Deep dives

In-depth analysis of the metrics that matter most: customer acquisition cost, churn rate, net revenue retention, customer lifetime value, gross margin, and conversion rate.

Deep-dive guides on CAC, churn rate, NRR, customer lifetime value, gross margin, conversion rate, and other critical business metrics.

9 guides

Chapters
01What CAC is and why it matters
02The CAC formula and its components
03Decomposing CAC with a metric tree
04Blended vs channel-specific CAC
05The CAC:LTV ratio and unit economics
06CAC benchmarks by industry and channel
07How to reduce CAC using tree-based analysis
GuideDeep dives
Customer acquisition cost: a metric tree approach
Customer acquisition cost is one of the most important metrics in any business, yet most teams treat it as a single number. That hides more than it reveals. When CAC rises, the obvious response is to cut spend, but the real cause might be a conversion rate problem, a channel mix shift, or a sales cycle that has lengthened by weeks. A metric tree decomposes CAC into its component inputs so you can diagnose the root cause and fix the right lever. This guide covers the CAC formula, its components, how to build a CAC metric tree, blended vs channel-specific CAC, the CAC:LTV ratio, industry benchmarks, and a structured approach to reducing acquisition cost.
View guide9 min read
Chapters
01Types of churn rate and when each matters
02Decomposing churn with a metric tree
03Churn by segment, cohort and reason
04Leading indicators of churn
05Reducing churn with tree-based diagnosis
06Churn benchmarks and how to use them
GuideDeep dives
Churn rate analysis: formulas, benchmarks and fixes
Churn rate is one of the most scrutinised metrics in subscription businesses, yet most teams measure it as a single number and hope it goes down. That approach tells you how many customers or how much revenue you lost, but not why. A metric tree decomposes churn into its component parts, connects each branch to a specific cause, and gives every team a clear lever to pull. This guide covers the different types of churn, the formulas behind each, how to build a churn-focused metric tree, and how to use segmentation, cohort analysis, and leading indicators to move from reactive reporting to proactive retention.
View guide9 min read
Chapters
01What is net revenue retention?
02Why NRR matters for SaaS valuation and growth
03Decomposing NRR with a metric tree
04NRR benchmarks by segment and stage
05Levers to improve NRR
06Connecting NRR to other SaaS metrics
GuideDeep dives
Net revenue retention: formula, benchmarks and levers
Net revenue retention is the single best indicator of whether a SaaS business can compound growth from its existing customer base. An NRR above 100% means your installed base is growing on its own, without a single new logo. But NRR is a composite outcome. It is the net result of gross retention, expansion revenue, and contraction. To move NRR deliberately, you need to decompose it into the operational levers underneath. This guide walks through the NRR formula, shows how to build a metric tree that decomposes it, shares benchmarks by segment and stage, and identifies the levers that reliably improve each branch.
View guide9 min read
Chapters
01What revenue per employee actually tells you
02Decomposing revenue per employee with a metric tree
03Benchmarks by industry and company stage
04How to improve revenue per employee
05When revenue per employee is misleading
06Connecting to other efficiency metrics
GuideDeep dives
Revenue per employee as a metric tree
Revenue per employee is one of the most widely cited efficiency metrics in business. It appears in board decks, investor reports, and benchmarking studies across every industry. Yet on its own, the number tells you remarkably little. A company with high revenue per employee might be genuinely efficient, or it might be outsourcing heavily, underpaying staff, or riding a capital-intensive business model that has little to do with workforce productivity. This guide shows how to decompose revenue per employee into a metric tree that reveals the real drivers of efficiency and gives you levers to improve it.
View guide9 min read
Chapters
01The war room problem
02A systematic diagnostic framework
03Walking the tree: a worked example
04Five common causes of metric movement
05Why dashboards fail at diagnosis
06Building a diagnostic habit
GuideDeep dives
Why did my metric change? A diagnostic framework
When a metric moves unexpectedly, most teams scramble. They open dashboards, run ad-hoc queries, and schedule war room meetings. The investigation starts from scratch every time because there is no persistent model of how the business works. A metric tree solves this by providing a structure you can walk through systematically. This guide teaches the diagnostic framework.
View guide10 min read
Chapters
