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Start with the decision, not the dashboard
Before adding a metric, name the question it should help answer and the action you might take based on the result. If neither is clear, the metric probably does not belong in the active dashboard.
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Alistair Croll and Benjamin Yoskovitz, authors of Lean Analytics, write, “You can’t just start measuring everything at once.” Their five-stage framework moves from understanding the problem to testing whether a product is sticky, whether it spreads, whether it earns revenue, and whether the business can scale. The authors also recognize that stage boundaries will not fit every company perfectly. Treat the sequence as a way to focus, not a mandatory checklist.
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Empathy: Is the problem important enough to solve?
Before product usage is meaningful, look for evidence that a real customer has a painful problem and wants a solution. Useful signals include repeated descriptions of the same problem in interviews, observed workarounds, and evidence that the buyer would pay. These are partly qualitative; traffic and signups alone do not establish that the problem matters.
Stickiness: Do customers reach value and return?
Define an activation event as the action that indicates a customer has reached the product’s initial core benefit. The event must fit the product: a collaboration tool, developer product, and accounting platform will not necessarily share the same value moment. Track the share of eligible customers who complete it, how long it takes them to do so, and whether they repeat the core workflow.
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Pair engagement with retention or customer feedback. For B2B SaaS, distinguish account retention from activity by individual users: one active champion does not necessarily mean an account has adopted the product broadly. Lean Analytics describes Stickiness as testing whether the product is good enough to retain users; its SaaS material includes lifecycle, engagement, and churn.
Virality: Does using the product create organic spread?
Measure a growth loop only when sharing, invitations, or collaboration are plausible parts of the product experience. In that case, track the share or invite action, the conversion of invited prospects, and the time from invitation to arrival. A viral coefficient is not useful merely because it can be calculated. The Lean Analytics sequence puts Virality after Stickiness, a useful reminder to establish repeat value before making a growth loop the main focus.
Revenue: Can customers be monetized sustainably?
Track paying customers, monthly recurring revenue (MRR), net new MRR, customer churn, revenue churn, expansion, and contraction. Add gross margin when costs can be attributed. When acquisition becomes repeatable rather than anecdotal, measure customer acquisition cost (CAC) by channel or segment and CAC payback.
Keep trials, pilots, services, and contracted-but-not-live accounts from obscuring the recurring-revenue measure. Stripe’s SaaS metrics guide describes MRR and annual recurring revenue (ARR) as recurring-revenue measures and excludes one-time payments and professional services.
Scale: Can a working model expand efficiently?
Once retention and monetization are established, consider channel efficiency, customer concentration, gross margin, cash burn and runway, and support or implementation cost. The operational view should fit the business motion: self-serve, sales-led, usage-based, and enterprise SaaS can have very different costs and bottlenecks. Lean Analytics names Scale as its final stage while cautioning that companies do not all fit its stages exactly.
Define core SaaS metrics so they can guide decisions
Metric names are not definitions. Write down the customer unit, period, denominator, and inclusions before comparing results over time or between teams.
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| Metric | Practical definition and reporting note |
|---|---|
| Activation | A product-specific event showing that a customer reached an initial core benefit. State the event and denominator; there is no universal SaaS activation formula. |
| Cohort retention | The share of a defined starting group that remains a customer or continues a chosen value behavior over time. Identify the cohort rule and elapsed period, and say whether retention means customer/logo, revenue, or product activity. |
| Customer churn | Customers lost during a period divided by customers at the start of that period. Disclose the period, denominator, and treatment of reactivations. |
| Revenue churn | Recurring revenue lost from customers during a selected period. Show gross revenue churn separately from expansion or net retention so growth in existing accounts does not hide losses. |
| MRR and ARR | Normalized monthly or annual recurring subscription revenue. Define how discounts, variable usage, annual prepayments, and contracted-but-not-live accounts are treated; exclude one-time payments and professional services under Stripe’s guide. |
| Net new MRR | A movement view combining new and expansion revenue with contraction and churn. Keep component definitions stable month to month. |
| CAC | Sales and marketing cost associated with acquiring new customers divided by customers acquired over the defined period. Document the cost scope and attribution window. |
| CAC payback | The time required for gross profit or contribution from a new customer to recover acquisition cost. A simplified calculation may divide CAC by that customer’s MRR; state whether it adjusts for gross margin, onboarding, or contract timing. |
| LTV | A forecast of value over a customer relationship, dependent on retention, revenue, margin, and other assumptions. Identify those assumptions, especially when retention history is short. |
| NRR | Revenue retained from an existing customer group, incorporating expansion, contraction, and churn over a period. It can exceed 100% when expansion outweighs losses, but that does not mean every customer stayed. |
Stripe’s guide distinguishes customer churn from revenue churn, describes LTV as predictive and dependent on historical data and assumptions, and treats net revenue retention (NRR) as a measure of the existing customer base. Its formula examples explain calculations; they are not representative benchmark data.
Use cohorts to find retention problems averages conceal
A company-wide retention average can mask customers who never activate, accounts that stop using the product after onboarding, or segments that retain well. Group customers by a meaningful starting rule—such as signup month—and compare their outcomes at the same elapsed time. Stripe also describes grouping by plan, region, acquisition channel, and early behavior.
Choose the cohort dimensions that could change a decision, and ensure each group has enough observations to interpret. For B2B products, decide whether the cohort is an account, seat, or user; for usage-based products, a usage cohort may be more informative than a logo count. Keep the retention outcome explicit: customer retention, product activity, gross revenue retention, or NRR answer different questions.
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Add acquisition economics when the signal is actionable
CAC and CAC payback become useful when the company can connect acquisition costs to a reasonably consistent stream of new customers. Segment by channel or customer type when the sample supports comparison, and use the same attribution window and cost definition. A fast-growing blended customer count can conceal a channel that is expensive or produces poor-retaining accounts.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchLTV is less certain for a young SaaS business because it forecasts future customer value from limited history and assumptions. Show the assumptions and compare like customer segments; do not present a precise-looking LTV:CAC ratio as a confident decision number when retention history is thin. Observed payback and cohort behavior can be more actionable while that history accumulates. Stripe’s guide is a current provider explanation of the metrics, not a universal threshold for healthy performance.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare like with like, and treat benchmarks cautiously
Before interpreting a change or comparing a benchmark, align the definition and context. At minimum, check:
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- Business motion: self-serve, product-led, sales-led, enterprise, or usage-based.
- Customer unit: user, account/logo, seat, or usage cohort.
- Retention view: customer, product activity, gross revenue, or net revenue retention.
- Customer group: signup month, plan, region, contract type, acquisition channel, or early behavior.
- Time horizon: compare cohorts at the same time since signup or contract start, not a mature group against a new one.
- Calculation rules: treatment of reactivations, billing failures, trials, discounts, refunds, services, and expansions.
Do not treat a familiar churn, growth, or LTV:CAC target as a universal law. A 2019 multi-vocal literature review by Kai-Kristian Kemell, Xiaofeng Wang, Anh Nguyen-Duc, Jason Grendus, Tuure Tuunanen, and Pekka Abrahamsson compiled more than 100 startup metrics, but the authors said the practitioner-derived suggestions were not empirically verified. That is a reason to be careful about generic thresholds, not a reason to discard metrics that help answer a well-defined business question.
Build a compact operating view
A useful early-stage view can be small: one current-stage question, a product-value measure, a retention view, and recurring-revenue movements once customers pay. Add acquisition economics when costs and outcomes can be connected reliably. For each number, record its definition, owner, cadence, segment, and the decision it informs. Keep exploratory analysis available, but do not confuse every collected event with a metric the team should manage.
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