METRICS TREE
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The metrics tree

Business and product metrics arranged by what feeds what. Every card carries a formula, the parent metric it rolls up into, and a worked example with numbers.

01

Money: from GMV to free cash flow

One ladder. Each line comes from the one above by subtracting a named cost. If a metric has no slot here, it is not a money metric.

  1. GMVTurnover through the platform at buyer prices. Sellers' money, not yours.
  2. less cancellations, refunds, fraud
  3. Net GMVThe turnover that survived to fulfilment.
  4. × take rate, or revenue recognised under your own model
  5. RevenueCompany revenue. Gross for a reseller, net for an agent or marketplace.
  6. less COGS: cost of goods, hosting, licences, delivery
  7. Gross ProfitGross profit. Divided by revenue it becomes gross margin.
  8. less other variable costs: payments, support, last mile
  9. Contribution MarginWhat one order leaves behind before marketing.
  10. less acquisition marketing
  11. CM after CACCohort profit after paying for the cohort. This is where growth and bought growth separate.
  12. less OPEX: engineering, brand, G&A
  13. EBITDAOperating result before depreciation, interest and tax.
  14. less depreciation, interest, tax
  15. Net IncomeNet profit. The line a shareholder sees.
  16. ± working capital, less CAPEX
  17. Free Cash FlowCash in the bank. Profit can be paper, cash cannot.

GMV

Gross Merchandise Value
Everything that crossed the counter, counted whole, before any deduction
Σ price × quantity across paid orders

Turnover before deductions. A measure of the platform's scale, not its income.

→ rolls up into Net GMV
Show example Orders in the month 1,200 Average order value $350 GMV = 1,200 × 350 = $420,000 Orders later refunded are still in GMV. The deduction happens one line below.

Take rate

Commission rate
take GMV
Of the whole stack, the platform keeps the top coin
Revenue ÷ GMV

The share of turnover the platform keeps. A rising take rate with falling GMV means sellers are leaving.

links GMV to Revenue
Show example Revenue $42,000 GMV $420,000 Take rate = 42,000 ÷ 420,000 = 10% Use net GMV, otherwise refunds inflate the base and understate the rate.

Revenue

Top line
Revenue seller
The flow splits: a share stays with the platform, the rest goes to the seller
Net GMV × take rate, or Σ recognised revenue

The top line. In an agency model it is the commission, not the turnover.

→ rolls up into Gross Profit
Show example GMV $420,000 Refunds 5% −$21,000 Net GMV $399,000 Revenue = 399,000 × 10% = $39,900 A reseller on the same data reports $399,000 of revenue and a huge COGS. The model moves the number tenfold.

COGS

Cost of Goods Sold
hosting licences support Revenue
Three streams leave the jar before any profit exists
direct cost of delivering the product

In SaaS: hosting, CDN, licences, the infrastructure that keeps the product running.

subtracted from Revenue
Show example Hosting and CDN $6,200 Licences and APIs $3,100 Product support $2,700 COGS $12,000 Gross Profit = 40,000 − 12,000 = $28,000

Gross Margin

Gross margin
70% COGS Revenue
The revenue column: the hatched base is cost, what stays above is margin
(Revenue − COGS) ÷ Revenue

The ceiling of the model. SaaS holds 70-85%, marketplaces lower, retail lower still.

derived from Gross Profit
Show example (40,000 − 12,000) ÷ 40,000 = 70% Every new dollar of revenue brings 70 cents to cover everything else.

CM1 / CM2 / CM3

Layers of contribution margin
Rev CM1 CM2 CM3
Three cuts: each level takes its own slice off
CM1 = Revenue − COGS CM2 = CM1 − other variable costs CM3 = CM2 − marketing

The numbering is internal. Agree it once and write it down, or two reports will never match.

→ rolls up into EBITDA
Show example Revenue $40,000 − COGS $12,000 CM1 $28,000 − payments, support $8,000 CM2 $20,000 − marketing $12,000 CM3 $8,000

EBITDA

Earnings before interest, tax, depreciation, amortisation
Rev GP CM EBITDA
Four steps down: what you can stand on is left at the very end
CM3 − OPEX

A proxy for operating efficiency. Not cash flow.

