How to get the benefits of progressive delivery
Progressive delivery is a software development approach that decouples deployment from release, allowing teams to control who sees what features and when. Rather than releasing features to all users simultaneously in an all-or-nothing event, progressive delivery enables gradual rollouts where features can be shown to specific user segments while monitoring their impact.
Traditional deployment strategies meant that once code shipped to production, every user received the same experience. This created substantial risk. If something went wrong, the entire user base could be affected. Progressive delivery addresses this by using feature flagging systems that control feature visibility across different parts of the technology stack. If problems arise, features can be quickly rolled back through configuration changes without requiring new code deployments.
The practice emerged from the realization that companies operating at scale needed better ways to validate their work. Early adopters in the tech industry discovered that building features based solely on intuition or stakeholder requests often led to disappointing results. Testing hypotheses with real users and real data produced better outcomes. This shift from vision-driven to data-driven development fundamentally changed how successful product teams operate.
Technical infrastructure requirements
To implement progressive delivery effectively, organizations need specific technical capabilities. The most fundamental requirement is a feature flagging system that can reliably control feature visibility across different parts of your technology stack. Your mobile applications, web applications, backend services, and any other components must all be able to query whether a particular feature should be active for a given user.
The challenge becomes more complex when dealing with multiple platforms and versions. Consider a music education company with apps on iOS, Android, Windows, and macOS, plus a web presence. Users might have different app versions installed, some supporting new features and some not. The feature flagging system must account for these variations while maintaining a consistent user experience. If a user sees a particular pricing offer on their phone, they should see the same offer on the website, even if those systems are technically separate.
Real-time evaluation is critical for consistent feature behavior. Running evaluations close to your application ensures decisions stay fast and available even when network conditions or third-party dependencies fluctuate.
Create consistent user experiences
One of the most challenging aspects of progressive delivery involves maintaining consistency as users move between devices, log in and out, or travel between locations. The system must make intelligent decisions about how to identify users and which features they should see.
For features that require authentication, using account identifiers makes sense. A user discovers a new search interface on their iPhone while logged into their account. Later that day, they open the same app on their iPad. Because the feature flag system uses their account identifier to determine feature visibility, they see the same new search interface on both devices. This consistency builds trust and prevents confusion.
However, not all features should work this way. For experiments targeting the signup flow or other pre-authentication experiences, account identifiers do not exist yet. In these cases, device identifiers provide the stability needed. A user testing different signup button colors should see the same color every time they open the app on the same device, even if they reinstall the app or clear their data. The feature flag system must support this flexibility through configurable stickiness parameters.
Build on progressive delivery for experimentation
Progressive delivery provides the foundation for comprehensive experimentation programs. While basic feature flags allow teams to turn features on and off, experimentation requires additional capabilities. The system must be able to randomly assign users to different variants while ensuring that assignment remains stable over time. Statistical rigor demands that users consistently experience the same variant throughout an experiment’s duration.
The progression typically starts simple. Early experiments might test button colors or text variations. These tests require minimal technical infrastructure and provide quick feedback on whether the experimentation system works correctly. As confidence builds, teams naturally move toward more complex experiments involving entire screen redesigns or feature additions.
Develop features behind flags
One of the most powerful but underutilized applications of progressive delivery involves using feature flags during development itself. Rather than maintaining long-lived feature branches in version control, developers can merge code to the main branch frequently while keeping features hidden behind inactive flags.
This approach delivers multiple benefits. It eliminates the painful merge conflicts that arise when multiple developers work on separate branches for extended periods. It allows for continuous integration and deployment, since inactive flagged code poses no risk to production users. It enables early feedback from colleagues who can access pre-release features through targeted flag configurations.
Practical example: Gradual feature rollout
Consider a development scenario: a team building a new playlist feature for a music app. Using feature flags for development, they can start merging playlist-related code immediately, even though the feature is months away from public release. They set up the flag so it activates only for employees. As development progresses, coworkers use the playlist feature daily, providing feedback and catching issues. When development completes, enabling the flag for a small percentage of real users becomes a configuration change rather than a deployment event.
This also facilitates gradual rollouts once features are ready. Rather than releasing to the entire user base simultaneously, teams can start with a small percentage and gradually increase exposure as confidence builds. If problems emerge, rolling back is as simple as adjusting a flag configuration. No code deployment is required, no app store approval process, no waiting for users to update their apps.
Consequences of poor flag management
The consequences of poor flag hygiene manifest gradually. Developers must mentally track which flags are active, which are permanent, and which are obsolete. Code reviews become harder because reviewers must understand the flag context. Testing becomes more complex because each flag represents a potential code path. The cognitive load increases until it noticeably slows development velocity.
A team runs an experiment on a pricing screen redesign. The experiment concludes successfully, and they keep the new design. However, the flag remains in the code because removing it requires touching multiple components. Six months later, a new developer joins and sees code paths for both the old and new pricing screens. They waste time understanding why two implementations exist and which one actually runs. They might even inadvertently break the old code path, not realizing it is dead code that should have been removed long ago. (And cleaning up unused feature flags isn’t exactly an exciting task.)
Measuring the value of progressive delivery
Organizations investing in progressive delivery infrastructure and practices naturally want to quantify the return on that investment. The value manifests in multiple ways, some more tangible than others.
Avoiding bad releases
The most direct benefit comes from avoiding bad releases. Without progressive delivery, releasing a feature that harms key metrics affects the entire user base until the problem is discovered and fixed. With progressive delivery, the harm is contained to a small percentage during initial rollout. If the metrics decline, the rollout stops or reverses. A feature that would have reduced conversion by 5% for all users instead only affected 10% of users for three days before being rolled back. The company avoided 90% of the potential revenue loss.
Learning from successful experiments
Successful experiments provide another source of value. When teams discover that a new feature improves metrics, they gain confidence to invest further in that direction. They avoid wasting resources on approaches that testing revealed as ineffective. A product team might have five different ideas for improving user engagement. Rather than building all five and hoping for the best, they can build and test them sequentially. Ideas that show no improvement get killed early, saving the effort that would have gone into fully building and launching them.
Improving development velocity
Development velocity improvements from flag-based development are harder to quantify but substantial. Teams that can merge code continuously and deploy without fear spend less time on integration challenges and more time on feature development. They also get faster feedback from colleagues and users.