Feature flags, experimentation, and product analytics used to gate and test pricing.
Statsig is a product development platform combining feature flags, controlled experimentation, and product analytics in one system. Engineering and growth teams use its gates to control feature exposure, its experiments to measure the impact of changes, and its analytics to follow the downstream metrics. In a revenue stack it shows up in two places: flags doing double duty as plan entitlements — gating features by tier — and the experimentation layer running pricing, paywall, and packaging tests with statistical rigor.
Which of the capability map's modules Statsig covers — each links to the module's own page, with every tool that supports it.
| Module | Phase | Depth | Note |
|---|---|---|---|
| Create Demand | |||
| Web & Product Analytics | Demand & Campaign Ops | Supported | |
| Win the Deal | |||
| Pricing Experimentation (A/B, Shadow Rating) | Configure & Quote | Core | rigorous A/B testing applied to pricing, paywalls, and packaging |
| Fulfill & Bill | |||
| Entitlement Management (Feature Flags, Caps, Access) | Fulfill & Activate | Supported | feature gates commonly repurposed to enforce plan-based access |
Statsig's distinctive move is fusing flags, experiments, and analytics on one event pipeline, so every rollout is automatically measurable — you do not stitch a flag tool to a separate stats engine to learn what a change did. Warehouse-native deployment options let teams run the same rigor on their own data infrastructure.
To a point. Feature gates express plan-based access rules cleanly, and many teams start there. What flags lack is billing awareness — usage caps, credit balances, and plan-change proration live in your billing layer, so flag-based entitlements need a sync between the two systems and a clear owner for the mapping.
Revenue metrics are high-variance and slow to mature — a pricing change shows up in conversion immediately but in retention and expansion months later. A platform with proper statistical guardrails, sequential testing, and warehouse-level metrics helps avoid shipping a price change on a noisy early read.