→ rolls up into Net Income
Show example CM3 $8,000 − engineering $4,500 − brand and G&A $2,000 EBITDA $1,500 EBITDA margin = 1,500 ÷ 40,000 = 3.75%.

FCF, Burn, Runway

Cash flow, burn, runway
runway
The bag feeds the months until it runs out: what is left is the runway
Runway = cash on hand ÷ monthly net burn

Runway is measured in months. Below 12 it stops being a metric question and becomes a plan question.

the last line of the money ladder
Show example Cash on hand $6,000,000 Net burn per month $500,000 Runway = 6,000,000 ÷ 500,000 = 12 months A 20% rise in burn costs two months of runway with no change in revenue at all.
02

Subscription: how MRR moves

In subscription, revenue is not a number but a balance of five flows. Read the composition of the movement, not the total.

  1. Opening MRRThe base you start from.
  2. plus New MRR and Expansion MRR: upgrades, seats, overage
  3. less Contraction MRR and Churned MRR
  4. Net New MRRNet growth for the period. Negative means marketing is pouring into a leaking bucket.
  5. accumulated month over month
  6. MRR → ARRARR = MRR × 12. A yearly projection, not a fact.

MRR

Monthly Recurring Revenue
Jan Feb Mar
The same coin lands every month
Σ normalised monthly value of active subscriptions

Annual plans divide by 12. One-off payments never belong in MRR.

→ rolls up into Revenue
Show example 300 monthly × $120 = $36,000 50 annual × $1,200 ÷ 12 = $5,000 One-off setup fee $9,000 = 0 MRR = $41,000

ARR

Annual Recurring Revenue
MRR ARR = 12 × MRR
Twelve monthly bars under one bracket
MRR × 12

The investor's language. Valuation usually starts as a multiple of ARR.

derived from MRR
Show example MRR $41,000 × 12 = ARR $492,000 At a 6x multiple the valuation is ≈ $2,950,000

NRR

Net Revenue Retention
start 105% + expansion
The same cohort outgrows its own starting line
(start + expansion − contraction − churn) ÷ start

Measured on a fixed cohort, new customers excluded. Above 100% means growth without acquisition.

the quality of MRR
Show example Cohort a year ago $100,000 + expansion $18,000 − contraction $4,000 − churn $9,000 Total $105,000 NRR = 105,000 ÷ 100,000 = 105% New customers stay out, otherwise the metric collapses into plain revenue growth.

GRR

Gross Revenue Retention
start 87% ceiling 100%
The bar can only lose height: the start line is the ceiling
(start − contraction − churn) ÷ start

Capped at 100%. Shows the leak raw, with no upgrade makeup.

the pair to NRR
Show example (100,000 − 4,000 − 9,000) ÷ 100,000 = 87% NRR 105% with GRR 87% means the base leaks while large accounts pay more. Concentration risk.

SaaS Quick Ratio

Growth efficiency
new + exp churn 3 : 1
Scales: new and expansion against contraction and churn
(New + Expansion) ÷ (Contraction + Churned)

How much new MRR arrives per dollar lost. Below 1 the business is shrinking.

a read on Net New MRR
Show example New $15,000 + Expansion $6,000 = $21,000 Contraction $2,000 + Churn $5,000 = $7,000 Quick Ratio = 21,000 ÷ 7,000 = 3.0

Bookings, Billings, Revenue

Three different numbers
booking billing revenue
One contract, one invoice, revenue spread across the months
Bookings = contracts signed Billings = invoices issued Revenue = revenue recognised

Mixing them in one report is the most common way to fool yourself a quarter ahead.

feed Revenue with a lag
Show example January: a 12-month contract, $120,000 Bookings in January $120,000 Billings in January $120,000 Revenue in January $10,000 Deferred revenue $110,000
03

Unit economics: a single customer

The whole business folded into one user. If it is negative here, scaling only speeds up the loss.

ARPU

Average Revenue Per User
revenue all users
One coin split across the whole crowd, free users included
Revenue ÷ active users

Counted over everyone, free users included. Falling ARPU with rising revenue means the base is being diluted.

