AARRR Dashboard: Metric Definitions, Thresholds, and Investigation Playbooks

Part 1: Metric Definitions by Stage

Acquisition Metrics

1. Signup Conversion Rate

Definition: (Total signups) / (Total landing page visitors) for a specified time period Calculation: Divide new signups by traffic to core landing page or website Data source: Google Analytics, Mixpanel, Amplitude Typical benchmark:

• Cold traffic (ads): 1-3%
• Organic traffic: 3-8%
• Enterprise with sales outreach: 50-80%

How to track it:

Weekly signup conversion rate = New signups this week / Visitors this week
Track separately by source: Direct, Organic Search, Paid Ads, Partnership, Sales-generated

2. Signup-to-Trial Start Rate

Definition: (Users who start a trial) / (Users who signed up for account) Calculation: Some signups create an account but don't start a trial. Track how many actually enter the trial. Data source: Your auth system + trial management system Typical benchmark: 70-90% (Most signups start trial, some abandon)

3. Trial Start to Trial Conversion Rate

Definition: (Trial users who convert to paid) / (Trial users who started trial) Calculation: Of users who began a trial, what percentage become paying customers within trial window? Data source: Your auth system + Stripe/payment processor Typical benchmark: 5-15% depending on trial length and product

4. Customer Acquisition Cost (CAC)

Definition: (Total acquisition spending) / (Customers acquired) Calculation: All marketing spend + sales salaries (if applicable) / Number of new customers acquired Data source: Stripe + marketing analytics + accounting system Typical benchmark:

• Self-serve SaaS: CAC $50-500 per customer
• Mid-market: CAC $2k-10k per customer
• Enterprise: CAC $25k-100k+ per customer

5. Lead Quality Score

Definition: Percentage of signups from high-quality sources (company size, vertical, intent) Calculation: (Signups from ideal customer profile) / (Total signups) Data source: Manually score signups or use IP lookup + survey data Typical benchmark: 40-60% of signups are from ICP

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Activation Metrics

1. Activation Rate (Primary)

Definition: Percentage of signups who reach "aha moment" within N days (typically 7) Calculation: (Users who completed aha moment in first 7 days) / (Total signups in period) Data source: Mixpanel, Amplitude, or custom event tracking Typical benchmark:

• Most SaaS: 20-40%
• High-intent (sales-generated): 60-80%
• Freemium with low intent: 10-25%

Defining "aha moment" (examples):

• Project management: Created first project
• Analytics: Created first report or dashboard
• Communication: Sent first message
• Workflow automation: Created first automation or workflow
• Billing: Created first invoice

2. Time to Activation

Definition: Median time from signup to aha moment Calculation: For all users who activated, calculate days from signup date to aha moment date. Report the median. Data source: Mixpanel, Amplitude Typical benchmark: 20 minutes to 48 hours depending on product

Track separately:

• Mobile vs. Desktop
• Traffic source (does referral convert faster than ads?)
• Account type (free plan vs. trial vs. enterprise)

3. Onboarding Completion Rate

Definition: Percentage of signups who complete your structured onboarding Calculation: (Users who finished onboarding) / (Users who started onboarding) Data source: Mixpanel, custom event tracking Typical benchmark: 60-80%

Note: Onboarding completion ≠ activation. You can complete onboarding and never use the product. Activation is what matters.

4. Feature Discovery Rate

Definition: Percentage of new users who discover/use key features within first N days Calculation: (Users who used feature X) / (Total activated users) within first 7 days Data source: Mixpanel feature tracking Typical benchmark: 40-70% depending on feature importance

5. Activation by Cohort

Definition: Activation rate broken down by signup source, device, or account type Calculation: Same as activation rate, but segmented Data source: Mixpanel, Amplitude Why track it: Tells you if acquisition source quality is changing

Example:

Organic search signups: 45% activation rate
Paid ads signups: 28% activation rate
Referral signups: 52% activation rate

→ If paid ads activation drops from 28% to 18%, investigate the ad platform or messaging

6. Activation-to-Retention Correlation

Definition: Of users who activated, what % return on day 2/7/30? Calculation: (Activated users still active on day N) / (Total activated users) Data source: Mixpanel, Amplitude Why track it: Tells you if aha moment was real or false positive

Example:

Activation rate: 35%
Day 7 retention of activated users: 62%
→ Of users who activated, 62% are still using it a week later. Good signal.

vs.

