Craft · Ledger
Seven metrics, and where each one quietly lies.
CAC, LTV, ROAS, CTR, attribution, retention and NPS are the seven numbers most marketing decisions actually rest on. Each one is real and useful — and each one has a well-documented way of misleading whoever reads it uncritically. This ledger states the mechanics, the gaming vectors and the honest limits, sourced throughout, with no invented benchmark for what a "good" number looks like.
Every row traces to a source in Section 03 — see “About this page.”
01 — Before The Ledger
Two honest patterns worth knowing first.
Most people land on a page like this browsing for a specific number — "what's a good CAC," "is my ROAS healthy." What's actually useful to know first is two patterns that repeat across all seven metrics below:
- Every metric here can be gamed by changing what counts as the denominator or numerator — excluding costs from CAC, counting logo retention instead of revenue retention, timing an NPS survey right after a good experience. The ledger's "how it's gamed" column exists because this happens constantly, often without bad intent.
- A metric that looks strong in isolation can still describe a business that's losing money — a high ROAS on a low-margin product, a high CTR from clickbait creative that never converts, an NPS that predicts nothing about actual growth. The "pair it with" column is the real point of this page: no metric here is meant to be read alone.
02 — The Ledger
Search, sort, filter.
Click a column header to sort; use the search box or the type filter to narrow the list. All data is static and client-side — nothing here is fetched live.
| CAC Customer Acquisition Cost | Acquisition | Total sales & marketing spend ÷ new customers gained, in the same period.1 | Excluding salaries, tools or overhead from the numerator; counting free or self-serve sign-ups as "customers" to inflate the denominator. | Compared across channels with very different sales cycles, or read without checking the payback period against LTV. | LTV (as an LTV:CAC ratio) and CAC payback period in months. |
| LTV Customer Lifetime Value / CLV | Value | Total revenue or margin expected from a customer over the relationship (avg. purchase value × frequency × lifespan, refined with margin).2 | Using revenue instead of margin (hides service/support cost); assuming an optimistic lifespan not yet observed in your own cohort data. | Projected from too little cohort history, or blended across segments that behave very differently. The widely cited "3:1 LTV:CAC" rule of thumb was derived from mature, public SaaS companies at steady state and described by its own author as a viability floor, not a universal target — it is routinely misapplied to early-stage or non-SaaS businesses.3 | CAC and payback period; cohort-level (not blended) retention. |
| ROAS Return on Ad Spend | Efficiency | Conversion value generated per unit of ad spend.4 | Crediting the ad platform with revenue from organic, brand-search or repeat buyers who would likely have bought anyway. | Ignores margin entirely — a high ROAS on a low-margin product can still lose money — and ignores value earned outside the tracked attribution window. | Contribution margin and CAC, not revenue alone. |
| CTR Click-Through Rate | Engagement | Clicks ÷ impressions.5 | Clickbait creative or misleading thumbnails pull clicks without buying intent, inflating CTR while conversion collapses. | Used alone as a "success" signal without checking downstream conversion; varies hugely by ad format and placement, so cross-format comparisons mislead. | Conversion rate, then CAC. |
| Attribution Last-click, multi-touch, MMM | Method | Which touchpoint(s) get credit for a conversion — a rule, a set of rules, or a data-driven model.6 | Platforms run their own attribution logic that tends to flatter themselves — the same sale can be claimed in full by several "walled garden" platforms at once. | Last-click ignores upper-funnel and brand-building effort entirely; multi-touch degrades as cookie deprecation and cross-device gaps widen; no attribution model on its own establishes causation, only correlation. | Incrementality (holdout) testing or marketing-mix modelling for a causal check. |
| Retention / Churn | Loyalty | The share of customers, or revenue, kept (or lost) over a period. | Reporting logo retention while ignoring revenue churn, or vice versa — a business can retain 95% of accounts while losing meaningful revenue to downgrades. | A single blended figure hides very different cohort behaviour. The famous "5% better retention lifts profit 25–95%" claim is often repeated as a universal law; the original 1990 study reported specific, industry-different figures — 85% in one bank's branch system, 50% in an insurance brokerage, 30% in an auto-service business — a good example of a real, sourced number flattened into a myth through repetition.7 | Cohort-level retention curves and revenue (not just logo) churn. |
| NPS Net Promoter Score | Loyalty | % Promoters (score 9–10) minus % Detractors (0–6) on "how likely are you to recommend us."8 | Survey timing/targeting (asking only happy customers right after a good experience); incentivised responses; excluding unhappy segments from the sample. | Treated as a guaranteed predictor of growth: an independent academic replication of Reichheld's own exemplar industries, with a larger sample, found NPS performed no better than ordinary satisfaction measures at predicting growth — the original "clear superiority" claim did not hold up under independent testing. The 9/7 promoter-passive-detractor cutoffs are also arbitrary, not derived from the data itself.8 | A direct satisfaction (CSAT) measure and actual behaviour — repeat purchase, tracked referrals — not just stated intent. |
Method: rendered as static HTML so the full ledger is present without JavaScript; search, sort and filter enhance it client-side.