→ rolls up into LTV
Show example Monthly revenue $40,000 Active users 60,000 ARPU = 40,000 ÷ 60,000 = $0.67

ARPPU

Average Revenue Per Paying User
1 of 5 pays
Only the marked figures hold a coin: the rest are free
Revenue ÷ paying users

The pair to ARPU. The gap between them is the paid conversion rate.

the pair to ARPU
Show example $40,000 ÷ 1,600 payers = $25 Paid conversion = 1,600 ÷ 60,000 = 2.7% Check: $25 × 2.7% = $0.67 = ARPU

AOV

Average Order Value
$350 one order
One cart, one price tag: how much leaves in one go
Revenue ÷ number of orders

Moved by upsell, bundles and the free-shipping threshold.

→ rolls up into ARPU
Show example $420,000 ÷ 1,200 orders = $350 A $400 free-shipping threshold lifted AOV to $378 in two months

Frequency

Purchase frequency
1.5 orders per buyer
The same buyer comes back three times in the period
orders ÷ buyers in the period

The second multiplier of revenue. Usually cheaper to lift than the average order.

→ rolls up into ARPU
Show example 1,200 orders ÷ 800 buyers = 1.5 Revenue = 800 × 1.5 × $350 = $420,000

LTV

Lifetime Value
lifetime
A stream of coins from one customer, thinning month by month
ARPPU × gross margin ÷ churn rate

Count it in margin, not revenue. Cap the horizon at 12 or 24 months.

→ rolls up into LTV/CAC
Show example ARPPU $25 per month Gross margin 70% → $17.50 of margin Churn 5% per month LTV = 17.50 ÷ 0.05 = $350 Churn at 7% instead of 5% drops LTV to $250. The metric is brutally sensitive to its denominator.

CAC

Customer Acquisition Cost
spend 1 new
Money goes into the megaphone, one customer comes back
acquisition spend ÷ new customers

Only new customers below the line. Media and growth-team salaries both go above it.

→ rolls up into LTV/CAC
Show example Media spend $60,000 Growth team $18,000 Creative $2,000 Total $80,000 New customers 400 CAC = 80,000 ÷ 400 = $200

Blended vs Paid CAC

Blended and paid
blended $80 paid $200
Two funnels: the wide one counts everyone, the narrow one only paid
Blended = spend ÷ all new users Paid = paid spend ÷ paid-acquired users

Blended looks better and says less: organic hides what the channel actually costs.

a refinement of CAC
Show example 1,000 new users, 400 of them paid Blended = 80,000 ÷ 1,000 = $80 Paid = 80,000 ÷ 400 = $200 A decision to scale a channel is made on paid, never on blended.

LTV / CAC

Value to cost ratio
LTV CAC 1.75×
Two columns side by side: what a customer brings against what they cost
LTV ÷ CAC

Below 1 the business pays for the right to operate. Above 5 usually means underinvestment in growth.

the verdict of unit economics
Show example LTV $350 ÷ CAC $200 = 1.75 A healthy SaaS benchmark is 3.0 To reach 3.0 you either cut churn to 2.9% or CAC to $117.

CAC Payback

Payback period
CAC month 11
Cumulative margin crosses the CAC line in month eleven
CAC ÷ monthly margin per customer

Measured in months and hits runway directly. It matters more than LTV/CAC when cash is short.

→ drives Free Cash Flow
Show example CAC $200 ÷ $17.50 margin per month = 11.4 months At 5% churn half the cohort leaves before it pays back. Payback and retention are read together.

Magic Number, Burn Multiple

Efficiency of the dollar invested
0.75 S&M
How much new ARR came out per dollar put in
Magic = ΔARR ÷ prior-quarter sales spend Burn Multiple = net burn ÷ Net New ARR

Magic above 0.75 means press the accelerator. A Burn Multiple below 1.5 is acceptable.

the joint between growth and money
Show example Quarterly ΔARR $900,000 Prior-quarter S&M $1,200,000 Magic = 0.75 Annual net burn $2,000,000 Net New ARR $1,500,000 Burn Multiple = 1.33
04

The funnel: AARRR

Five stages, each with its own metrics. Always work the stage where the leak is biggest in people, not in percent.

