Metric Relationships

How metrics connect and influence each other. This is the map.

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The Core Model

Revenue is the outcome. Everything else drives it. The interactive diagram above visualizes these relationships - here’s the breakdown:

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Revenue Drivers

ARR Growth

What increases it:

• New MRR (acquisition)
• Expansion MRR (upsell/cross-sell)

What decreases it:

• Churned MRR (lost customers)
• Contraction MRR (downgrades)

Formula:

ARR Change = New MRR + Expansion MRR - Churned MRR - Contraction MRR

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New MRR Drivers

New MRR = Deals Won × Average Deal Size

Deals Won = Pipeline × Win Rate

Pipeline = MQLs × MQL-to-SQL Rate × SQL-to-Opp Rate × ACV

MQLs = Leads × Lead-to-MQL Rate

Leads = Traffic × Conversion Rate

Levers:

To Increase New MRR	Action
More deals	Increase MQLs (marketing)
Better conversion	Improve win rate (sales)
Bigger deals	Increase ACV (pricing, product)
Faster deals	Reduce sales cycle (process)

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Expansion MRR Drivers

Expansion MRR = Customers × Expansion Rate × Average Expansion Size

Levers:

Driver	What Influences It
Expansion Rate	Product adoption, health score, CSM engagement
Expansion Size	Pricing tiers, usage growth, additional seats
Eligible customers	Contract structure, feature gating

Key relationship: High product adoption → high health scores → higher expansion probability.

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Churned MRR Drivers

Churned MRR = Customers × Churn Rate × Average Customer Value

Levers:

Driver	What Influences It
Churn Rate	NPS, health score, support quality, product fit
Churn Timing	Contract length, renewal process, early warning

Key relationships:

• Low NPS → higher churn (leading indicator by 3-6 months)
• Low health score → higher churn risk
• High support tickets → potential churn signal
• Poor onboarding → higher early churn

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Efficiency Drivers

CAC (Customer Acquisition Cost)

CAC = Sales & Marketing Spend / New Customers

What increases CAC:

• Higher marketing spend without proportional lead increase
• Lower win rates
• Longer sales cycles
• More expensive channels

What decreases CAC:

• Better lead quality (higher conversion)
• More efficient channels
• Faster sales cycles
• Product-led growth

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LTV (Lifetime Value)

LTV = ARPA × Gross Margin / Churn Rate

What increases LTV:

• Higher ARPA (pricing, upsell)
• Better margins (lower COGS)
• Lower churn (better retention)

Key relationship: LTV and CAC must be considered together. LTV:CAC > 3:1 indicates healthy unit economics.

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CAC Payback

CAC Payback (months) = CAC / (ARPA × Gross Margin)

Influenced by:

• CAC (higher = longer payback)
• ARPA (higher = faster payback)
• Gross Margin (higher = faster payback)

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Retention Chain

The retention flywheel connects onboarding to revenue:

Onboarding Quality → Product Adoption → Health Score → Renewal Probability → NRR/GRR

Each stage feeds back into engagement, NPS/CSAT, and ultimately revenue retention.

Key relationships:

If This Goes Down	These Follow
Onboarding completion	Product adoption, early churn
Product adoption	Health scores, expansion, NPS
Health scores	Renewal probability, churn
NPS	Churn (lagged 3-6 months)
Support CSAT	NPS, health score

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Leading vs Lagging Indicators

Leading Indicators (Early Warning)

These change first, before outcomes:

Note: Lead times are approximate ranges based on typical SaaS patterns. Actual lag varies by business model, sales cycle, and customer segment. Calibrate to your own data.

Metric	What It Predicts	Typical Lead Time
Pipeline coverage	Revenue miss	1-2 quarters
MQL volume	Future pipeline	1-2 months
NPS trend	Churn	3-6 months
Health score decline	Churn	1-3 months
Support ticket spike	NPS decline, churn	1-2 months
Product usage decline	Health decline, churn	2-4 weeks
Onboarding completion	Early churn	1-3 months

Lagging Indicators (Outcomes)

These confirm what happened:

Metric	What It Confirms
Revenue	Overall performance
Churn	Retention failure
Win rate	Sales effectiveness
NRR	Customer value growth

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Departmental Impact Map

If Revenue Is Down

Check in this order:

1. New MRR down? → Sales problem
   • Check pipeline, win rate, cycle time
2. Expansion down? → CS/Product problem
   • Check health scores, adoption, CSM coverage
3. Churn up? → Retention problem
   • Check NPS, support metrics, product issues

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If Churn Is Up

Investigation path:

1. Check NPS trend — If declining → deeper customer satisfaction issue
2. Check support metrics — If tickets up / CSAT down → support or product quality
3. Check onboarding metrics — If completion down → onboarding process issue
4. Check product adoption — If declining → product-market fit or engagement
5. Check by segment/cohort — If concentrated → specific segment issue

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If Pipeline Is Light

Investigation path:

1. Check MQL volume — If down → marketing funnel issue
   • Check lead volume → awareness/traffic
   • Check conversion → content/targeting
2. Check SQL conversion — If down → lead quality or SDR effectiveness
3. Check ACV — If down → deal size/pricing issue
4. Check source mix — If channel shift → channel performance issue

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The Full System

The customer lifecycle flows through five stages, each with key metrics:

Stage	Key Metrics	Feeds Into
Acquire	Traffic → Leads → MQLs → SQLs → Pipeline → Closed Won	New MRR
Onboard	TTFV, Onboarding Completion	Adoption, Health Score
Adopt	DAU/MAU, Health Score, NPS	Expansion, Retention
Expand	Expansion Rate, Upsell, Cross-sell	Expansion MRR
Retain	GRR, NRR, Churn Rate	Revenue Protection

Every department’s metrics feed into this system. Understanding the connections is how you diagnose problems and identify opportunities.