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Metrics Glossary

North Star Metric

Weekly Active Collaborative Workspaces

Definition: Number of unique workspaces that had at least one collaborative action (shared, invited user, or co-edited) in the past 7 days.

Why This Metric:

  • Combines engagement (weekly active) with value creation (collaboration)
  • Directly correlates with revenue (collaborative users pay more)
  • Captures network effects that drive retention
  • Actionable: teams can impact it through product changes

Target: 5,000,000 workspaces

Calculation:

SELECT COUNT(DISTINCT workspace_id)
FROM events
WHERE event_type IN ('workspace_shared', 'user_invited', 'collaborative_edit')
  AND timestamp >= CURRENT_DATE - INTERVAL '7 days';

Engagement Metrics

DAU (Daily Active Users)

Definition: Unique users who performed at least one action in the product on a given day.

Benchmark:

  • Good productivity tool: 20-40% of MAU
  • Excellent productivity tool: 40-60% of MAU

Calculation:

dau = events_df[events_df['date'] == target_date]['user_id'].nunique()

WAU (Weekly Active Users)

Definition: Unique users who performed at least one action in the past 7 days.

Calculation:

wau = events_df[
    (events_df['date'] >= target_date - timedelta(days=7)) &
    (events_df['date'] <= target_date)
]['user_id'].nunique()

MAU (Monthly Active Users)

Definition: Unique users who performed at least one action in the past 30 days.

Calculation:

mau = events_df[
    (events_df['date'] >= target_date - timedelta(days=30)) &
    (events_df['date'] <= target_date)
]['user_id'].nunique()

DAU/MAU Ratio (Stickiness)

Definition: Percentage of monthly active users who are active daily. Measures product stickiness.

Benchmark:

  • < 10%: Poor stickiness
  • 10-20%: Average
  • 20-40%: Good
  • 40%+: Excellent (Facebook ~50%, Instagram ~32%)

Calculation:

stickiness = (dau / mau) * 100

Interpretation: A 35% DAU/MAU means the average user is active 10.5 days per month.

Funnel Metrics

Awareness

Definition: Users who have been exposed to the product (visited website, saw ad, heard about it).

Measurement: In this analysis, all users represent awareness stage.

Signup Rate

Definition: Percentage of aware users who create an account.

Benchmark: 10-20% for freemium products

Calculation:

signup_rate = (total_signups / total_awareness) * 100

Activation Rate

Definition: Percentage of signed-up users who reach their "aha moment" (first value realization).

For Notion: Creating first page/block

Benchmark: 40-70% for good onboarding

Calculation:

activated_users = users_with_event_type('page_created')
activation_rate = (activated_users / total_signups) * 100

Engagement Rate

Definition: Percentage of activated users who use the product regularly (3+ times in first week).

Benchmark: 30-50% for productivity tools

Calculation:

engaged_users = users_with_3plus_events_in_first_week
engagement_rate = (engaged_users / activated_users) * 100

Collaboration Rate

Definition: Percentage of engaged users who share workspaces with others.

For Notion: This is critical as it drives retention and monetization

Benchmark: 40-60% for collaboration tools

Calculation:

collaborative_users = users_with_event_type('workspace_shared')
collaboration_rate = (collaborative_users / engaged_users) * 100

Monetization Rate

Definition: Percentage of collaborative users who convert to paid plans.

Benchmark:

  • Freemium SaaS: 2-5% overall, 20-40% of power users
  • Collaboration tools: 15-30% of teams

Calculation:

paid_users = users_with_plan_type('paid')
monetization_rate = (paid_users / collaborative_users) * 100

Retention Metrics

Day-N Retention

Definition: Percentage of users from a cohort who return on day N after signup.

Key Days: Day 1, 7, 14, 30, 60, 90

Calculation:

cohort_users = users_signed_up_on_date
returned_on_day_n = users_active_on_date + timedelta(days=n)
retention = (returned_on_day_n / cohort_users) * 100

Classic Retention

Definition: Percentage of users from a cohort who are active in period N.

Calculation:

# Month 3 retention
cohort_size = users_signed_up_in_month_0
active_in_month_3 = users_from_cohort_active_in_month_3
retention = (active_in_month_3 / cohort_size) * 100

Rolling Retention

Definition: Percentage of users active in period N or any period after N.

Use Case: Better for products with irregular usage patterns

Growth Metrics

User Growth Rate

Definition: Month-over-month percentage increase in total users.

Calculation:

growth_rate = ((users_this_month - users_last_month) / users_last_month) * 100

Viral Coefficient (K-factor)

Definition: Average number of new users each user brings to the product.

Calculation:

k_factor = (invites_sent / total_users) * (signups_from_invites / invites_sent)

Benchmark:

  • K > 1: Viral growth
  • K = 0.5-1: Strong word-of-mouth
  • K < 0.5: Paid acquisition needed

Quick Ratio

Definition: (New MRR + Expansion MRR) / (Churned MRR + Contraction MRR)

Benchmark:

  • < 1: Shrinking
  • 1-4: Growing sustainably
  • 4+: Hypergrowth

Monetization Metrics

ARPU (Average Revenue Per User)

Definition: Average revenue generated per user (all users, including free).