  1. AcquisitionImpressions, clicks, visits, installs. Metrics: CPM, CTR, CPC, CPI, CAC, channel share.
  2. visit → signup conversion
  3. ActivationThe user reached value. Metrics: signup rate, activation rate, TTV, onboarding completion.
  4. activation → second visit
  5. RetentionCame back, and came back again. Metrics: D1/D7/D30, cohort curves, churn, resurrection.
  6. active → paying conversion
  7. RevenuePaid. Metrics: conversion to paid, ARPU, AOV, expansion.
  8. share who bring others
  9. ReferralBrought someone new. Metrics: k-factor, viral cycle time, invite rate, NPS as a proxy.

CPM, CTR, CPC, CPA

The traffic cost chain
CPM CPC CPA CAC
Four blocks in a row: impressions, clicks, signups, payers
CPC = CPM ÷ (CTR × 1000) CPA = CPC ÷ conversion to action

One chain, four entry points. A cheap click with poor conversion costs more than an expensive one.

→ rolls up into CAC
Show example CPM $12, CTR 1.5% CPC = 12 ÷ (0.015 × 1000) = $0.80 Signup conversion 8% CPA = 0.80 ÷ 0.08 = $10 Paid conversion 5% CAC = 10 ÷ 0.05 = $200

Activation Rate

Share who reached value
value 40% get here
Through the door, three steps, to the star: that is activation
users who did the key action ÷ signups

The key action is chosen by correlation with day-30 retention, not by team taste.

→ rolls up into Retention
Show example 10,000 signups 4,000 reached the first export Activation = 40% D30 among activated 38% D30 among the rest 4% The 38% versus 4% gap is what makes that action the key one.

TTV

Time to Value
18 min median
The stopwatch measures the road from the start to the star
median time from signup to the key action

Median, not mean. The mean breaks on the tail of people who came back six months later.

a driver of Activation Rate
Show example Median TTV 18 minutes Mean TTV 9 days TTV under 10 min → D30 45% TTV over 1 day → D30 12%

Step conversion

Per step and end to end
100k 10k 4k 600
A funnel of four levels, each narrower than the one above
Step CR = exits ÷ entries End-to-end CR = product of steps

End-to-end conversion hides where the break is. Keep both.

the structure of the funnel
Show example 100,000 visits → 10,000 signups 10% → 4,000 activations 40% → 600 payers 15% End to end = 0.6% People lost: 90,000 at the first step The worst percentage is step three, but step one is the fix: 90,000 people are lost there.

K-factor

Viral coefficient
K
One brings two, and the two bring the next ones
invites per user × invite conversion

Above 1 growth sustains itself. In reality it is almost always below.

→ lowers CAC
Show example 0.6 invites × 25% = K 0.15 1,000 paid → 150 free Effective CAC = 200 ÷ 1.15 = $174

Channel Mix

Acquisition mix
SEO paid referral
Three streams of different width enter the same door
share of new users, CAC and retention by channel

A channel is judged on three: volume, price, cohort quality. Two out of three is an incomplete decision.

a cut of Acquisition
Show example SEO 40% of new, CAC $30, D30 38% Paid social 35% of new, CAC $240, D30 14% Referral 15% of new, CAC $12, D30 41% Other 10% of new LTV/CAC on paid social = 350 ÷ 240 = 1.46
05

Engagement: how the product is used

The layer between activation and money. It answers whether a habit exists at all.

DAU / WAU / MAU

Daily, weekly, monthly actives
DAU 12k WAU 38k MAU 60k
Three nested circles: the day inside the week inside the month
unique users with an activity event in the window

Write the definition of activity down. "Opened the app" and "completed an action" differ by multiples.

the base of every product metric
Show example App opened: DAU 12,000 Key action done: DAU 4,300 MAU 60,000, WAU 38,000, DAU 12,000 Same product, two definitions, a 2.8x difference.

