Metrics by business model skill

Metrics by Business Model

Table of Contents

• How to Use This File
• E-commerce
• SaaS
• Free Mobile App
• Media Site
• User-Generated Content
• Two-Sided Marketplace
• Hybrid Models

How to Use This File

Name your primary business model, install that model's metric tree, write a definition doc for each metric before building dashboards, and watch the failure modes — most analytics disasters are measurement errors, not data errors. The metrics below are the canonical set; your OMTM at any moment is one of them (chosen by stage), and the rest are supporting or counter-metrics.

E-commerce

Core question: do visitors buy, and do buyers come back?

Metric	Formula	Why it matters
Conversion rate	orders ÷ unique visitors	The fundamental efficiency of the store
Average order value (AOV)	revenue ÷ orders	Second lever on revenue besides traffic
Annual repurchase rate	% of buyers purchasing again within 12 months	Decides your mode (below)
Cart abandonment	1 − (orders ÷ carts created)	Where intent dies; checkout friction signal
Revenue per customer (RPC)	revenue ÷ customers in period	Conversion × AOV × repurchase in one number
Customer acquisition cost (CAC)	acquisition spend ÷ new customers	Must sit far below RPC over the customer's life

The mode decision. The annual repurchase rate determines what kind of e-commerce business you are, and the playbooks differ sharply. Under roughly 40% of buyers repurchasing in a year, you're in acquisition mode: the game is top-of-funnel efficiency, conversion, and AOV — loyalty programs are wasted spend. Above roughly 60%, you're in loyalty mode: the game is repeat behavior, recommendations, reorder flows, and share of wallet. Between the two, run a hybrid: grow acquisition while nudging the repurchase rate up. Misdiagnosing the mode is the most expensive e-commerce metric mistake — building loyalty infrastructure for a one-and-done product (mattresses) or burning ad spend where retention should compound (consumables).

Instrumentation notes: instrument the full funnel as distinct events (product view → add-to-cart → checkout start → payment → confirmation) so abandonment localizes to a step; tie every order to an acquisition source at the customer level, not the session level; deduplicate customers across devices and guest checkouts (email is the usual join key); record refunds and returns as first-class events.

Measurement failure modes: celebrating traffic while conversion slides; reporting one blended conversion rate across channels with wildly different intent (brand search converts 10x display); ignoring returns, refunds, and shipping costs so "revenue" overstates reality; comparing holiday weeks to ordinary ones instead of year-over-year; and counting sessions as shoppers, which double-counts the same person comparison-shopping on two devices.

SaaS

Core question: do trials become long-lived, expanding subscriptions?

Metric	Formula	Why it matters
MRR / ARR	sum of active subscription value per month/year	The growth headline, normalized
Customer churn	customers lost ÷ customers at period start	The leak that caps growth
Revenue churn (gross/net)	MRR lost (gross); MRR lost − expansion (net)	Net churn below 0% means the base grows itself
LTV	ARPA × gross margin ÷ churn rate	What a customer is worth, honestly margined
CAC	sales + marketing spend ÷ new customers	What a customer costs, fully loaded
LTV:CAC	LTV ÷ CAC	>3 is the standard health line
CAC payback	CAC ÷ (ARPA × gross margin)	Months to recover acquisition; <12 is the heuristic
Trial-to-paid conversion	paying ÷ trials in cohort	Funnel efficiency; watch by channel
Time-to-value	median time from signup to first core value	The strongest onboarding lever on conversion
Expansion/upsell	MRR added from existing accounts	Cheapest growth; drives net churn negative

Instrumentation notes: revenue numbers must come from the billing system, not the analytics tool — analytics events drop, double-fire, and miss dunning; model the subscription as a state machine (trial → active → past_due → paused → churned, plus upgrade/downgrade transitions) and emit an event per transition; define "active use" by core action so you can see engagement churn — accounts that stopped using but haven't stopped paying — which is next quarter's revenue churn, visible today.

Measurement failure modes: conflating MRR with cash collected (annual prepays inflate cash now and create a renewal cliff later — track both, label both); reporting gross churn as if it were net, or hiding gross churn behind healthy expansion; computing LTV without gross margin (an LTV of revenue, not profit); survivor-biased LTV from your oldest, happiest cohort; counting paused or downgraded accounts inconsistently across reports; and blending churn across plan tiers when enterprise and self-serve behave like different species.

Free Mobile App

Core question: do downloads become engaged users, and does a small slice pay enough to carry everyone?

Metric	Formula	Why it matters
Launch/activation rate	users reaching first value ÷ installs	Installs are shelf-ware until first value
D1 / D7 / D30 retention	% of install cohort active N days later	The shape of the leak
DAU/MAU	daily actives ÷ monthly actives	Habit strength; how many days a month users need you
% paying	payers ÷ active users	The size of the monetized slice
ARPDAU	daily revenue ÷ DAU	Revenue intensity, comparable across days
ARPPU	revenue ÷ paying users	Depth of monetization among payers
k-factor	invites per user × invite conversion	Organic distribution multiplier
Churn	users inactive >N days ÷ actives	Defines the ceiling on MAU growth

Instrumentation notes: define "active" as performing the core action, not opening the app (push-notification-driven opens flatter DAU without value); fix your retention convention — calendar-day vs rolling 24-hour windows produce different numbers, and mixing them invalidates trends; expect platform/store reporting lag and reconcile attribution SDK installs against store-reported installs; track the revenue distribution, not just the mean — monetization usually concentrates in a small cohort of high spenders, so medians and deciles tell the truth that ARPPU hides.

