SaaS Analytics Checklist
Critical checklist for SaaS teams to set up product analytics, track churn drivers, measure conversion funnels, and connect usage data to revenue impact.
Analytics Foundation Setup
Establish baseline event tracking and user identification to power all downstream analysis. Without clean foundational data, downstream metrics are unreliable.
Implement consistent user identification
Ensure every user has a unique ID tracked across web, mobile, and backend. Use Segment or raw API calls to your analytics tool to enforce this.
Map and instrument core feature events
Define 15-20 key events representing critical user actions: sign-up, feature usage, invitations. Instrument each with consistent property naming.
Set up revenue event tracking
Send subscription, upgrade, and expansion events to Amplitude or Mixpanel with MRR/tier metadata. Connect Stripe webhooks or use Segment.
Create user property enrichment pipeline
Enrich user profiles with company size, plan tier, signup date, and NPS. Pull from Stripe, HubSpot, or a custom backend lookup to add business context.
Validate data quality monthly
Audit event completeness, identify missing properties, and check for duplicate or orphaned events. Run SQL queries to spot anomalies.
Churn & Retention Tracking
Identify why users leave and which cohorts are at risk. Poor onboarding and feature adoption are top churn drivers for B2B SaaS.
Define churned user cohorts
Segment users by churn date, plan tier, and company size. Compare behavior (logins, features used, support tickets) in last 30 days vs. active users.
Track time-to-first-key-feature
Measure days from signup to first use of critical feature (e.g., creating dashboard or running report). Slow adoption predicts churn.
Analyze exit survey and win/loss data
Interview churned customers or deploy exit surveys via Typeform. Combine quantitative churn data with qualitative 'why' to drive product fixes.
Monitor feature adoption among at-risk cohorts
Flag users whose engagement drops below threshold (e.g., <2 logins in 30 days) and compare their feature usage to power users.
Predict churn risk with cohort analysis
Build simple churn-risk score based on login frequency, support tickets, and days since last core feature use. Flag accounts >70 for outreach.
Conversion & Growth Metrics
Optimize trial-to-paid conversion and measure feature adoption that drives expansion revenue. These KPIs are most correlated with growth.
Build trial conversion funnel
Segment trial users by step: sign-up → first login → key feature use → upgrade attempt → paid. Measure drop-off at each step.
Track DAU/MAU ratio and engagement trends
Measure the ratio of daily active users to monthly active users monthly. Higher ratios indicate stronger product-market fit and lower churn.
Identify expansion-revenue drivers
Analyze which features and usage patterns correlate with upgrades. For example, users who invite 3+ teammates are 5x more likely to upgrade.
Measure feature adoption by plan tier
Compare which features enterprise customers use vs. SMB customers. Enterprise usage patterns reveal high-value differentiators for positioning.
Build feature-to-revenue attribution model
Measure which features, when adopted early, correlate with higher LTV and lower churn. Weight features by contribution to expansion revenue.
Revenue Impact & Health Metrics
Connect product usage to MRR, NRR, and CAC payback. Align product decisions with revenue growth and demonstrate analytics ROI to finance.
Calculate and track Net Revenue Retention (NRR)
Measure how much revenue from existing customers is retained plus expansion as percentage of prior-year revenue. NRR >100% indicates healthy upsell.
Track CAC payback period by acquisition channel
Calculate months to recover customer acquisition cost (CAC). Measure by marketing channel and plan tier to optimize spend allocation.
Correlate usage metrics with LTV and retention
Build simple model: LTV ≈ ARPU × Retention_months. Measure which usage behaviors predict 24-month retention and optimize onboarding for them.
Monitor expansion revenue per existing customer
Track monthly expansion revenue (upsells, add-ons, seat expansion) as percentage of base revenue. High expansion indicates product-market fit.
Build cohort health dashboard tied to revenue goals
Create dashboard showing retention, NRR, and expansion revenue by signup cohort and tier. Use weekly to spot failing cohorts and adjust.
Key Takeaway
Strong product analytics reveal why users leave, which features drive expansion, and whether your unit economics are sustainable. Instrument these four areas to become data-driven and predictable.