Stickiness

Stickiness
6 of 30 days
A month of squares with six marked: that is how many days people return
DAU ÷ MAU

How many days a month the typical user comes back.

derived from DAU/MAU
Show example 12,000 ÷ 60,000 = 20% 20% × 30 days ≈ 6 days a month Messengers 50-60% Social 30-40% Utilities 10-20%

Sessions

Count, length, depth
2.4 sessions a day
Short visits along one day, the clock counts each
sessions per user, median length, actions per session

A long session is not automatically good. A utility wants it shorter, content wants it longer.

a cut of DAU
Show example Sessions per active per day 2.4 Median length 3 m 10 s Actions per session 7 After simplifying export: length 1 m 50 s, sessions 3.1 Sessions got shorter and more frequent. For a utility that is an improvement.

Feature Adoption

Feature uptake
breadth 15%, depth 12×
In the crowd few use the feature, and those few use it often
breadth = feature users ÷ actives depth = uses per feature user

Two numbers per feature. Narrow and deep is a niche tool, wide and shallow is decoration.

explains Retention
Show example CSV export breadth 9,000 ÷ 60,000 = 15% depth 12 times a month D30 with the feature 46%, without it 19%

Power User Curve

Distribution of active days
1 day 20+ days
The histogram dips in the middle and rises again on the right
histogram of users by 1-30 active days in a month

A smile with a right-hand hump means a habit core. A left peak only means the product is one-off.

a cut of MAU
Show example 1-3 days 60% of users, 18% of actions 4-10 days 28% of users, 34% of actions 20+ days 12% of users, 48% of actions 12% of users produce nearly half the activity. Their churn hurts more than anyone else's.

Core Action

Core action
85k times a week
One big button, pressed again and again
completions per unit of time

The thing the user came for. It usually becomes the North Star.

→ rolls up into North Star
Show example Exports per week 85,000 Per active user 2.2 Actives doing at least one 41%
06

Retention and churn

Retention is a cohort curve, not a number. A flat plateau matters more than a high D1.

Retention D1 / D7 / D30

The classic cohort
D1 D7 D30 plateau
The curve drops fast, then flattens into a plateau: that is the core
users returning on day N ÷ cohort size

State the model: N-day, unbounded or rolling. The same data yields different numbers under each.

→ sets LTV
Show example Cohort of 5,000 signups D1 2,100 = 42% D7 1,150 = 23% D30 800 = 16% D60 760 = 15.2% D90 750 = 15% The curve flattens near 15%. That plateau is the core LTV is built on.

Churn Rate

Churn
5% a month
A bucket with a hole: what leaks out is the churn
lost in the period ÷ base at the start

Separate behavioural churn (stopped using) from billing churn (cancelled).

the inverse of Retention
Show example Base on the 1st 1,600 Cancelled in the month 80 Churn = 80 ÷ 1,600 = 5% Annualised ≈ 1 − (1 − 0.05)^12 = 46% 5% a month sounds mild and means losing nearly half the base in a year.

Logo vs Revenue Churn

Customer churn and revenue churn
logo churn revenue churn
Two small customers leave on one side, one large on the other
Logo = lost customers ÷ customers Revenue = lost MRR ÷ MRR

The gap shows who is leaving: small accounts or large ones.

→ rolls up into GRR
Show example Logo churn 5.0% Revenue churn 2.1% → small accounts are leaving The reverse case: Logo 1.5%, Revenue 6.0% → one large contract walked out

Customer Lifetime

Expected lifetime
M1 M2 M20
An hourglass: the customer months run through it
1 ÷ churn rate

Only valid when churn is stable. On a young base it overstates lifetime by multiples.

a multiplier inside LTV
Show example 1 ÷ 0.05 = 20 months 1 ÷ 0.03 = 33 months 1 ÷ 0.08 = 12.5 months Two percentage points of churn move lifetime by 13 months.

Resurrection

Resurrected users
dormant back
Someone who left comes back through the same door
returning after a dormant spell ÷ dormant users

The fourth flow in the user balance: new, retained, churned, resurrected.

a flow into MAU
Show example MAU last month 58,000 + new 6,500 + resurrected 1,900 − churned 6,400 MAU this month 60,000 The 2,000 gain rests on resurrected users. Without them MAU would be flat.

