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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Measure retention by first defining who counts, what behavior qualifies as a return, when the return must happen, and which cohort members belong in the denominator. In a crypto product, an on-chain address is not automatically a person, and a transaction is not automatically evidence of meaningful use. Treat your definitions as part of the metric, not as back-end implementation details.
Choose what is returning
Start with the entity whose continued use matters to the product. The best unit depends on how customers use it and what your data can reliably identify.
| Unit | Useful when | Important limitation |
|---|---|---|
| Authenticated account | A custodial exchange or account-based app can observe signed-in activity. | One person may have more than one account unless identities are linked. |
| Wallet address | A permissionless protocol can observe on-chain activity by address. | An address is a wallet-level proxy, not proof of one human: a person may control several wallets, and a wallet may be shared or automated. |
| Linked wallet cluster | A product has a documented, defensible method for linking addresses to an account or identity. | The linking method can be incomplete or change over time; disclose it and keep it consistent across reporting periods. |
| Device | Device-level behavior is meaningful and the product can identify it consistently. | Device counts do not necessarily equal account or person counts. |
Dune describes combining on-chain activity with CRM, billing, and product data for a fuller view. That integration can enrich analysis, but it does not by itself establish that each address maps one-to-one to a person. If you cannot link identities reliably, label the result wallet retention or address retention rather than user retention.
Set the cohort entry event
A cohort is the set of eligible entities that begin the behavior you want to study. Choose a start event that matches the question; different entry events create different populations and denominators.
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| Question | Possible cohort start | What the cohort represents |
|---|---|---|
| Do new accounts return after onboarding? | Account creation | New accounts, whether or not they reach a value-bearing action. |
| Do DEX users repeat a core action? | First successful swap | Entities that reached an initial swap, not every signup or connected wallet. |
| Do lending users continue after activation? | First deposit or borrow | Entities that began lending activity. |
| Do governance participants return? | First proposal vote | Entities that participated in governance at least once. |
Also decide whether the cohort consists of first-ever actors or anyone who performs the start event during each reporting period. First-ever cohorts track new entrants; recurring cohorts can include returning actors who meet the start condition again. PostHog documents these as distinct retention options. Do not compare results from these two cohort designs as though they had the same denominator.
Define a return that signals value
Choose a return event that reflects continued use of the product’s core value, and specify the required threshold. Depending on the product, that might 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.
- Count the unit that fits the question: distinct wallets, successful transactions, successful calls, active days, or value-bearing events.
- Where the data supports reliable filtering, exclude failed transactions, system-generated activity, incidental contract calls, and obvious spam.
- Document how you handle bots and activity driven by incentives or airdrops. A burst of low-intent transactions can inflate activity without showing that users found ongoing value.
Match the observation interval to the product’s natural usage cadence. A daily-use trading tool and an episodic governance product may require different intervals; Amplitude’s usage-interval documentation emphasizes that frequency affects how retention should be interpreted.
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One illustrative threshold is study-specific, not a universal standard: CoinGecko Research’s 2026 chain comparison counted wallets with at least five successful transactions in Q1 2025. It used the threshold to exclude one-off visitors, airdrop claimers, and single-session users, but did not filter bot activity. Its rule should not be imported as a default without considering your product and data.
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Choose the return rule and time bucket
“Return on” and “return on or after” describe different outcomes. Label which one a chart uses; otherwise, a rising-looking retention curve may be misunderstood or compared with an incompatible result.
| Rule | What counts | Question answered |
|---|---|---|
| Return on period N | A qualifying event occurs in that exact period. | What share came back during this particular interval? |
| Return on or after period N | A qualifying event occurs in period N or any later period in the observation window. | What share returned by this point, including later returns? |
Specify whether periods are rolling elapsed windows or calendar days, weeks, or months. Rolling 24-hour periods differ from calendar-day buckets, and time zones and event timestamps can move an event across a boundary. PostHog and other product analytics documentation describe retention as a configurable relationship between start events, return events, and measurement periods; Adobe Customer Journey Analytics likewise frames the analysis around events, durations, and cohorts.
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Calculate and report the rate
For a fixed cohort and period, calculate:
Period-N retention = distinct eligible cohort members with a qualifying return in period N ÷ eligible cohort size.
For an “on or after” measure, the numerator instead counts distinct eligible cohort members who return in period N or later within the defined observation window. State the rule alongside the rate, and keep the denominator tied to the cohort definition rather than changing it after observing who returned.
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Define churn as an observable operating rule
For many crypto products, the team cannot see whether an address owner intended to leave. Define churn as inactivity-based or operational churn: no qualifying return during a specified inactivity window. Choose that window to fit the expected use frequency and product value cycle; do not adopt seven, 30, or 90 days merely because the interval is familiar. If the classification affects decisions, compare more than one reasonable window to see how sensitive the churn count is.
Keep explicit cancellation or account closure separate when the product can observe it. An account closure is a recorded event; a wallet with no qualifying transaction during the window is only inactive under your rule. Interval-based dormant-user classifications in analytics frameworks illustrate why “dormant” also depends on a chosen interval.
For an on-chain product, specify the scope of the inactivity measure. CoinGecko Research’s 2026 comparison treated a wallet that moved to another chain as churned from its origin chain. That is appropriate for an origin-chain retention question, but it does not establish that the wallet left the broader product or ecosystem.
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| Measure | What continued activity means |
|---|---|
| Product-account retention | The linked or signed-in account returns to the product under the account’s event rules. |
| Same-protocol retention | The observed entity performs qualifying activity in the protocol again. |
| Same-chain retention | The observed entity returns on the chain being measured; activity elsewhere does not satisfy this rule. |
| Cross-chain return | The observed entity returns on any chain included in the stated ecosystem scope. |
Use segments to diagnose differences, not to claim causes
Once definitions are fixed, compare cohorts by acquisition source, onboarding completion, first value action, feature, chain, and account or user type. Use geography only where it is lawful and meaningful. A single aggregate curve can hide distinct activation patterns or a chain-specific drop-off.
If the business earns fees or subscriptions, pair behavioral retention with retained revenue or cohort revenue rather than replacing one with the other. Transaction volume and total value locked (TVL) describe activity or capital, not how many eligible accounts or wallets returned. Amplitude’s N-Day LTV material treats cohort revenue as a separate measure that can complement behavioral retention.
A feature or onboarding action associated with higher later retention is a useful hypothesis, not proof that the action caused retention. Establishing causation requires an appropriate experiment or causal design. Keep definitions stable when comparing periods, and disclose changes to instrumentation, chain coverage, identity resolution, or filtering that could alter who qualifies.
What to put on a retention chart
- The unit of analysis and any identity-linking method.
- The cohort start event, and whether cohorts are first-ever or recurring.
- The return event, activity threshold, and exclusions for unsuccessful, automated, or incentive-driven activity.
- The return rule: exact period or period-and-later.
- The time bucket, time zone, product or chain scope, and inactivity window where churn is shown.
- Cohort size, observation maturity, and whether any period is incomplete.
There is no universal crypto-product retention or churn benchmark established by the sources cited here. A benchmark is useful only when the unit, cohort, activity rule, time basis, and scope are sufficiently comparable.
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