Blog

Donnu A/B Blog

Complete, honest guides on CRO, A/B testing and experimentation. The most complete articles on the web, no fluff. (page 13 of 17)

Feature Flags

Feature Flags for Mobile Apps: A Practical Guide

Feature flags for mobile apps: kill switches, staged rollouts, remote config caching, and how to dodge App Store review pitfalls on iOS and Android.

Growth Experimentation

Growth Experimentation for SaaS: The Complete PLG Playbook

Growth experimentation for SaaS: the activation funnel, ICE prioritization, funnel statistics, and the full PLG playbook for growth teams.

Feature Flags

Feature Flag Tools Compared: LaunchDarkly, Unleash & More

A neutral comparison of feature flag tools: LaunchDarkly, Unleash, Flagsmith and PostHog by hosting model, pricing, and A/B testing integration.

Feature Flags

How to Implement Server-Side A/B Testing (Step by Step)

How to implement server-side A/B testing: SDK setup, deterministic hashing, cross-service parity, A/A validation, and QA before you trust the pipeline.

Feature Flags

Progressive Rollouts and Canary Releases Explained

Progressive rollout vs canary release: what each means, guardrail metrics to watch, rollback triggers, and how this differs from A/B testing.

Feature Flags

Client-Side vs Server-Side A/B Testing Compared

Client-side vs server-side testing explained: how each works, the flicker problem, SEO and Core Web Vitals impact, and when to choose which.

Feature Flags

What Is a Feature Flag? The Complete Guide for Product Teams

What is a feature flag? Learn the 4 flag types, safe rollout strategies, and how to avoid feature flag technical debt in your product.

Feature Flags

Feature Flags vs A/B Testing: The Real Difference

Feature flags vs A/B testing: what each one solves, when to use just one, and how to combine controlled rollout with real statistical rigor.

Statistics

Multi-Armed Bandits for Email Subject Lines

Multi-armed bandits in email: auto-optimize subject line features are a simple bandit, not a rigorous A/B test. See the sample size math.