Cohort table

The retention triangle
M3 Feb
A triangle of cells: rows are cohorts, columns are age
rows are cohorts by entry date, columns are age

Reading down a column shows product effect, reading across a row shows cohort quality.

the instrument for Retention
Show example Cohort M1 M2 M3 January 42% 24% 19% February 44% 27% 22% March 47% 31% 26% Column M3 rises from 19% to 26%: the onboarding changes are working.
07

Perception and support

Survey metrics do not replace behavioural ones. Their place is early diagnosis and segment cuts.

NPS

Net Promoter Score
55% 25% 20% NPS 35
Three faces: promoters, passives, detractors
% promoters (9-10) − % detractors (0-6)

Range −100 to 100. The value sits in the comments and the cuts, not in the number.

a proxy for Referral
Show example 500 responses Promoters 275 = 55% Passives 125 = 25% Detractors 100 = 20% NPS = 55 − 20 = 35 Paying users NPS 48 Free users NPS 12

CSAT

Customer Satisfaction
86% satisfied
A closed ticket and a star rating
satisfied responses ÷ all responses

Tied to one interaction: a ticket, a purchase, a release.

a cut of quality
Show example 430 ratings of 4-5 out of 500 = 86% Survey response 500 ÷ 1,800 tickets = 28% At 28% response the unhappy answer more readily. The level is biased down, the trend still reads.

CES

Customer Effort Score
high effort low effort
The same box: heavy on one side, light on the other
rating of "it was easy to get my task done"

Predicts repeat purchase better than NPS in utilitarian products.

a driver of Retention
Show example Mean score 5.8 of 7 Share 6-7 64% Share 1-3 11% Churn among 1-3 responders 14%/mo Churn among 6-7 responders 2%

PMF Score

The Sean Ellis test
44% threshold 40%
Four in ten would be very disappointed without the product
share answering "very disappointed" without the product

Threshold 40%. Ask active users only, otherwise the number means nothing.

the gate before scaling
Show example 300 active users answered Very disappointed 132 = 44% Somewhat 120 = 40% Not disappointed 48 = 16% Analyst segment 61% Student segment 18% PMF exists in one segment, not in the product overall. That segment is what you scale.

Contact Rate

Support load
3 of 100 write in
Out of the crowd, only a few reach support
tickets ÷ active users

The direct cost of poor UX. It rises with COGS and falls once the top three causes are fixed.

→ rolls up into COGS
Show example 1,800 tickets ÷ 60,000 = 3.0% Cost per ticket $4.20 Support spend $7,560 a month Top cause = 38% of tickets Fixing it = −$2,870 a month

FRT, Resolution Time

First response and resolution time
median 2h p90 11h
A stopwatch between the question and the answer
median and p90 across tickets

Report percentiles. A support average always lies in the company's favour.

a driver of CSAT
Show example FRT median 2 h 10 m FRT p90 11 h 40 m Resolution median 9 h Resolution p90 52 h CSAT when FRT under 1 h 94% CSAT when FRT over 8 h 61%
08

Marketplace and platform

A two-sided market is measured symmetrically: demand, supply and the quality of the match.

Liquidity

Supply liquidity
28% sell
Ten listings on the shelf, three of them sold
transactions ÷ listings posted

The share of supply that finds a buyer. The core health metric of a marketplace.

→ rolls up into GMV
Show example 3,400 deals ÷ 12,000 listings = 28% Core cities 41% Everywhere else 19% Below 20% a seller does not recover the effort of listing and leaves.

Fill Rate

Demand coverage
86% filled
Requests come in, most get matched, some are left hanging
fulfilled requests ÷ all requests

The share of demand that got an answer. Liquidity seen from the buyer's side.

→ rolls up into Net GMV
Show example 8,600 of 10,000 requests = 86% Morning 94% Peak 6-8 pm 71% The problem is not the volume of supply but its schedule.

Search-to-Fill

Search to transaction
1 deal
From the magnifier through the results to a single deal
transactions ÷ search sessions

The quality of search and matching, not the size of the catalogue.

→ rolls up into Conversion
Show example 3,400 deals ÷ 41,000 searches = 8.3% Zero-result searches 11% of sessions After synonym expansion: 9.6%

Supply / Demand

Balance of sides
buyers sellers
A seesaw: many buyers, few sellers
active sellers ÷ active buyers

Shows the tilt of the market and which side the budget belongs to.

