SaaS Analytics Checklist

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.

Use email as the stable identifier for B2B SaaS—it persists across sessions and links to CRM data. Avoid user_id from unsanitized sources that might change.

Define 15-20 key events representing critical user actions: sign-up, feature usage, invitations. Instrument each with consistent property naming.

Use naming convention like 'feature_name.action' (e.g., 'dashboard.viewed', 'report.exported') to keep events queryable and groupable in your analytics UI.

Send subscription, upgrade, and expansion events to Amplitude or Mixpanel with MRR/tier metadata. Connect Stripe webhooks or use Segment.

Track both event and revenue amount as properties. This lets you segment cohorts by LTV and correlate feature usage with expansion revenue.

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.

Automate via Segment transformations or scheduled scripts that update user traits nightly. Stale user properties lead to misanalysis and false conclusions.

Audit event completeness, identify missing properties, and check for duplicate or orphaned events. Run SQL queries to spot anomalies.

Set up a simple dashboard that counts events per day and flags unexpected drops. A 50% event drop usually means tracking broke, not user behavior changed.

Segment users by churn date, plan tier, and company size. Compare behavior (logins, features used, support tickets) in last 30 days vs. active users.

Cohort analysis in Amplitude reveals patterns: do free-trial users churn at day 14? Do SMB customers churn 6 months after upgrade?

Measure days from signup to first use of critical feature (e.g., creating dashboard or running report). Slow adoption predicts churn.

If 30% of users don't hit key feature within 7 days, your onboarding is leaky. Use Intercom or email to prompt them; measure impact.

Interview churned customers or deploy exit surveys via Typeform. Combine quantitative churn data with qualitative 'why' to drive product fixes.

Link survey responses to user IDs. This lets you cross-check stated reasons against actual usage data to separate excuses from real issues.

Flag users whose engagement drops below threshold (e.g., <2 logins in 30 days) and compare their feature usage to power users.

Use Mixpanel or PostHog retention curves—they visually show which features correlate with long-term retention. Prioritize features used by week-4 cohorts.

Build simple churn-risk score based on login frequency, support tickets, and days since last core feature use. Flag accounts >70 for outreach.

Plug churn-risk scores into HubSpot to auto-assign accounts to CSM bucket for check-ins. Prevention is cheaper than win-back campaigns.

Segment trial users by step: sign-up → first login → key feature use → upgrade attempt → paid. Measure drop-off at each step.

If 60% convert at step 1 but only 30% at step 2, your onboarding email or welcome flow is broken. Fix that before optimizing later steps.

Measure the ratio of daily active users to monthly active users monthly. Higher ratios indicate stronger product-market fit and lower churn.

A 30% DAU/MAU ratio is healthy for B2B SaaS; below 20% signals engagement issues. Compare by plan tier—enterprise should have higher ratios.

Analyze which features and usage patterns correlate with upgrades. For example, users who invite 3+ teammates are 5x more likely to upgrade.

Run cohort analysis: segment users by early feature adoption, then track conversion rates. Use Amplitude or SQL to compute correlations.

Compare which features enterprise customers use vs. SMB customers. Enterprise usage patterns reveal high-value differentiators for positioning.

If enterprise customers use advanced reporting 10x more than SMB, make advanced reporting a tier-exclusive feature to justify higher pricing.

Measure which features, when adopted early, correlate with higher LTV and lower churn. Weight features by contribution to expansion revenue.

Use logistic regression or correlation matrix: for each feature adoption, measure impact on CAC payback and LTV. Prioritize high-impact features.

Measure how much revenue from existing customers is retained plus expansion as percentage of prior-year revenue. NRR >100% indicates healthy upsell.

Break NRR by cohort: enterprise NRR may be 110% while SMB is 85%. Focus product investments on highest-NRR cohort to compound growth.

Calculate months to recover customer acquisition cost (CAC). Measure by marketing channel and plan tier to optimize spend allocation.

If CAC payback is >18 months, your pricing or onboarding is broken. Faster payback (<12 months) means you can reinvest aggressively.

Build simple model: LTV ≈ ARPU × Retention_months. Measure which usage behaviors predict 24-month retention and optimize onboarding for them.

If users who create a team stay 2x longer, make team creation frictionless. Usage predictors of retention are your north star metrics.

Track monthly expansion revenue (upsells, add-ons, seat expansion) as percentage of base revenue. High expansion indicates product-market fit.

SaaS benchmarks show 15-30% expansion revenue for healthy companies. Below 10% signals weak upsell strategy or poor retention.

Create dashboard showing retention, NRR, and expansion revenue by signup cohort and tier. Use weekly to spot failing cohorts and adjust.

Automate in Amplitude, SQL, or BI tool. If Q1 cohort NRR drops 5 points month-over-month, investigate onboarding or support quality issues.