Bubble.io SaaS Metrics: Building a Dashboard That Tracks What Matters
Eight metrics every Bubble SaaS must track, the data model that stores them correctly, and the weekly metrics review ritual that turns numbers into decisions. Built from Stripe webhook data and Bubble’s native query capabilities.
The SaaS Numbers Worth Tracking in Your Bubble Admin Dashboard
A SaaS founder who does not track metrics makes decisions by intuition. A founder who tracks the wrong metrics makes decisions that optimise for the wrong outcomes. This guide covers exactly which metrics to track in a Bubble SaaS admin dashboard, why each one matters, and how to implement them with Bubble’s data model and Stripe webhook architecture.
Definitions and Formulas
| Metric | Formula | What It Tells You | Update Trigger |
|---|---|---|---|
| MRR | Sum of all active subscription amounts (monthly equivalent) | Revenue momentum; the foundational SaaS metric | Every Stripe webhook that changes subscription state |
| MRR Growth Rate | (This month MRR – Last month MRR) / Last month MRR x 100 | Velocity of growth; investor’s favourite single number | Calculated monthly from stored MRR snapshots |
| Churn Rate | MRR lost to cancellations / Starting MRR x 100 | Product stickiness; the most important health metric | Every subscription.deleted webhook event |
| Net Revenue Retention | (Start MRR + Expansion – Contraction – Churn) / Start MRR x 100 | Whether existing customers are spending more or less over time | Monthly calculation from stored billing events |
| Trial to Paid Rate | Paid conversions / Trial starts x 100 | Onboarding effectiveness; value proposition clarity | Calculated from Workspace subscription status changes |
| Time to First Value | Average time from signup to first core action | Onboarding speed; first impression quality | Stored on Workspace: signup time vs first action time |
| DAU / MAU Ratio | Daily Active Users / Monthly Active Users | Product stickiness; habit formation | Session tracking; login events logged per day |
| Customer Lifetime Value | Average MRR per customer / Monthly churn rate | How much each customer is worth over time | Calculated from MRR and churn rate |
The Data Architecture for Metrics
MonthlyMRRSnapshot
month_start
: date (first day of month)
mrr_total
: number
new_mrr
: number (new customers this month)
expansion_mrr
: number (upgrades)
contraction_mrr
: number (downgrades)
churn_mrr
: number (cancellations)
// Scheduled backend workflow: runs on the 1st of each month
DailyActiveUser
user
: User
date
: date (date only, not time)
workspace
: Workspace
// On every page load: check if record exists for today
// Only create if not exists (prevents duplicate counting)
signup_at
: date
first_action_at
: date (set when first core action taken)
time_to_first_value_hrs
: number (auto-calculated)
// When first_action_at is set:
time_to_first_value_hrs
= (first_action_at – signup_at) in hours
A Simple Ritual for Data-Driven Decisions
Every Monday morning, a 30-minute metrics review. Not a dashboard glance — a structured review with questions:
MRR Review
Did MRR grow or shrink vs last week? If it shrank: what churned and why? If it grew: what converted and from which acquisition source? The answer determines this week’s growth priority.
Churn Review
Did any workspaces cancel in the last 7 days? Have churn interviews been conducted for each? What was the stated reason? Are there any common patterns in the last 3 churn events?
Activation Review
What is this week’s average time-to-first-value? Is it improving or worsening? Which onboarding step is causing the most drop-off? What one change would most improve activation this week?
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