→ sets Liquidity
Show example 4,200 sellers : 26,000 buyers = 1 : 6.2 Target corridor 1 : 4 … 1 : 8 Furniture category 1 : 14 → supply shortage

Repeat Rate

Repeat buyer share
30% come back
The same buyer returns to the same shop
buyers with 2+ orders ÷ all buyers

Whether demand returns without advertising. It lowers the load on CAC directly.

→ rolls up into Frequency
Show example 2,900 of 9,800 buyers = 30% Their share of GMV 58% Their CAC $0

Concentration

Turnover concentration
62% top 10% of sellers everyone else
One wide block and a scatter of narrow ones share the same turnover
GMV share held by the top 10% of sellers

Dependency risk on a few players. Read it alongside their retention.

→ a risk to GMV
Show example Top 10% = 420 sellers Their GMV share 62% Top 5 sellers 19% Losing one of the top 5 = −$80,000 GMV

Time to First Transaction

Time to first deal
6 days
Days on the calendar before the first handshake
median from signup to first transaction

How fast a new participant reaches the market. The two-sided version of TTV.

→ rolls up into Activation
Show example Buyers median 2 days Sellers median 6 days Sellers with a deal in week one: 90-day retention 64% Everyone else 17%

Leakage

Off-platform transactions
on platform around it
Two sides shake hands outside the dashed boundary of the platform
estimated share of deals done around the platform

How much take rate escapes the till. Measured by survey and indirect signals.

→ a leak out of Revenue
Show example Survey of 600 sellers: 14% of deals off-platform Off-platform GMV ≈ $59,000 a month Take rate lost ≈ $5,900 Signal: 23% of chats contain a phone number
09

Reliability and delivery speed

Technical metrics enter the product tree exactly where they move conversion and churn.

Uptime, SLO, Error Budget

Availability and error budget
error budget 49% spent
A month of ticks with two gaps: what is left is the error budget
successful requests ÷ all requests error budget = 1 − SLO

99.9% is 43 minutes of downtime a month. The error budget is what releases spend.

→ hits Churn
Show example SLO 99.9% → budget 43 m 12 s a month Incident 1 12 m Incident 2 9 m Spent 21 m = 49% of the budget 99.95% → 21 m 36 s 99.99% → 4 m 19 s

Latency p50 / p95 / p99

Response time percentiles
p50 p95 p99
A distribution with a long tail: p50 near the peak, p99 far to the right
percentile of the response-time distribution

Users live in the tail. Average latency describes nobody's experience.

→ a driver of Conversion
Show example p50 180 ms p95 1.4 s p99 3.2 s Mean 310 ms 1.2M requests a day p99 = 12,000 requests slower than 3.2 s A 310 ms mean looks calm until the tail is converted into people.

Crash-free rate

Crash-free sessions and users
99.2% crash-free 0.8%
A phone with a crack: the share of sessions that make it to the end
sessions without a crash ÷ all sessions

On mobile the bar is above 99.5% by session. Below that it shows up in reviews and store rating.

→ hits Retention
Show example Crash-free sessions 99.2% Crash-free users 94.1% Of 60,000 users, 3,540 saw a crash Their D30 is 9%, everyone else 34%

Core Web Vitals

LCP, INP, CLS
LCP INP CLS
Three gauges: loading, response, layout shift
LCP ≤ 2.5 s, INP ≤ 200 ms, CLS ≤ 0.1 at p75

Thresholds apply to the 75th percentile of real users, not to a lab run.

→ a driver of Acquisition and Conversion
Show example LCP p75 3.1 s → above threshold INP p75 140 ms → within CLS p75 0.04 → within After compressing the hero image LCP 2.2 s, visit → signup conversion 10% → 11.4%

DORA: speed

Deployment Frequency, Lead Time for Changes
14 deploys a week
Commits travel down the pipe to production, the clock times the trip
releases per week, time from commit to production

The speed of feedback. Rare releases make every release risky.

