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Measure retention by defining exactly who is being tracked, what counts as a meaningful return, when that return must happen, and which cohort members belong in the denominator. For crypto products, call an on-chain measure wallet retention unless you can reliably link wallets to accounts or people: an address is not proof of a unique human, and a transaction alone is not proof of continued value.
What should your retention metric measure?
Retention is a behavioral rate, not a property that an analytics tool discovers automatically. The cohort-entry event, qualifying return event, time bucket, observation window, and denominator are all part of the metric. Change one and you may change the result.
First decide whether you want to know if customers return to the product, if wallets return to a protocol, or if activity persists on a particular chain. Those are related but different questions:
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| Account retention | An authenticated product account | Whether accounts return to use product features, including activity that may not be visible on-chain. |
| Wallet retention | An address, or a clearly defined linked wallet cluster | Whether observed addresses return to perform qualifying on-chain actions; it does not establish that the same person controls each address. |
| Same-chain retention | A wallet’s activity on the chain where it first qualified | Whether activity continues on that chain; a move to another chain can count as inactivity here. |
| Cross-chain return | A wallet’s qualifying activity across the chains included in the analysis | Whether observed activity continues within the selected multi-chain scope, even if it moves from the origin chain. |
A person may use several wallets; a wallet may be shared or controlled by software. Treating one address as one person is an assumption, not a fact established by on-chain data. If you join wallet events to CRM, billing, or product events, record the identity-linking method and keep it consistent. Dune describes integrating on-chain analysis with those business data sources, but the integration itself does not prove a one-to-one relationship between addresses and people.
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How do you define a cohort that answers the right question?
Choose the entity and the entry event
Pick the entity whose return matters, then choose an entry event that represents the behavior you want to evaluate. A signup cohort measures what happens after onboarding; a first-value cohort asks whether someone who has reached a product outcome comes back. These cohorts have different denominators and should not be compared as if they were the same population.
- Account creation: useful for onboarding retention in an account-based app.
- First successful swap: useful for a decentralized exchange’s first-value retention.
- First deposit or borrow: useful for studying lending activation.
- First stake or proposal vote: useful for staking or governance participation.
Also state whether the cohort includes only first-ever entrants or anyone who performs the start event during each period. A first-ever cohort tracks new entrants; a recurring cohort can include returning actors who qualify again. PostHog documents these as distinct retention approaches.
What counts as a meaningful return?
Choose an action that indicates the user received or sought continuing value from your product. Depending on the product, that could be another successful swap, a deposit or withdrawal, a position adjustment, an NFT listing, a governance action, a completed payment, or return to a core app feature. State whether you count actions per account, wallet, transaction, successful call, active day, or other unit.
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Filter events that do not represent the behavior you are trying to measure when your data supports reliable identification. Failed transactions, system-generated calls, incidental contract interactions, and obvious spam can inflate activity counts. For incentive-driven products, decide whether airdrop or reward claims qualify; do not silently treat a claim as evidence of ongoing use.
Usage cadence matters. Amplitude’s usage-interval guidance notes that products have different natural frequencies of use, so a daily bucket can make an episodic product look less retained than it is. Select an interval that fits the product’s value cycle and use the same rule when comparing cohorts.
A concrete study rule is not a universal standard: CoinGecko Research used at least five successful transactions per wallet in Q1 2025 for its chain comparison, aiming to exclude one-off visitors, airdrop claimers, and single-session users. The study did not filter bot activity and counted later activity on another chain as churn from the original chain. Those choices describe that study’s method, not a recommended threshold for every crypto product.
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How do you calculate and label retention?
For a fixed cohort and period, calculate:
Period-N retention = distinct eligible cohort members with a qualifying return in period N ÷ eligible cohort size.
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Label the calculation mode because “return on” and “return on or after” are not interchangeable:
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| Rule | Who qualifies in period N? | Interpretation |
|---|---|---|
| Return on | Cohort members with a qualifying event in that exact period | Exact-period activity; a member active in an earlier or later period does not count for N unless active in N. |
| Return on or after | Cohort members with a qualifying event in period N or any later observed period | Cumulative from N onward; it will not answer the same question as exact-period retention. |
State whether buckets are rolling elapsed windows or calendar days, weeks, or months. Calendar cutoffs and time zones can move events between buckets; rolling windows instead measure elapsed time from each cohort member’s entry. Analytics systems such as PostHog and Adobe Customer Journey Analytics describe retention in terms of selected start events, return events, and measurement periods.
Keep the denominator fixed to the eligible cohort size under the chosen definition. Show cohort size and maturity alongside the rate. Do not compare a fully observed 30-day cohort with a new cohort that has only had 12 days to return, or include immature cohorts in a 30-day aggregate as though they had a full observation window.
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How should you define churn?
For many crypto products, a team cannot observe whether an address owner deliberately left. Define churn as an operational rule—no qualifying return during a specified inactivity window—and label it inactivity-based churn or inactivity. Choose the window according to expected product usage and the time it normally takes for the product to deliver value, rather than selecting 7, 30, or 90 days by habit.
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If the threshold materially changes decisions, show how the classification changes under more than one reasonable inactivity window. Report explicit cancellation or account closure separately when the product records those events. An address with no qualifying transaction during the observation window is not evidence of intent to abandon the product.
Keep the scope explicit. A wallet that stops using a protocol on its origin chain but becomes active on another chain is churned for a same-chain metric, but may still be active in a cross-chain view. State which chains and products are included rather than treating movement outside the observed scope as ecosystem-wide departure.
How can you diagnose retention without confusing it with value?
Once definitions are stable, compare cohorts by meaningful dimensions such as acquisition source, onboarding completion, first value action, feature, chain, geography where lawful and relevant, and account or user type. Keep the unit, start event, return rule, time basis, scope, and filters fixed across the comparison; otherwise, differences may reflect changed measurement rather than changed behavior.
Use segments to find associations worth investigating, not to claim causation. If users who complete onboarding retain better, that alone does not establish that onboarding completion caused the improvement; an experiment or appropriate causal design is needed for that conclusion.
Behavioral retention is also distinct from transaction volume, total value locked, and revenue. If the business depends on fees or subscriptions, pair user or account retention with retained revenue or cohort revenue rather than substituting one for the other. Product analytics platforms can support cohort breakdowns and revenue views, but those measures answer different questions.
What should every retention chart disclose?
- The tracked entity: account, wallet, device, or linked identity, including how identities are joined.
- The cohort start event and whether entrants are first-ever or recurring.
- The return event, success criteria, activity threshold, and exclusions for bots, incentives, failed events, or system calls.
- The calculation rule: return on or return on or after; the time bucket and timezone or rolling-window basis.
- The observation window, inactivity threshold if reporting churn, and whether cohorts are fully mature.
- The denominator, cohort size, chain or product scope, and any cross-chain treatment.
There is no universal crypto-product retention or churn benchmark established by the available examples. A useful result is one whose definitions are explicit, stable, and tied to the product’s expected value—not one compared with an unrelated protocol’s threshold.
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