Activation rate: 35%
Day 7 retention of activated users: 28%
→ Users activate but immediately churn. Aha moment is weak or false.

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Retention Metrics

1. N-Day Retention (Primary)

Definition: Percentage of users active on day N who were active on day 1 Calculation: (Users active on day N) / (Users active on day 1 in cohort) Data source: Mixpanel, Amplitude Standard benchmarks:

• 1-day retention: 40-60% (expect dropoff from first day)
• 7-day retention: 30-50%
• 30-day retention: 20-40%
• 90-day retention: 15-30%

Most important: 7-day and 30-day retention. Use 7-day as a leading indicator and 30-day as your primary health metric.

2. Churn Rate

Definition: Percentage of users active in period N who are not active in period N+1 Calculation: (Users active in month N) - (Users active in month N and month N+1) / (Users active in month N) Data source: Mixpanel, Amplitude Typical benchmark:

• Daily churn: 3-8% per day
• Monthly churn: 5-15% per month
• For B2B: Should stabilize around 3-5% monthly after first 3 months

3. Cohort Retention Curve

Definition: Track retention for each weekly/monthly cohort over time Calculation: Create a table where rows are signup cohorts and columns are days/weeks after signup Data source: Mixpanel, Amplitude Why track it: Tells you if retention is getting worse (newer cohorts retain worse) or better

Example:

        Day 7   Day 30  Day 90
Jan 1:  45%     28%     15%
Jan 8:  47%     29%     16%
Jan 15: 42%     25%     12% ← Worse retention for newer cohort
Jan 22: 39%     22%     10% ← Getting worse

→ Either something changed in your onboarding/product, or acquisition quality changed

4. Weekly Active Users (WAU)

Definition: Number of distinct users active in a week Calculation: Count unique users with at least one session in the week Data source: Mixpanel, Amplitude Why track it: Tells you if returning users are actually coming back regularly

Track alongside DAU:

• DAU: Daily Active Users
• WAU: Weekly Active Users
• WAU/DAU ratio should be 3-5x for a healthy product

5. Session Frequency

Definition: Average number of sessions per active user per week Calculation: (Total sessions) / (Active users) / 7 days Data source: Analytics tool Typical benchmark:

• Casual use product: 1-2 sessions/week
• Regular use product: 3-5 sessions/week
• Daily habit product: 6-7 sessions/week

6. Feature Retention by Feature

Definition: Retention rate for users of specific features Calculation: (Users who used feature X and returned 30 days later) / (Users who used feature X) Data source: Mixpanel feature tracking Why track it: Tells you which features drive habit loops

Example:

Feature A retention: 52% (sticks with product)
Feature B retention: 28% (weak habit)
Feature C retention: 67% (strong habit)

→ Invest in promoting/expanding Feature C. Fix or deprecate Feature B.

7. Segment-Specific Retention

Definition: Retention broken down by customer segment Calculation: Same calculation, segmented by plan type, company size, geography, etc. Data source: Mixpanel, Amplitude Why track it: Tells you if a segment is churning unexpectedly

Example:

All users: 48% 30-day retention
Free tier users: 32% retention
Pro tier users: 65% retention
Enterprise users: 78% retention

→ Free tier retention is low. Consider changing onboarding or adding limitations to drive upgrade

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Revenue Metrics

1. Payable Activation Rate

Definition: Percentage of users who convert to paid within a specified window (typically 30 days) Calculation: (Users who start paying subscription) / (Users who activated) Data source: Stripe + analytics integration Typical benchmark: 5-15% depending on trial length and freemium conversion

2. Trial-to-Paid Conversion

Definition: Percentage of trial users who convert to paying customer Calculation: (Trial users who convert to paid) / (Trial users who started trial) Data source: Auth system + Stripe Typical benchmark:

• 7-day trial: 3-5%
• 14-day trial: 5-10%
• 30-day trial: 8-15%

3. Average Revenue Per Activated User (ARAU or ARPU)

Definition: Total revenue from activated users / Number of activated users Calculation: Track revenue monthly and divide by number of users who activated in that month Data source: Stripe + analytics Typical benchmark: Varies wildly by product, but track the trend

4. Free-to-Paid Conversion (Freemium)

Definition: Percentage of free users who upgrade to paid plan Calculation: (Free users who upgraded) / (Total free users in cohort) Data source: Your app + Stripe Typical benchmark: 1-5% for freemium products

5. Revenue by Cohort

Definition: Total lifetime revenue from users acquired in a specific week/month Calculation: Sum of all revenue from cohort / Number of users in cohort Data source: Stripe + cohort tracking Why track it: Tells you if conversion is improving over time and if acquisition quality is stable

Example:

Jan 1 cohort: $850 per user LTV
Jan 8 cohort: $920 per user LTV
Jan 15 cohort: $740 per user LTV ← Revenue declining for newer cohorts
Jan 22 cohort: $620 per user LTV ← Getting worse

→ Either onboarding is getting worse (users not activating), conversion is declining, or you're attracting lower-intent users

6. Expansion Revenue Rate

Definition: Monthly recurring revenue growth from existing customers (upsells + cross-sells) Calculation: (MRR from existing customers in month N) - (MRR from existing customers in month N-1) / (Total MRR month N-1) Data source: Stripe + analytics Typical benchmark: 3-10% month-over-month

7. Net Revenue Retention (NRR)

Definition: Revenue from existing customers, including expansion and minus churn Calculation: (Revenue from existing customers + expansion) - (Churned revenue) / (Starting revenue) Data source: Stripe + accounting Typical benchmark:

• Healthy: 90-100% (losing customers but growing in others)
• Great: 100-120% (expanding faster than churning)
• Exceptional: 120%+

8. ARPU Growth

Definition: Average revenue per user trend month over month Calculation: Track ARPU every month. % growth = (ARPU this month - ARPU last month) / ARPU last month Data source: Stripe Typical benchmark: 1-3% month-over-month growth

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Referral Metrics

1. Net Promoter Score (NPS)

Definition: "How likely are you to recommend us to a colleague?" (0-10 scale)

• Promoters: 9-10
• Passives: 7-8
• Detractors: 0-6 Calculation: (% Promoters) - (% Detractors) Data source: Intercom, SurveyMonkey, or in-app survey Typical benchmark:
  • Below 0: Poor (many detractors)
  • 0-20: Good
  • 20-50: Great
  • 50+: Exceptional

2. Referral Participation Rate

Definition: Percentage of customers who actually use your referral feature Calculation: (Users who clicked referral link or shared) / (Total users) Data source: Your referral tracking Typical benchmark: 5-15%

3. Referred Customer Activation Rate

Definition: Activation rate for users referred by existing customers Calculation: (Referred users who activated) / (Total referred users) Data source: UTM tracking + analytics Typical benchmark: Should be 20-40% higher than organic (referred users are warmer)

4. Referral Conversion Rate

Definition: Percentage of referred users who convert to paying customers Calculation: (Referred users who pay) / (Total referred users) Data source: UTM tracking + Stripe Why track it: Referred users are warmer leads, should convert better

5. Viral Coefficient

Definition: Average number of new users acquired per existing user Calculation: (New users acquired from referrals) / (Existing users) Data source: Referral tracking + signup attribution Typical benchmark:

• Under 0.5: Not viral (need paid acquisition)
• 0.5-1.0: Slowly growing virally
• Over 1.0: Exponentially growing (each user brings more than 1 new user)

6. NPS by Segment

Definition: NPS broken down by customer type Calculation: Same NPS calculation, segmented by plan, company size, tenure, etc. Data source: Survey data + CRM Why track it: Tells you which customer segments are most/least satisfied

Example:

Enterprise: +58 NPS (very satisfied)
Mid-market: +22 NPS (satisfied)
SMB: -8 NPS (dissatisfied)

→ SMB customers are unhappy. Investigate why. Is product not right-fit? Is support lacking?

