7 Retention = (Users active on day 7 / Users who signed up on day 0) × 100
Benchmarks:
- Excellent: > 40%
- Good: 25-40%
- Needs work: < 25%
3. Net Promoter Score (NPS)
Definition: User satisfaction on -100 to +100 scale.
Why it matters: Predicts growth through referrals.
How to calculate:
- Ask: "How likely are you to recommend us?" (0-10)
- Calculate: % Promoters (9-10) - % Detractors (0-6)
Benchmarks:
- Excellent: > 50
- Good: 30-50
- Needs work: < 30
4. Time to Value (TTV)
Definition: Time from signup to first "aha" moment.
Why it matters: Faster TTV = better experience.
How to calculate:
- Track timestamp of signup
- Track timestamp of first core action
- Calculate difference
Benchmarks:
- Excellent: < 3 minutes
- Good: 3-10 minutes
- Needs work: > 10 minutes
5. Organic Growth Rate
Definition: Percentage of new users coming without paid acquisition.
Why it matters: Shows if users love it enough to share.
How to calculate:
Organic Rate = (Users from organic/search/referral / Total new users) × 100
Benchmarks:
- Excellent: > 30%
- Good: 15-30%
- Needs work: < 15%
Common Mistakes with First 100 Users
Mistake #1: Chasing Vanity Metrics
Problem: Celebrating signups while users churn.
Solution: Focus on activation and retention, not total users.
Mistake #2: Collecting Feedback But Not Acting
Problem: Surveying users and doing nothing with results.
Solution: Take action on at least one piece of feedback per week.
Mistake #3: Changing Too Much Too Fast
Problem: Pivoting based on 10 users' opinions.
Solution: Wait for patterns from 50+ users before major changes.
Mistake #4: Ignoring Early Churn
Problem: Assuming early churn is "not the right users."
Solution: Interview churned users. Their feedback is gold.
Mistake #5: Not Segmenting Data
Problem: Looking at aggregate numbers.
Solution: Always segment by:
- Acquisition source
- User type
- Device
- Geography
When to Make Decisions
Decision Timeline
| Timeline | Users | Decision Type |
|---|---|---|
| Day 1-3 | 0-30 | Fix bugs only |
| Day 4-14 | 30-70 | Small improvements |
| Day 15-30 | 70-100 | Feature prioritization |
| Day 31-60 | 100+ | Pivot or persevere |
Signs You're on Track (vs. Need to Pivot)
Signs You're On Track ✅
- Activation rate > 40%
- Day 7 retention > 20%
- NPS > 30
- Users asking for features (not complaining about existence)
- Positive feedback outweighs negative 3:1
- Organic growth starting
Signs You Need to Pivot 🔄
- Activation rate < 20%
- Day 7 retention < 10%
- NPS < 0
- Most feedback is negative
- Users don't understand the value
- No organic growth after 60 days
Signs to Keep Iterating 🔧
- Activation rate 20-40%
- Day 7 retention 10-20%
- NPS 0-30
- Mixed feedback with patterns
- Some users very happy, some churned
Your First 100 Users Checklist
| Phase | Task | Status |
|---|---|---|
| Setup (Before Launch) | Set up analytics (PostHog, Mixpanel) | ☐ |
Create feedback collection system | ☐ | |
Define core activation action | ☐ | |
Set up support channel | ☐ | |
| Days 1-14 | Fix critical bugs within 24 hours | ☐ |
Thank first 10 users personally | ☐ | |
| Interview 5 users | ☐ | |
Track daily metrics | ☐ | |
| Days 15-30 | Interview 10 more users | ☐ |
Create user personas | ☐ | |
Document top 5 learnings | ☐ | |
Make pivot/persevere decision | ☐ |
The Bottom Line
Your first 100 users are your most important users. They validate (or invalidate) your assumptions. They teach you what to build. They become your first advocates or your first critics.
Key principles:
- Get to 100 users fast, then analyze deeply
- Track activation, retention, and NPS from day one
- Collect qualitative feedback from 15+ users
- Segment your data to find patterns
- Make decisions at day 30, not day 7
Your action plan:
- Set up tracking today (if not done)
- Define your activation metric
- Schedule 3 user interviews per week
- Review metrics daily for first 30 days
- Make your pivot/persevere decision at day 30
The goal isn't perfection. It's learning fast enough to build something users want.
At Startupbricks, we've helped 50+ startups navigate their first 100 users. We know the patterns to look for, the questions to ask, and when to pivot versus persevere.