Metric Relationships

The Core Model

Revenue is the outcome that every other metric here drives. The interactive diagram above draws these relationships and the sections below take them one at a time, starting with Annual Recurring Revenue (ARR) and the four Monthly Recurring Revenue (MRR) movements that change it.

---

Reading the Map

The interactive diagram above and the MCP traversal tools read the same graph. Its edges come in three groups:

• Component edges say A is an input to B’s formula. They are generated from the canonical formulas rather than drawn by hand, so they always match them.
• Drives, reduces and predicts edges are curated. A drives B when A rising pushes B up. A reduces B when A rising pushes B down, which lets a lower-is-better metric such as First Response Time, or a cost, point at what it affects. A predicts B when A moves first and forecasts where B goes.
• Measures edges link a metric to the business entity it tracks (Logo Churn Rate measures Customer) and are derived from the datasets each formula reads. Entity edges such as owns, converts_to and generates describe how the business objects relate.

Two rules shape how the tools walk the graph:

• One direction per pair. The graph has no two-way edges. A rate computed from an amount does not also claim to move that amount, so a walk upstream never loops back through a ratio’s own inputs.
• Baseline inputs end the walk. A metric read at period start or inside lag() is a baseline: NRR, for example, reads MRR at the start of the period over the customers active then. Driver traversal lists the baseline as a component and stops there, because whatever moved it did so before the period began. New business reaches MRR and enters NRR only as next period’s baseline, which is why the tools leave New MRR, Win Rate and pipeline out of NRR’s drivers.

---

Revenue Drivers

ARR Growth

What increases it:

• New MRR (acquisition)
• Expansion MRR (upsell/cross-sell)
• Reactivation MRR (churned customers returning)

What decreases it:

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

Formula:

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

ARR moves by twelve times Net New MRR, because ARR is MRR × 12.

---

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)

---

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.

---

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 Net Promoter Score (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

---

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

---

LTV (Lifetime Value)

LTV = ARPA × Gross Margin / Revenue Churn Rate

What increases LTV:

• Higher Average Revenue per Account (ARPA) from pricing and upsell
• Better margins (lower COGS)
• Lower churn (better retention)

Key relationship: Read LTV against CAC. An LTV:CAC ratio above 3:1 indicates healthy unit economics.

---

CAC Payback

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

Influenced by:

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

---

Retention Chain

The retention chain runs from onboarding to Net Revenue Retention (NRR) and Gross Revenue Retention (GRR):

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

Each stage also feeds engagement and NPS/CSAT, which in turn feed 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

---

Leading vs Lagging Indicators

Leading Indicators (Early Warning)

These move before the outcomes do:

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

---

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

---

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. Logo Churn Rate, Revenue Churn Rate, Churned MRR, NRR and GRR each declare a by_segment breakdown. If churn is concentrated → specific segment issue

---

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

---

The Full System

The customer lifecycle runs through five stages. The table gives the main metrics at each stage and what they feed.

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, so when an outcome moves, the table above shows which stage to check first.