7. Detractor Themes

Definition: Top reasons customers give for poor experience (from NPS detractors) Calculation: Manually categorize detractor feedback Data source: NPS survey open-ended responses Why track it: Tells you what to fix

Example:

Top detractor themes:
1. "Missing feature X" (28% of detractors)
2. "Performance is slow" (22%)
3. "Support is unresponsive" (18%)
4. "Too expensive" (12%)

→ Prioritize feature X and performance improvements

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Part 2: Threshold Recommendations by Stage

Acquisition Thresholds

Metric	Alert Level	Action
Signup conversion rate	Drops below 80% of baseline	Check: Did landing page change? Did traffic source mix change? Did product update affect signup?
Signup-to-trial rate	Drops below 85% of baseline	Check: Are users having auth issues? Is there friction in creating account?
CAC	Increases over 120% of baseline	Check: Are you spending more for same customers? Should you pause paid channels?
Lead quality	Drops below 75% of baseline	Check: Did you change targeting? Are you capturing lower-ICP leads?

Activation Thresholds

Metric	Alert Level	Action
Activation rate	Drops below 85% of baseline	Check: Did onboarding change? Is aha moment harder to reach? Did product regression? Interview non-activators.
Time to activation	Increases over 110% of baseline	Check: Did you add required steps to onboarding? Is something broken in signup flow?
Onboarding completion	Drops below 90% of baseline	Check: Where are users dropping off in onboarding? Is it a specific step?
Activation-to-retention correlation	Drops below 85% of baseline	Check: Is aha moment too weak? Do activated users actually find value?

Retention Thresholds

Metric	Alert Level	Action
7-day retention	Drops below 85% of baseline	Check: Is there day-1-to-day-2 cliff? Did something break for day-2 use case?
30-day retention	Drops below 90% of baseline	Check: Are existing cohorts churning or only new ones? Did you ship a regression?
Cohort retention curve	New cohorts dropping 10%+ vs. previous month	Check: Did onboarding or product quality change? Is acquisition quality worse?
Monthly churn rate	Increases above 110% of baseline	Check: Which segment is churning? Is it a feature break or natural cycling?
Session frequency	Drops below 90% of baseline	Check: Did you deprecate a feature? Is there a regression?

Revenue Thresholds

Metric	Alert Level	Action
Payable activation rate	Drops below 85% of baseline	Check: Did pricing change? Did trial length change? Did you add friction to conversion?
Trial-to-paid conversion	Drops below 90% of baseline	Check: Is trial messaging clear? Are users reaching aha moment?
ARPU	Drops below 95% of baseline	Check: Are users choosing lower-tier plans? Did you add a lower-priced option?
Revenue by cohort	New cohort revenue 15%+ lower than previous month	Check: Are activated users smaller-spend accounts? Are conversion rates dropping?
Expansion revenue	Drops below 90% of baseline or doesn't grow 2%+ monthly	Check: Are power users maxed out? Did you fail to launch expansion features?
NRR	Drops below 95% of baseline	Check: Churn increasing? Expansion declining? Both?

Referral Thresholds

Metric	Alert Level	Action
NPS	Drops below 85% of baseline	Check: What changed? Survey detractors. Did you ship a regression?
Referral participation	Drops below 90% of baseline	Check: Did you change referral mechanics? Is referral feature broken?
Referred customer activation	Drops below 85% of baseline	Check: Are referrals lower-quality? Is the referred experience bad?

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Part 3: Investigation Playbook Template

When you see a metric drop, use this playbook to diagnose quickly:

Diagnostic Steps (In Order)

1. Confirm the signal is real

   • Is this drop above noise threshold? (2-3 day trend, not one data point)
   • Is this a data quality issue? (Check if tracking code deployed correctly)
   • Is this seasonal? (Compare to same time last year)

2. Isolate the affected segment

   • Which source? (Acquisition, organic, referral, sales?)
   • Which device? (Mobile, desktop, tablet?)
   • Which geography? (US, EU, Asia?)
   • Which customer segment? (Free, paid, enterprise?)
   • If all segments affected equally, it's a product issue. If one segment affected, it's targeted.