01What dashboards do well
02Where dashboards fall short
03What a metric tree adds
04The diagnostic gap
05Dashboards and metric trees together
06Signs you have outgrown your dashboards
GuideDeep dives
From dashboards to metric trees
Dashboards have earned their place. They surface numbers, highlight trends, and keep stakeholders informed at a glance. But when a metric moves and you need to understand why, dashboards leave you on your own. They show what happened without modelling the cause. A metric tree picks up where dashboards stop by encoding the relationships between metrics into a navigable structure. This guide walks through what dashboards do well, where they fall short, and how metric trees complement them.
View guide8 min read
Chapters
01What LTV is and why it matters
02The LTV formula and its components
03Simple vs cohort-based LTV calculation
04Decomposing LTV with a metric tree
05The LTV:CAC ratio and industry benchmarks
06Improving LTV through the tree
GuideDeep dives
Customer lifetime value: a metric tree decomposition
Customer lifetime value is the metric that connects acquisition spending to long-term profitability. It tells you how much a customer is worth over the entire duration of their relationship with your business, and therefore how much you can afford to spend to acquire them. Yet most organisations calculate LTV as a single number and leave it at that. The result is a metric that informs board decks but does not guide daily decisions. A metric tree changes this by decomposing LTV into its component parts: average revenue per user, gross margin, customer lifespan, and expansion dynamics. When LTV moves, the tree tells you which component drove the change and which team can act on it. This guide covers the LTV formula, simple versus cohort-based calculation methods, how to build an LTV metric tree, the LTV:CAC ratio, industry benchmarks, and a structured approach to improving lifetime value through each branch of the tree.
View guide9 min read
Chapters
01What gross margin is and why it matters
02Decomposing gross margin with a metric tree
03Gross margin by business model
04Gross margin benchmarks and what drives them
05Improving gross margin through tree-based analysis
06Connecting gross margin to the broader metric tree
GuideDeep dives
Gross margin: a metric tree approach
Gross margin is one of the most scrutinised numbers in any business. It tells investors whether a company can generate profit from its core operations, tells operators whether pricing and cost structures are sustainable, and tells finance teams where margin is being won or lost. Yet most organisations treat gross margin as a single percentage on a P&L statement, reacting to movements after the fact rather than understanding the drivers behind them. A metric tree decomposes gross margin into its revenue and cost components, making the causal structure visible and giving teams specific levers to pull. This guide covers the gross margin formula, how to decompose it with a metric tree, benchmarks across SaaS, e-commerce, and services business models, and a structured approach to improving margin through tree-based analysis.
View guide9 min read
Chapters
01What conversion rate really measures
02Types of conversion across the funnel
03Decomposing conversion rate with a metric tree
04Conversion by channel and segment
05Conversion benchmarks by industry
06Improving conversion through tree-based diagnosis
GuideDeep dives
Conversion rate: a metric tree decomposition
Conversion rate is the metric that sits at the heart of every growth model, yet most teams treat it as a single number. That conceals more than it reveals. When conversion drops, the instinct is to redesign a landing page or increase ad spend, but the real cause might be a qualification problem, a pricing friction, or a channel mix shift that has flooded the top of the funnel with low-intent traffic. A metric tree decomposes conversion rate into its constituent stages, channels, and segments so you can diagnose the root cause and act on the right lever. This guide covers what conversion rate really measures, the types of conversion that matter at each funnel stage, how to build a conversion rate metric tree, how to analyse conversion by channel and segment, benchmarks by industry, and a structured approach to improving conversion through tree-based diagnosis.
View guide9 min read

Deep dives

Customer acquisition cost: a metric tree approach

Churn rate analysis: formulas, benchmarks and fixes

Net revenue retention: formula, benchmarks and levers

Revenue per employee as a metric tree

Why did my metric change? A diagnostic framework

From dashboards to metric trees

Customer lifetime value: a metric tree decomposition

Gross margin: a metric tree approach

Conversion rate: a metric tree decomposition

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