What is an anonymous flag?

Anonymous flags are a powerful tool in feature management that enable targeted experimentation and gradual rollouts for users who haven't logged in or provided identifying information. Unlike user-based flags, which rely on known user IDs, anonymous flags assign temporary identifiers to track and manage feature access for anonymous users.

The purpose of anonymous flags is to allow feature testing and controlled releases for a broader audience, including first-time visitors and users who haven't created an account. By assigning consistent identifiers within a session or device, anonymous flags ensure that users receive a coherent experience, even without a persistent user ID.

Anonymous flags play a crucial role in gradual rollouts and A/B testing. They allow you to test new features on a subset of your anonymous traffic, gather data, and make informed decisions before rolling out changes to your entire user base. This approach minimizes risk and enables data-driven optimization of your features.

By leveraging anonymous flags, you can gain valuable insights into how new features perform across your entire user base, including those who haven't logged in. This comprehensive approach to feature management ensures that you're making informed decisions based on a representative sample of your users, leading to better product development and user experiences.

When experimenting with anonymous flags, choose your traffic type thoughtfully:

• Use anonymous traffic type for features everyone sees, logged-in or not

• Use user traffic type for features only shown to logged-in users

• Avoid experimenting on the same feature across both traffic types simultaneously

Client-side SDKs require separate SDK clients for anonymous and logged-in traffic types. Server-side SDKs are more flexible; just pass the appropriate ID to getTreatment().

Use cases for anonymous flags

Anonymous flags are a powerful tool for experimenting with and personalizing experiences for users who have not logged in or identified themselves. Here are some common use cases:

Testing new features on first-time visitors

When launching a new feature, you may want to test its impact on engagement and conversion rates for first-time visitors. By using an anonymous flag, you can randomly assign new users to different variations of the feature and measure the results.

This allows you to optimize the onboarding experience without affecting existing users. It also helps you gather data on how the feature performs for users who are not yet familiar with your product.

Implementing gradual rollouts for public-facing changes

If you're making significant changes to your website or app's public-facing interface, you may want to roll them out gradually to minimize risk and gather feedback. Anonymous flags enable you to show the new version to a small percentage of users at first, then ramp up the rollout over time.

This approach is particularly useful for changes that could potentially disrupt the user experience or cause confusion. By monitoring key metrics and user feedback during the rollout, you can make adjustments as needed before exposing the changes to your entire user base.

Personalizing experiences for anonymous users

Even if a user hasn't logged in or provided personal information, you can still tailor their experience based on factors like location, device type, or referral source. Anonymous flags allow you to create targeted variations of your content or features and serve them to specific segments of anonymous users.

For example, you might show different homepage hero images to users in different countries, or display a special offer to users who arrived from a particular ad campaign. By personalizing the experience for anonymous users, you can increase engagement and conversion rates without requiring users to create an account or log in.

Best practices for implementing anonymous flags

When working with anonymous users, it's crucial to choose appropriate identifiers. Session IDs, device IDs, or randomly generated UUIDs can serve as anonymous user keys. Ensure these IDs are unique and consistent within the user's session or device.

Balancing consistency and randomization is key for anonymous flags. You want users to have a consistent experience during their session while still ensuring proper randomization for experiments. Store the assigned treatment in a cookie or local storage to maintain consistency across page views or sessions.

Handling edge cases and potential conflicts is important to maintain data integrity. If a user logs in after being assigned an anonymous treatment, decide whether to keep the anonymous treatment or assign a new treatment based on their logged-in ID. Implement clear rules to avoid conflicts between anonymous and logged-in treatments.

When tracking events for anonymous users, send the anonymous ID alongside the event data. This allows you to analyze the behavior of anonymous users separately from logged-in users. If a user later logs in, you can associate their anonymous events with their logged-in ID for a complete view of their journey.

Be mindful of privacy concerns when working with anonymous IDs. While they don't contain personally identifiable information, it's still important to handle them securely. Avoid sending anonymous IDs to third parties unnecessarily and ensure compliance with privacy regulations like GDPR or CCPA.

Testing and monitoring are crucial when implementing anonymous flags. Thoroughly test your implementation to ensure consistent treatment assignment and event tracking. Monitor your experiments closely to detect any anomalies or unexpected behavior related to anonymous users.

By following these best practices—choosing appropriate identifiers, balancing consistency and randomization, handling edge cases, tracking events accurately, prioritizing privacy, and testing thoroughly—you can effectively implement anonymous flags and run experiments that include both anonymous and logged-in users.