3. Map the change to recent events

   • Did you ship code in the last 3 days? (Check deploy history)
   • Did you change messaging or positioning?
   • Did you change pricing or trial terms?
   • Did competitors launch something?
   • Did you send an email that got negative response?
   • Did you change targeting or traffic sources?

4. Create hypothesis

   • Based on isolated segment and recent changes, form 1-3 hypotheses
   • Example: "Activation rate dropped 6% because we added required field to signup form and users are abandoning before completion"

5. Test hypothesis quickly

   • Look for correlating signals
   • Example: If hypothesis is "signup form broke," check: (1) Did signup-to-trial rate drop? (2) Did time to signup increase? (3) Did mobile signups specifically drop?

6. Make decision

   • If hypothesis confirmed and issue is small: Monitor and fix
   • If hypothesis confirmed and issue is critical: Roll back change immediately
   • If hypothesis not confirmed: Move to next hypothesis

7. Document and act

   • Record what happened, why, and what you changed
   • Set reminder to re-check metric in 3-5 days to confirm fix worked

Specific Investigation Templates by Metric

Activation Rate Drops

Activation rate: 38% → 32% (down 6 points)

CONFIRM:
□ Is this a 2-3 day trend or just one day? (Check chart)
□ Affected cohorts: Last 7 days signups only / All cohorts equally

ISOLATE:
□ Did any traffic source drop? (Breakdown by source)
  - Organic: __ %
  - Paid ads: __ %
  - Referral: __ %
□ Did device type change?
  - Mobile: __ % vs. baseline __ % (Change: __ %)
  - Desktop: __ % vs. baseline __ % (Change: __ %)
□ Did account type change?
  - Free trial: __ % vs. baseline __ %
  - Sales-provided trial: __ % vs. baseline __ %

HYPOTHESES:
□ Onboarding changed or broke (check deploy log)
□ Aha moment definition changed (did you update product?)
□ Traffic source quality decreased (did you launch new ad campaign?)
□ Offer/messaging changed (did copywriting change?)
□ Tracking broken (is activation event still firing correctly?)

TEST:
□ If onboarding hypothesis: Check which step users drop off in. Interview 5 non-activated users.
□ If traffic quality hypothesis: Check ICP score of recent signups. Did new traffic source bring lower-intent users?
□ If tracking hypothesis: Manually test onboarding flow end-to-end. Is activation event triggering?

ACTION:
□ If tracking is broken: Fix immediately, retest
□ If onboarding broke: Roll back or fix regression
□ If traffic quality: Adjust targeting or pause underperforming source
□ If offer/messaging: Re-test messaging or revert copy

30-Day Retention Drops

30-day retention: 48% → 42% (down 6 points)

CONFIRM:
□ Is this affecting all cohorts or new cohorts only?
  - Jan 1-7 cohort 30-day retention: __ %
  - Jan 8-14 cohort 30-day retention: __ %
  - Jan 15-21 cohort 30-day retention: __ %
□ Is this a product issue (all cohorts dropping) or onboarding issue (only new cohorts dropping)?

ISOLATE BY COHORT:
□ If older cohorts affected:
  - What changed in the product 35+ days ago? (Look back in deploy log)
  - Did a feature break or get deprecated?
  - Did you ship a regression that only shows up over time?
□ If new cohorts affected:
  - Onboarding or product quality issue
  - Check activation rate. Did it drop too?

HYPOTHESES:
□ Product regression (feature broken)
□ Behavioral change (users stopped using key feature)
□ Competitive loss (competitor launched alternative)
□ Seasonal drop-off (compare to same period last year)
□ Segment-specific churn (is one customer segment churning?)

