Experiments
Feature flags, A/B testing, and progressive rollout — ship with confidence.
What the Experimentation Platform Provides
Bosca's experimentation platform gives you a complete toolkit for controlling how features reach your users. Instead of shipping a change to everyone at once and hoping for the best, you can gradually introduce features, measure their impact, and make data-driven decisions about whether to keep them.
The platform brings together four capabilities that work as a unified system:
- Feature flags let you control who sees what, without requiring code changes or redeployments.
- A/B experiments attach measurement to feature flags so you can determine which variation performs best.
- Exclusion layers prevent users from being in multiple experiments at once, keeping your results clean.
- Rollout policies automate the process of gradually shifting traffic to a winning variation, with safety checks along the way.
How It All Fits Together
Feature flags are the foundation. An experiment is simply a flag with measurement attached. Rollout policies control how quickly a winning variation reaches everyone. And exclusion layers keep concurrent experiments from interfering with each other.
Here is the typical workflow from idea to full rollout:
- Create a feature flag with two or more variations (for example, the current experience and the new design you want to test).
- Add targeting rules to control which users see which variation — by segment, user attributes, device properties, or other criteria.
- Attach an experiment to the flag and define what success looks like by setting conversion goals.
- Measure results as the experiment runs. The platform tracks assignments and conversions automatically and produces statistical analysis.
- Roll out or roll back based on the results. Use a rollout policy to gradually shift all traffic to the winning variation, or disable the flag if the change did not perform well.
Key Concepts at a Glance
| Concept | What It Does |
|---|---|
| Feature Flag | Controls what users see. Each flag has a type (on/off, percentage, text, or structured data), a set of variations, and a default value. |
| Variation | One of the possible values a flag can return. For a simple toggle, this is "on" or "off." For other types, it can be any value you define. |
| Targeting Rule | A condition that determines which variation a user receives. Rules are checked in order — the first match wins. |
| Experiment | A flag with statistical measurement attached. Experiments track who sees what and whether they convert, then determine which variation performs best. |
| Exclusion Layer | Ensures a user is in at most one experiment per layer, preventing interference between concurrent tests. |
| Rollout Policy | Controls how traffic is gradually shifted to the winning variation, with automatic safety checks that can halt the rollout if something goes wrong. |
| Analysis Report | Statistical results for an experiment, including confidence levels, performance comparisons, and optional AI-generated insights with recommendations. |
Explore Each Area
Dive deeper into each part of the experimentation platform:
- Feature Flags — Flag types, variations, targeting rules, how evaluation works, and live exposure tracking.
- A/B Experiments — How experiments work, conversion goals, user assignment, and experiment lifecycle.
- Exclusion Layers — Preventing users from being in multiple experiments at once.
- Rollout Policies — Gradually rolling out winning variations with manual, scheduled, and adaptive modes.
- Results & Analysis — Statistical analysis, variance reduction, and AI-powered insights.