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Start by deciding what “retained” means
A retention result is the outcome of a measurement rule, not a self-explanatory property of a product. At minimum, specify the entity being counted, how it enters the cohort, the qualifying return event, the time bucket, and the denominator. Google Analytics’ cohort guidance and product analytics documentation from PostHog describe cohorts in terms of users grouped by a shared start point and their subsequent activity; changing those definitions changes the result.
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Keep behavioral retention separate from related measures. A retained account, an active wallet, a returning transaction, retained revenue, and total value locked are not interchangeable outcomes.
Choose the unit of analysis
Use the entity whose continued use matters to the product, and name it on every chart. The natural choice depends on how the product works:
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| Unit | Useful when | What the metric establishes |
|---|---|---|
| Authenticated account or customer | A custodial exchange or account-based app can reliably associate activity with signed-in accounts. | Return of an account under the product’s identity model, not necessarily one distinct human. |
| Wallet address | A permissionless protocol observes actions on-chain but has no reliable account link. | Return of an address. One person may control several addresses; an address may be shared or automated. |
| Linked wallet cluster | The product has a documented, defensible method for associating addresses. | Return of the linked cluster under that method, which may be imperfect and should remain consistent over time. |
| Device | The question concerns continued use on a device and device identity is stable enough for the purpose. | Return of the tracked device, not necessarily the same person or account. |
Do not label addresses “users” without qualifying the term. On-chain records identify addresses and actions; they do not establish a one-to-one mapping to people. Dune describes combining protocol activity with CRM, billing, and product data for a fuller view, but data integration by itself does not prove identity.
Define the cohort entry event
Choose a start event that matches the behavior you want to retain. Account creation measures whether onboarding leads to later use; a first successful swap measures whether a DEX user returns after a first value action. A lending product might start a cohort at a first deposit or borrow, staking at a first stake, and governance at a first proposal vote. Each creates a different population and denominator, so their retention rates should not be compared as though they describe the same cohort.
Also say whether the cohort includes only first-ever actors or anyone who performs the start event during the period. A first-ever cohort tracks new entrants. A recurring cohort can include actors who qualify again in multiple periods. PostHog documents these as distinct retention options; silently switching between them makes a trend difficult to interpret.
Set a return event that signals value
Pick an action that reflects continued use of the product’s core value, not merely an event that is easy to count. Depending on the product, a return could be another successful swap, a deposit or withdrawal, a position adjustment, an NFT listing, a governance action, a completed payment, or use of a core app feature. State whether you count by address, transaction, successful call, active day, or another explicit unit.
Where instrumentation allows, exclude failed transactions, system-generated calls, incidental contract interactions, and obvious spam. A contract call or airdrop claim may show activity without showing that someone returned for product value. Set an activity threshold only when the product needs one, and explain why it is appropriate.
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Use study thresholds as examples, not standards
CoinGecko Research’s 2026 chain comparison treated a wallet as active if it made at least five successful transactions during Q1 2025, aiming to exclude one-off visitors, airdrop claimers, and single-session users. The study did not filter bot activity, and it counted later activity on another chain as churn from the wallet’s original chain. Those are that study’s methodological choices, not a universal threshold or a recommended default for every crypto product.
Choose the time bucket and calculation
A simple period-N retention rate is:
Period-N retention = distinct eligible cohort members with a qualifying return in period N ÷ eligible cohort size.
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesUse a fixed denominator for the cohort as defined. Display the cohort size, the start event, and how mature the cohort is—how much of its observation period has elapsed. Do not compare a fully observed 30-day cohort with a recent cohort that has had only 12 days to return, or include the latter in a period-N aggregate before it can be observed for the full period.
Two common return rules answer different questions:
| Rule | Meaning | Best used to answer |
|---|---|---|
| Return on | A qualifying event occurs in that exact period. | How many cohort members came back during this period? |
| Return on or after | A qualifying event occurs in that period or any later period. | How many had returned by this point or subsequently? |
“Return on or after” is cumulative from that point forward; it is not directly comparable with exact-period retention. Label the rule on each chart and in each comparison. PostHog and Amplitude documentation describe configurable start and return events and differing retention calculation modes.
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Specify whether buckets are rolling elapsed windows or calendar periods such as days, weeks, or months. A rolling 24-hour window and a calendar day can assign the same event differently; calendar boundaries also depend on the chosen time zone. Choose an interval that fits expected usage. Amplitude’s usage-interval guidance notes that products have different natural usage frequencies: a daily-use trading tool and an episodic governance product need not use the same cadence.
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Define churn as an observable operating rule
For many crypto products, a team cannot observe whether an address owner intended to leave. Define churn as inactivity-based or operational churn: no qualifying return event during a specified inactivity window. Choose that window based on the product’s expected usage frequency and value cycle rather than applying an arbitrary 7-, 30-, or 90-day threshold. If the classification materially affects decisions, compare results under more than one reasonable window.
Keep explicit cancellation or account closure separate when the product can observe it. An account closure is an observed event; an address with no qualifying transaction in the selected window is observed inactivity, not proof of abandonment. Analytics systems’ dormant-user classifications likewise depend on a selected interval, as illustrated in Amplitude’s Retention Lifecycle Framework.
Separate product, chain, and ecosystem scope
For on-chain products, make the scope explicit. Same-protocol retention asks whether an address returns to the protocol; same-chain retention asks whether it remains active on that chain; cross-chain return asks whether it continues in the broader multi-chain product or ecosystem. A wallet that migrates chains can count as churned under an origin-chain rule while still returning to the product elsewhere. CoinGecko Research’s example used the former interpretation, so its chain-level result should not be read as ecosystem abandonment.
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Once definitions are stable, compare cohorts by factors that can help explain different outcomes: acquisition source, onboarding completion, first value action, feature, chain, and user or account type. Include geography only where lawful and meaningful. Keep event filters, identity rules, and cohort definitions consistent across the comparison.
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A pattern is not a cause. If users who complete a particular onboarding step retain better, that association does not prove the step caused higher retention; test the change with an appropriate experiment or causal design before making that claim.
If the business depends on fees or subscriptions, pair behavioral retention with retained revenue or cohort revenue. Amplitude’s N-Day LTV material treats cohort revenue as a separate measure that can complement behavioral analysis. Do not substitute transaction volume or TVL for the number of returning accounts or wallets: they answer different questions.
A practical specification for every retention report
Before publishing or acting on a chart, record these choices alongside it:
- Entity: account, customer, device, address, or linked wallet cluster.
- Cohort entry: the exact start event and whether the cohort is first-ever or recurring.
- Return rule: the qualifying event, success criteria, activity threshold, and exclusions for bots, incentives, or noise where reliably identifiable.
- Time basis: rolling or calendar buckets, interval length, and time zone.
- Calculation: return on or return on or after, eligible denominator, cohort size, and maturity requirement.
- Scope: account, product, protocol, chain, or multi-chain ecosystem.
- Churn rule: inactivity window, plus any separately observed cancellations or closures.
- Interpretation: the outcome measured—behavioral return, revenue, transaction activity, or another value metric.
There is no universal crypto-product retention or churn benchmark established by these measurement methods. A rate is useful when its definitions fit the product, remain stable enough to compare, and are visible to the people interpreting it.
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