TEST:
□ Breakdown retention by feature usage: Do users of feature X retain better than feature Y?
□ Breakdown retention by segment: Free vs. paid? SMB vs. enterprise? Mobile vs. desktop?
□ Check support tickets: Did churn complaints spike? What are users complaining about?
□ Interview 3-5 churners: Why did they leave?

ACTION:
□ If feature broken: Roll back or fix
□ If behavioral change: Investigate why users stopped using product. Reengagement campaign or UX fix?
□ If competitive: Monitor. Consider feature response or positioning change.
□ If segment-specific: Focus fix on that segment.

Payable Activation Drops

Payable activation rate: 12% → 9% (down 3 points)

CONFIRM:
□ Is this affecting trial users or freemium users? (they have different funnels)
□ Did trial length change? This affects conversion window.

ISOLATE:
□ Breakdown by plan tier:
  - Basic plan conversion: __ % vs. baseline __ %
  - Pro plan conversion: __ % vs. baseline __ %
  - Enterprise plan conversion: __ % vs. baseline __ %
□ Breakdown by acquisition source:
  - Organic: __ % vs. baseline __ %
  - Paid: __ % vs. baseline __ %
□ Breakdown by time in trial:
  - Day 3 conversion: __ %
  - Day 7 conversion: __ %
  - Day 14 conversion: __ %

HYPOTHESES:
□ Pricing changed (did you adjust plan prices?)
□ Onboarding quality degraded (lower quality signups not reaching aha moment)
□ Conversion messaging weak (did you change pricing page or upgrade CTA?)
□ Competition (did competitor launch lower price?)
□ Seasonal (compare to last year)

TEST:
□ If pricing hypothesis: Check if activation rate also dropped. If not, it's onboarding quality, not pricing.
□ If messaging hypothesis: Check if page load time increased or CTA clarity decreased. A/B test messaging.
□ If competition hypothesis: Monitor. Consider response.

ACTION:
□ If pricing: Consider reverting or running discount experiment
□ If onboarding quality: Trace back to onboarding. Did it change?
□ If messaging: Redesign pricing page or upgrade flow
□ If seasonal: Monitor. May be natural

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Part 4: Weekly Review Template

Use this template every Friday to stay on top of AARRR health:

WEEKLY AARRR REVIEW - [Date]

ACQUISITION
□ Signup conversion rate: __ % (baseline: __ %) [GREEN / YELLOW / RED]
□ Signups YTD: __ (pace: on track / behind)
□ CAC this week: $__ (baseline: $__)
□ Top performing source: __ (__ % of signups)
□ Alert: [Any threshold breaches? Document them]

ACTIVATION
□ Activation rate (7-day): __ % (baseline: __ %) [GREEN / YELLOW / RED]
□ Time to activation: __ min (baseline: __ min)
□ Top aha-moment achieving step: __ (__ % of users reach it)
□ Alert: [Any threshold breaches? Document them]

RETENTION
□ 7-day retention: __ % (baseline: __ %) [GREEN / YELLOW / RED]
□ 30-day retention: __ % (baseline: __ %) [GREEN / YELLOW / RED]
□ Session frequency: __ x/week (baseline: __ x/week)
□ Alert: [Any threshold breaches? Document them]

REVENUE
□ Payable activation: __ % (baseline: __ %) [GREEN / YELLOW / RED]
□ MRR: $__ (vs. last week: __) [Change: +$__ / -$__]
□ NRR: __ % (baseline: __ %)
□ Alert: [Any threshold breaches? Document them]

REFERRAL
□ NPS: __ (baseline: __ ) [GREEN / YELLOW / RED]
□ Referral participation: __ % of users
□ Top detractor theme: __ (__ % of detractors cite this)
□ Alert: [Any threshold breaches? Document them]

PRIORITIES FOR NEXT WEEK
□ Investigation #1: [Metric that dropped] → [Hypothesis] → [Action]
□ Investigation #2: [Metric that dropped] → [Hypothesis] → [Action]
□ Wins to celebrate: [What went well this week?]
□ Patterns to watch: [What should we monitor next week?]