Measurement failure modes: treating downloads as the growth metric (it's the top of a funnel that loses ~80%+ by week one); averaging revenue across all users when a tiny whale cohort dominates it; forgetting the platform's ~30% cut so "revenue" overstates net by nearly a third; comparing retention across cohorts acquired from incentivized installs vs organic (they're different populations); and celebrating DAU spikes caused by notification blasts that simultaneously spike opt-outs — the engagement equivalent of a payday loan.

Media Site

Core question: can you grow attention and monetize it without destroying the reason it exists?

Metric	Formula	Why it matters
Audience growth	unique visitors, trended + return-visitor rate	Reach, qualified by loyalty
Engaged time	actively-measured time on content	Honest attention; raw time-on-page lies
CTR	ad clicks ÷ ad impressions	What advertisers ultimately buy
RPM	revenue ÷ 1,000 pageviews (or sessions)	Monetization efficiency of attention
Ad inventory	impressions available per period	The supply you sell
Content-to-ad balance	ad density per page/session	The tradeoff dial between revenue and trust

Instrumentation notes: measure engaged time with activity signals (scroll, focus, pointer movement), not last-page timestamps — a tab open in the background is not attention; attribute traffic by source and content type so you can see which content earns loyal readers vs drive-by social spikes; track the return rate of new visitors per content category — it tells you what to commission next.

Measurement failure modes: pageview inflation via pagination and slideshows — it manufactures ad inventory while destroying engaged time and reader trust, a vanity trade that monetizes the brand to death; counting bounce-heavy viral traffic as "audience" when it never returns; chasing CTR with clickbait that raises short-term clicks and long-term unsubscribes; and reporting RPM gains that came entirely from ad density increases (the counter-metric — return-visitor rate — catches this).

User-Generated Content

Core question: do visitors climb the engagement funnel and create content that brings others back?

The defining tool is the engagement funnel — a ladder of deepening participation: visitor → voyeur (consumes content) → commenter/voter (lightweight interaction) → creator (posts original content) → sharer (distributes it outward). Participation is radically unequal — in the spirit of the 90-9-1 rule, lurkers vastly outnumber contributors, who vastly outnumber creators — so the metrics that matter are the tier sizes and the transition rates between tiers, not the total account count.

Metric	Formula	Why it matters
Tier transition rates	users moving up a tier ÷ tier population	The health of the participation ladder
Content per active user	items created ÷ active users	Density of the content economy
Time between contributions	median gap per creator	Creator habit strength
Notification effectiveness	re-engagement CTR ÷ opt-out rate	Your main lever, and its cost
Spam/fraud rate	bad content ÷ total content	The tax on everything else

Instrumentation notes: define each tier by concrete actions and make tiers mutually exclusive per period so funnel math adds up; instrument content quality signals (votes, reports, removals) alongside volume; measure notification effects as a pair — every re-engagement campaign gets its opt-out cost attached.

Measurement failure modes: counting registered accounts when creators are what the system runs on; celebrating engagement minutes that are pure passive scroll while creation flatlines (the voyeur tier grows, the ladder is broken); excluding spam from no reports so content volume looks healthy while the real feed rots; and over-notifying — DAU rises for a month, then the opt-out wave caps your re-engagement channel permanently.

Two-Sided Marketplace

Core question: liquidity — do listings meet buyers fast enough that both sides come back?

Metric	Formula	Why it matters
Fill rate / sell-through	listings transacting within window ÷ listings	The supply side of liquidity
Search-to-fill	searches ending in a transaction ÷ searches	The demand side of liquidity
Time-to-transaction	median time from listing (or search) to deal	Liquidity's speed dimension
Buyer/seller ratio	active buyers ÷ active sellers	Which side is the constraint
Take rate	platform revenue ÷ GMV	How much of the flow you capture
GMV	total transaction value	Context only — vanity until × take rate
Repeat rate per side	% of each side transacting again in period	Whether liquidity converts to habit
Fraud/dispute rate	disputed transactions ÷ transactions	Trust, the marketplace's real product

Instrumentation notes: define the transaction window per category — 30 days is meaningless if cars take 60 and concert tickets take 2; instrument both sides' funnels separately (seller: list → inquiry → transact; buyer: search → view → contact → transact) because the constraint side changes over time; measure liquidity per market (city, category), never as a global blend — a marketplace is a portfolio of local liquidity pools.

Measurement failure modes: GMV as the headline while take-rate revenue stays trivial; subsidized transactions (coupons, free credits) counted as organic liquidity; concentration blindness — one power seller or one corporate buyer making aggregate liquidity look healthy while the long tail gets nothing; growing the easy side (usually supply) while the constraint is demand, so listings balloon and fill rate collapses; and blended liquidity across markets hiding the fact that you have one working city and five dead ones.

Hybrid Models

Most real businesses mix archetypes: a subscription box is e-commerce acquisition with SaaS retention economics; a marketplace may sell SaaS tools to its sellers; a media site may run a UGC community. The rule: pick ONE primary model — the one whose failure kills the company — and let it own the OMTM. The secondary model contributes supporting metrics and counter-metrics, not a second north star.

Deciding the primary: ask where the riskiest economics live. A subscription box dies from churn, not from cart abandonment → primary model SaaS, with AOV and cart metrics as supporting. A marketplace with seller tools dies if liquidity fails, no matter how good tool attach rates look → primary model marketplace until liquidity is proven, then revisit. If you'd describe yourself to an investor as "X with a bit of Y," X is the primary.

Two failure patterns to avoid: dual-OMTM paralysis, where two teams optimize two north stars and trade wins back and forth invisibly (the counter-metric structure exists precisely so the secondary model constrains rather than competes); and model drift, where the company quietly becomes the other model — the day your marketplace's SaaS-tool revenue exceeds take-rate revenue and retains better, re-run the model choice deliberately instead of letting the dashboard decide by inertia.