→ sets Cycle Time
Show example Deploys per week 14 Lead time median 6 h Lead time p85 31 h Elite: daily or more, under a day Low: monthly, over a month

DORA: stability

Change Failure Rate, Time to Restore
38 min rollback
A row of releases, one turned back, with the time to restore beside it
CFR = releases with a rollback ÷ all releases MTTR = median time to restore

The pair to speed. Without them deploy frequency turns into incident frequency.

→ guards Uptime
Show example 56 releases in a month, 4 rolled back CFR = 4 ÷ 56 = 7.1% MTTR median 38 m, p90 2 h 15 m Elite: CFR under 15%, MTTR under 1 h

Cycle Time

Task cycle time
dev review QA wait
Four stages on one line, review is the widest
from work started to shipped, median and p85

Breaks into stages. Fix the longest one, not the most visible one.

→ rolls up into Lead Time
Show example Development 1.4 days Review 2.9 days QA 0.8 days Waiting to ship 1.1 days Median 6.2 days, p85 14 days 47% of the time the task sits in review. That is the bottleneck.

Escaped Defects

Defects found by users
17% reach the user
The sieve catches the bugs, one slips through to the user
production bugs ÷ releases, and their share of all bugs

The guard metric for release speed. It rises first when tests are cut for deadlines.

→ guards Deployment Frequency
Show example Bugs found in total 240 Found by users 41 = 17% Per release 0.7 A quarter ago: 9% and 0.3 per release
10

North Star and building your own tree

The tree is built top down and driven bottom up. A team moves the bottom layer only.

  1. Business goalProfit, market share, valuation. Changes once a year.
  2. one metric that captures delivered value
  3. North Star MetricNights booked, minutes listened, reports sent. Not revenue.
  4. decomposes into a product or a sum
  5. DriversUsually 3-5: base width, frequency, depth, quality.
  6. each driver breaks into something controllable
  7. Team input metricsWhat a team moves in a sprint: step conversion, TTV, feature adoption, p95.

North Star criteria

actives share per user
One star, three factors under it
  • Captures user value, not the fact of payment.
  • Leads revenue: it moves before the money does.
  • Controllable by the team within a quarter.
  • Decomposes into factors that do not overlap.
  • Resists gaming: you cannot raise it by making the product worse.
Show example North Star: exports per week 85,000 = weekly actives 38,000 × share who export 41% × exports per exporting user 5.5 Three factors, three owners, three different teams.

Levels and owners

L0 money L1 star L2 drivers L3 team
A pyramid of four levels, each with its own owner
  • L0 money: CEO and board, horizon one year.
  • L1 North Star: product leadership, horizon one quarter.
  • L2 drivers: area owners, horizon one month.
  • L3 input metrics: the team, horizon one sprint.
  • L4 counters and events: analytics, refreshed daily.
Show example L0 EBITDA $1,500 a month L1 85,000 exports a week L2 38,000 actives, 41% share L3 TTV 18 min, activation 40%, p95 1.4 s L4 events export_clicked, export_done
11

Traps

Eleven ways to get the right number and the wrong conclusion.

  • Vanity metrics. Cumulative signups, downloads, followers. They only go up and change no decision.
  • Goodhart's law. A metric that becomes a target stops being a metric. Pair every target with a guard: speed plus quality, conversion plus refunds.
  • Means instead of percentiles. Load time, session length, order value: anything with a long tail is read at the median and p90.
  • A ratio with no denominator. Conversion rose to 8% because traffic halved. Always show both parts.
  • Survivorship bias. Retention measured on active users is flattering by construction.
  • Simpson's paradox. A metric rises in every segment and falls overall because the mix changed.
  • Mixed cohorts. A young base hides churn: the leavers have not had time to leave.
  • Last-click attribution. The closing channel eats the credit of the channel that introduced you.
  • Peeking in A/B tests. Stopping the moment significance appears inflates the effect. Fix sample size and duration before the start.
  • Local optimum. A string of small conversion wins will not save a product with no retention.
  • Definition drift. "Active user" changed quietly and the yearly chart became meaningless. Keep definitions in one place, each with the date it changed.

Nothing found. Try another word: churn, CAC, p95, take rate.