Calculation:

arpu = total_revenue / total_users

For Notion (estimated): $3.20/user/month

ARPPU (Average Revenue Per Paying User)

Definition: Average revenue generated per paying user only.

Calculation:

arppu = total_revenue / paying_users

For Notion (estimated): $8/user/month

LTV (Lifetime Value)

Definition: Total revenue expected from a user over their entire relationship.

Calculation:

ltv = arppu * average_lifetime_months
# Or
ltv = arppu * (1 / monthly_churn_rate)

Benchmark: LTV should be 3x CAC minimum

CAC (Customer Acquisition Cost)

Definition: Average cost to acquire one paying customer.

Calculation:

cac = (sales_costs + marketing_costs) / new_customers_acquired

CAC Payback Period

Definition: Months required to recover customer acquisition cost.

Calculation:

payback_months = cac / (arppu - cogs_per_user)

Benchmark:

  • < 12 months: Excellent
  • 12-18 months: Good
  • 18-24 months: Acceptable
  • 24+ months: Concerning

Net Revenue Retention (NRR)

Definition: Revenue retained from existing customers, including expansions.

Calculation:

nrr = ((starting_mrr + expansion - churn - contraction) / starting_mrr) * 100

Benchmark:

  • 100%: Breaking even
  • 100-110%: Good
  • 110-120%: Great
  • 120%+: World-class (best SaaS companies)

Behavioral Metrics

Time to Activation

Definition: Time from signup to first value realization (aha moment).

Benchmark:

  • < 5 minutes: Excellent
  • 5-30 minutes: Good
  • 30-60 minutes: Needs improvement
  • 60+ minutes: Poor

Impact: Users who activate within 24 hours have 2x better retention.

Feature Adoption Rate

Definition: Percentage of users who have used a specific feature.

Calculation:

adoption_rate = (users_who_used_feature / total_active_users) * 100

Feature Depth (Engagement)

Definition: Average usage frequency among users who adopted the feature.

Calculation:

depth = total_feature_uses / users_who_used_feature

Power User Ratio

Definition: Percentage of users in the top engagement tier.

Calculation: Top 10% of users by activity level

Insight: Power users often drive 50%+ of product usage

Cohort Metrics

Cohort Size

Definition: Number of users who signed up in a specific time period.

Cohort Retention Curve

Definition: Retention rate plotted over time for a specific cohort.

Shape Analysis:

  • Steep initial drop → Activation problem
  • Gradual decline → Engagement problem
  • Flattening curve → Found product-market fit

Cohort LTV

Definition: Average lifetime value of users from a specific cohort.

Use Case: Compare cohort quality by acquisition channel or time period.

Product Health Metrics

NPS (Net Promoter Score)

Definition: Likelihood users would recommend product (0-10 scale).

Calculation:

promoters = responses >= 9
detractors = responses <= 6
nps = (promoters% - detractors%) * 100

Benchmark:

  • < 0: Poor
  • 0-30: Good
  • 30-70: Great
  • 70+: World-class

CSAT (Customer Satisfaction Score)

Definition: Direct satisfaction rating (1-5 or 1-10 scale).

Active Users / Total Users Ratio

Definition: Percentage of all-time users still active.

Benchmark:

  • < 20%: High churn
  • 20-40%: Normal
  • 40-60%: Good retention
  • 60%+: Excellent

Usage Patterns

Session Duration

Definition: Average time user spends per session.

For Notion (estimated): 12 minutes/session

Session Frequency

Definition: Average sessions per user per week.

For Notion (estimated): 4 sessions/week

Core Action Frequency

Definition: How often users perform the key value action.

For Notion: Creating/editing content

Calculation Examples

Example 1: Calculate DAU/MAU

import pandas as pd

# Given
dau_on_date = 3_500_000
mau_on_date = 10_000_000

# Calculate
stickiness = (dau_on_date / mau_on_date) * 100
# Result: 35%

# Interpretation
days_active_per_month = stickiness / 100 * 30
# Result: 10.5 days/month average

Example 2: Calculate LTV

# Given
arppu = 96  # $8/month * 12 months
average_customer_lifetime_months = 24

# Calculate
ltv = arppu * average_customer_lifetime_months
# Result: $2,304

# Or using churn
monthly_churn_rate = 0.05  # 5% per month
ltv_alternative = arppu / monthly_churn_rate
# Result: $1,920

Example 3: Calculate Funnel Conversion

# Given funnel data
awareness = 1_000_000
signups = 150_000
activated = 90_000
engaged = 40_500
collaborative = 20_250
paid = 5_063

# Calculate stage conversions
signup_rate = (signups / awareness) * 100  # 15%
activation_rate = (activated / signups) * 100  # 60%
engagement_rate = (engaged / activated) * 100  # 45%
collaboration_rate = (collaborative / engaged) * 100  # 50%
monetization_rate = (paid / collaborative) * 100  # 25%

# Overall conversion
overall = (paid / awareness) * 100  # 0.51%

References

  • Amplitude: North Star Framework
  • Mixpanel: Retention Best Practices
  • Reforge: Growth Metrics
  • Lenny Rachitsky: Product-Market Fit Metrics
  • a16z: Consumer Metrics
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