Drive Long-Term User Retention.
Don't just acquire users—keep them. Analyze retention curves, predict churn, and measure product-market fit using your complete history of warehouse data.
Retention Cohort Analysis
Weekly retention by signup month
| Cohort | Week 0 | Week 1 | Week 2 | Week 3 | Week 4 | Week 5 | Week 6 | Week 7 |
|---|---|---|---|---|---|---|---|---|
| Jan 2024 | 100% | 68% | 52% | 45% | 42% | 40% | 39% | 38% |
| Feb 2024 | 100% | 71% | 55% | 48% | 44% | 41% | 40% | - |
| Mar 2024 | 100% | 73% | 58% | 51% | 47% | 44% | - | - |
| Apr 2024 | 100% | 69% | 54% | 47% | 43% | - | - | - |
| May 2024 | 100% | 72% | 56% | 49% | - | - | - | - |
| Jun 2024 | 100% | 74% | 59% | - | - | - | - | - |
42%
Avg 30-Day Retention
+8%
vs Last Quarter
Week 3
Stabilization Point
Trusted by companies worldwide
The Technical Advantage
Purpose-built retention analytics that go beyond what traditional SaaS tools can offer.
Unlimited History
SaaS tools delete data after 12 months. Mitzu analyzes your entire warehouse history to spot multi-year retention trends.
Complex Logic Made Simple
Switch between N-Day, Unbounded, and Bracket retention logic in one click. No SQL rewriting required.
Correlation Analysis
Automatically discover which features drive loyalty. Mitzu highlights the actions that correlate with long-term retention.
Visualize Your Fit.
See if your retention curve flattens. Identify exactly when users find value and stabilize, or where they drop off forever.
- Identify the week when retention stabilizes
- Compare curves across different user cohorts
- Benchmark against industry standards
- Track improvements over product iterations
Retention Curve Analysis
Does your curve flatten? That's product-market fit.
Do New Features Actually Stick?
Compare retention curves for users who engaged with a new feature vs. those who didn't. Prove the ROI of your product updates.
- A/B test feature impact on retention
- Identify sticky features that drive loyalty
- Measure the real value of product investments
- Share feature impact reports with stakeholders
Feature Impact Comparison
Users who used Feature X vs those who didn't
Identify Drop-off Patterns.
Spot the specific behavioral patterns that precede churn. Create segments of at-risk users and sync them to marketing tools for re-engagement.
- Define custom churn risk criteria
- Build segments from retention drop-offs
- Sync at-risk users to Braze, HubSpot, or Salesforce
- Automate re-engagement campaigns
Create At-Risk Segment
From retention drop-off to re-engagement campaign
Segment Criteria
Three Steps to Retention Insights
Start analyzing retention in minutes—no complex setup required.
Select Events
Choose your starting action (e.g., Signup) and returning action (e.g., Purchase).
Set Window
Define your time window (Daily, Weekly, Monthly).
Analyze
Instantly generate the heatmap and share findings with your team.
42%
Average Retention Improvement
10+
Years of History Analyzed
<1min
Time to First Insight
Get instant answers with AI Agents
Skip the SQL. Ask questions in plain English and get instant, accurate insights directly from your data warehouse. Our AI understands your metrics and delivers actionable answers.
- Natural language queries - no SQL required
- Context-aware answers based on your data model
- Automated anomaly detection and alerts
- Follow-up questions for deeper analysis
Mitzu AI Agent
Ask anything about your data
Users who complete 3+ actions in their first session have 68% retention vs 21% for those who don't. Focus on driving early engagement.
Users from the Q2 onboarding experiment show 15% better D7 retention. The guided tour appears to be working - consider rolling it out fully.
Frequently Asked Questions
Everything you need to know about retention analytics with Mitzu.
Ready to drive long-term retention?
Stop guessing why users churn. Start measuring retention with warehouse-native precision.