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Choose the identity that matches the stability you need: use a stable user ID to keep a logged-in person on the same variation across returns, a persisted anonymous ID for repeat visits before login, or a session ID to keep one checkout visit coherent. If users must remain in an already exposed experiment group after allocation or targeting changes, deterministic bucketing may not be enough; use a supported persistent-assignment feature and check its lifecycle rules.
Decide what “stable” means for checkout
Feature-flag assignment is reproducible only while the inputs to evaluation stay the same. A deterministic bucketing system can hash an identity with a rule-specific salt and map the result to a variation, but changing the identity or relevant experiment configuration can change the outcome. Statsig describes this identifier-and-salt approach in its evaluation documentation.
Before choosing a randomization unit, name the invariant your checkout needs:
- Same authenticated person on later returns: use a stable user identity.
- One coherent experience for a single visit, including login: consider a session identity and define how login changes the evaluation context.
- Keep an exposed experiment result despite allocation or targeting changes: use a supported persistent-assignment mechanism if the platform provides one.
These approaches solve different problems; none automatically guarantees all three.
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Choose the right identity
| Approach | Best suited to | Scope and limitation |
|---|---|---|
| User identity | Keeping the same logged-in person in the same variation on later returns | Can work across devices when the same stable user key is supplied. An anonymous-to-authenticated transition may change the variation if the evaluation identity changes. |
| Session identity | Keeping a checkout experience consistent within one visit, potentially through login | Usually limited to a browser/device session. A later session, another browser, or another device may have a different key and assignment. |
| Persisted assignment | Keeping a previously exposed experiment result when allocation or targeting changes | Depends on SDK support, storage, identity, and experiment lifecycle. It is not a substitute for choosing an appropriate user or session identity. |
User identity for returning logged-in customers
For customers who authenticate, evaluate the flag or experiment against a durable user identifier that remains the same across requests and logins. Avoid request IDs, newly generated IDs, or other values that change each visit. LaunchDarkly recommends user randomization when consistency for an individual over time is the priority; its guidance says logged-in people using the user randomization unit see the same variation when they return, assuming the relevant identity and experiment configuration remain suitable. See LaunchDarkly’s consistency guidance.
A stable user key does not by itself unify separate anonymous identities, nor does it guarantee continuity from the moment an unauthenticated shopper starts checkout. Decide what should happen at login rather than assuming the SDK will preserve the pre-login variation.
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Session identity for continuity through one checkout
A session identity can keep one visit coherent when the customer moves from anonymous checkout to login, provided the session identity is carried through the transition and the platform evaluates it as intended. It is generally a poor choice if the requirement is “the same person gets the same version every time they log in,” because session keys can differ across later sessions, browsers, and devices.
LaunchDarkly describes session keys as commonly stored in a cookie that expires after about 7–14 days. That is an approximate detail in its own guide, not a universal session-cookie lifetime; check the selected SDK and your application’s session behavior.
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Persisted anonymous identity for logged-out returns
If a logged-out visitor must receive a repeatable assignment in the same browser, use the SDK’s supported anonymous-context identity persistence where available, or implement an application-managed identity deliberately. LaunchDarkly says most of its client-side SDKs persist generated context keys in local storage, not cookies; storage can be disabled or cleared, and behavior varies by SDK. Consult its anonymous-context guidance and verify the exact SDK.
Browser-local persistence does not identify a person on another device. If a shopper must receive the same assignment on a phone and laptop, use a stable authenticated identity once known, or a vendor-supported way to associate anonymous and authenticated contexts.
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Handle anonymous-to-authenticated transitions
Choose which identity wins when a shopper logs in during checkout. If the anonymous assignment should continue for that visit, make sure the identity or context needed to preserve it remains available during the transition. If the authenticated user’s assignment should take precedence, expect the variation to change at login when that user evaluates differently.
For LaunchDarkly specifically, its documentation describes identifying a multi-context containing both the relevant anonymous and authenticated contexts at each evaluation, identify, or track call. The association does not persist between calls. This is vendor-specific behavior, not a general rule for feature-flag systems; other platforms may define context association differently.
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Keep assignments sticky when experiment settings change
Deterministic hashing gives a repeatable result for stable inputs; it does not inherently save a user’s previous experiment result. Changes to targeting, allocation, a rule’s salt or seed, or an experiment iteration can alter who receives each variation. LaunchDarkly warns that reducing allocation or stopping and restarting an iteration can move users between variations; see its traffic assignment guide.
If an already exposed customer must stay in the same experiment group through those changes, look for an explicit persistent-assignment feature and verify its storage and cleanup semantics. Statsig documents server persistent assignment using a custom storage adapter: an active experiment or layer evaluation can be saved on first evaluation and loaded on later evaluations. Its documentation lists Go, Ruby, Legacy Node, Node Core, Java Core, Kotlin, .NET, Python Core, PHP Core, and Rust Core server SDKs. It also says persisted values are deleted when omitted or when the experiment is inactive. Check the current documentation for the exact platform and version before depending on it: Statsig Server Persistent Assignment.
A practical implementation and validation sequence
- Write the invariant. Decide whether checkout needs continuity through one visit, consistency for the same logged-in person across returns, or persistence through experiment configuration changes.
- Set the evaluation identity. Use a durable authenticated user key for logged-in returners. For anonymous traffic, choose SDK-managed or application-managed identity based on the selected SDK’s documented behavior.
- Define login behavior. Decide whether the anonymous assignment continues or the authenticated assignment takes over, then implement the context transition using the vendor’s supported method.
- Plan for cross-device use. Do not rely on browser storage to connect separate devices. Use the authenticated key or a supported identity association when the same person needs a common assignment.
- Enable assignment persistence if necessary. Confirm platform and SDK support, storage adapter requirements, when saved values are read and written, and when they are deleted.
- Validate the real checkout flow. Test a first anonymous visit, a same-browser return, cleared storage, login during checkout, an authenticated return, a second browser or device, and a change to allocation or targeting.
Avoid identity patterns that break stability
- Do not generate a fresh anonymous key each visit. Returning visitors will appear to be new identities and can receive different assignments.
- Do not share one anonymous key among unrelated visitors. It can distort percentage rollouts and experiment results.
- Do not assume local storage always survives. Users, browser settings, privacy controls, or application behavior can clear or prevent it.
- Do not layer manual identity persistence over SDK-managed identity without checking for conflicts. The SDK may have its own key-management rules.
- Do not casually recreate or alter experiment rules when stable bucketing matters. Rule-specific salts, seeds, allocation, and iteration behavior can affect assignments.
Statsig’s documentation explains deterministic evaluation and rule-specific hashing in How Evaluation Works. The details of assignment persistence and context identity differ between vendors, SDKs, and versions, so confirm them for the system serving your checkout.
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