Product metrics

Product Metrics

North Star Metric Framework

What Makes a Good North Star

1. Captures value delivered to customers (not just revenue)
2. Predictive of long-term business success
3. Actionable — teams can influence it
4. Understandable — whole company can explain it
5. Measurable — available in near real-time

North Star Examples by Business Model

Business Model	North Star Metric
B2B SaaS	Weekly active teams
Consumer social	DAU/MAU ratio
Marketplace	GMV (gross merchandise value)
Media/content	Total reading time per week
E-commerce	Orders per customer per year
Developer tools	Weekly active developers running builds
Collaboration	Files shared per user per week
Fintech	Transactions processed per active user

Metric Tree (Input Metrics)

North Star: Weekly Active Teams

├── Acquisition
│   ├── New signups / week
│   ├── Trial activation rate (signup → first value)
│   └── Traffic × Conversion rate

├── Activation
│   ├── Time to first key action (< 3 days target)
│   ├── Onboarding completion rate (target: 70%)
│   └── Setup step completion rates (funnel)

├── Engagement (core action frequency)
│   ├── DAU / WAU / MAU
│   ├── Sessions per user per week
│   ├── Features used per session
│   └── Core action depth (# items created, shared, etc.)

├── Retention
│   ├── Day 1 / Day 7 / Day 30 retention
│   ├── Monthly churn rate
│   └── L30 (users active in last 30 days)

└── Expansion
    ├── Seats added per account per month
    ├── Upgrade rate (free → paid)
    └── NRR (Net Revenue Retention)

Retention Metrics

Cohort Retention Curve

Cohort: Users who signed up in January

         Day 0  Day 1  Day 7  Day 14  Day 30  Day 90
Jan cohort: 100%  40%   25%    20%     18%     15%

"Flattening" = product-market fit signal
Continuous decline = no PMF

Retention Formulas

Day N Retention = Users from cohort active on Day N / Cohort size

Rolling Retention = Users from cohort active on Day N or later / Cohort size

Churn Rate (monthly) = Customers lost in period / Customers at start of period

Retention Rate = 1 - Churn Rate

Quick Ratio = (New MRR + Expansion MRR) / (Contraction MRR + Churn MRR)
  > 4 = excellent growth
  2-4 = healthy
  < 2 = leaky bucket

Retention Benchmarks by Stage

Stage	Day 1	Day 7	Day 30
Early PMF (B2C)	25-35%	12-20%	8-15%
Strong PMF (B2C)	35-50%	20-35%	15-25%
B2B SaaS monthly	—	—	90-95%

Engagement Metrics

DAU/MAU Ratio (Stickiness)

Stickiness = DAU / MAU

> 20% = good (Slack ~50%, Facebook ~65%)
10-20% = moderate
< 10% = low engagement / wrong use case

Use WAU/MAU for weekly-use products

L-Ness Framework (Active Users)

L7 = users active in last 7 days
L14 = users active in last 14 days
L28 = users active in last 28 days

L7/L28 = stickiness equivalent

Feature Adoption Metrics

Feature Adoption Rate = Users who used feature / Total active users

Feature Frequency = Avg times feature used per user per period

Feature Breadth = Avg # of features used per active user (depth of product)

Revenue Metrics (SaaS)

Core SaaS Metrics

MRR = Monthly Recurring Revenue (sum of all monthly subscriptions)
ARR = MRR × 12 (or sum of annual contracts)

New MRR = revenue from new customers this month
Expansion MRR = upgrades, upsells, additional seats
Contraction MRR = downgrades
Churned MRR = cancelled subscriptions

Net New MRR = New MRR + Expansion MRR - Contraction MRR - Churned MRR

NRR (Net Revenue Retention) = 
  (MRR from cohort at end of period) / (MRR from same cohort at start)
  > 120% = world-class (customers expand faster than they churn)
  > 100% = growth without new customers
  < 100% = leaky bucket

GRR (Gross Revenue Retention) = NRR without expansion
  Healthy: > 85% SMB, > 90% enterprise

Unit Economics

CAC (Customer Acquisition Cost) = 
  Total Sales & Marketing spend / New customers acquired

LTV (Lifetime Value) = 
  ARPU × Gross Margin % / Churn Rate
  or: ARPU × Average customer lifespan in months

LTV:CAC Ratio:
  > 3:1 = healthy (< 3 years payback)
  < 1:1 = losing money per customer

CAC Payback Period = CAC / (ARPU × Gross Margin %)
  < 12 months = excellent
  12-18 months = good
  > 24 months = concerning

ARPU (Average Revenue Per User) = MRR / # active paying customers
ARPA (Average Revenue Per Account) = MRR / # accounts

Acquisition Metrics

Traffic → Sign-up Conversion Rate (target: 2-5% for B2B SaaS)
Sign-up → Activation Rate (target: 40-60%)
Activation → Paid Conversion Rate (target: 15-25% for PLG)
Trial → Paid Conversion Rate (target: 25% for freemium, 15% trial)

Lead Velocity Rate (LVR) = 
  (Qualified leads this month - last month) / last month × 100
  Leading indicator of future revenue growth

Dashboard Design Principles

Metric Hierarchy

Executive Dashboard (weekly)
├── North Star (vs goal)
├── MRR/ARR (vs goal)
├── Churn Rate (vs target)
└── CAC Payback Period

Team Dashboard (daily)
├── DAU / WAU (vs 7-day avg)
├── Activation rate (rolling 7d)
├── Feature-specific metrics
└── Error rates / P0 issues

Experiment Dashboard (real-time)
├── Treatment vs control metric
├── Statistical significance
└── Guardrail metrics (not regressions)

Anti-Patterns

• Vanity metrics: total sign-ups without activation context
• Lagging-only dashboards: MRR is lagging, track leading indicators
• Too many metrics: max 5-7 metrics per dashboard
• No targets: every metric needs a goal and time horizon
• Missing context: always show trend + vs period / vs goal

Metric Governance

• Each metric has a single owner
• Metric definitions documented (exact SQL/event name)
• Calculation methodology agreed cross-functionally
• Refresh cadence defined (real-time, daily, weekly)
• Metric changes require changelog entry
• Discrepancies between tools investigated and resolved