03 — FAQ
Common questions.
Why isn't a metric like impressions or followers on this list?
This page covers the seven metrics most often used to make a real decision — spend, value, efficiency and loyalty calls. Raw reach numbers are usually inputs into these seven rather than decisions in themselves, so they're referenced inside the ledger rather than given their own row.
Is NPS worthless, then?
No — it's a fast, cheap, widely understood pulse-check, and the underlying question is a reasonable one to ask. What the evidence doesn't support is the stronger original claim that it uniquely and reliably out-predicts growth better than other satisfaction measures. Use it as one loyalty signal among several, not as the only number that matters.
What's the "right" CAC:LTV ratio?
The commonly cited 3:1 rule of thumb comes from David Skok's "SaaS Metrics 2.0," derived from mature, publicly listed SaaS companies at steady state — described by its own author as a viability floor, with top performers closer to 5:1, not a universal target for every business or stage.
Why doesn't this page give a benchmark number for any of these metrics?
Because a single "good CAC" or "good CTR" figure isn't honest across industries, business models, price points and channels — the same number can be excellent in one context and a warning sign in another. This ledger gives you the mechanics and failure modes instead, so you can judge your own number in your own context.
What's the difference between multi-touch attribution and marketing-mix modelling?
Multi-touch attribution (MTA) tracks individual touchpoints and splits credit between them — granular, but dependent on tracking data that cookie deprecation and walled gardens increasingly break. Marketing-mix modelling (MMM) uses aggregated historical data instead, trading granularity for resilience to those tracking gaps. Neither establishes causation on its own; incrementality (holdout) testing is the closer approximation to a real experiment.
04 — Sources
Where the numbers and the caveats come from
- Corporate Finance Institute — “Customer Acquisition Cost (CAC).” Definition and formula.corporatefinanceinstitute.com/resources/accounting/customer-acquisition-cost-cac/ · accessed 27 Jul 2026
- Corporate Finance Institute — “Lifetime Value Calculation.” CLV/LTV formula.corporatefinanceinstitute.com/resources/valuation/lifetime-value-calculation/ · accessed 27 Jul 2026
- Skok, David. “SaaS Metrics 2.0 — A Guide to Measuring and Improving What Matters.” For Entrepreneurs. Origin of the 3:1 LTV:CAC rule of thumb.forentrepreneurs.com/saas-metrics-2/ · accessed 27 Jul 2026
- Google Ads Help — “About Target ROAS bidding.”support.google.com/google-ads/answer/6268637 · accessed 27 Jul 2026
- Google Ads Help — “Clickthrough rate (CTR): Definition.”support.google.com/google-ads/answer/2615875 · accessed 27 Jul 2026
- Google Analytics Help — “Get started with attribution” and Google Ads Help “About attribution models.”support.google.com/analytics/answer/10596866 · support.google.com/google-ads/answer/6259715 · accessed 27 Jul 2026
- Reichheld, F.F. & Sasser, W.E. “Zero Defections: Quality Comes to Services.” Harvard Business Review, Sep–Oct 1990, pp. 105–111 — source of the specific 85%/50%/30% industry figures cited in the Retention row.hbr.org/1990/09/zero-defections-quality-comes-to-services · accessed 27 Jul 2026
- Reichheld, F. “The One Number You Need to Grow.” Harvard Business Review, Dec 2003 (origin of NPS); Keiningham, T.L. et al. “A Longitudinal Examination of Net Promoter and Firm Revenue Growth.” Journal of Marketing, 71(3), 2007, pp. 39–51 (independent academic replication and critique).hbr.org/2003/12/the-one-number-you-need-to-grow · accessed 27 Jul 2026
About this page: every mechanic described (formula, what counts as gaming, documented failure modes) traces to one of the eight sources above. No "good" or "average" number for any metric is asserted anywhere on this page, because no single such figure is honest across industries and business models.
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