Experiment FlowThe Optimization Blog
Insights on A/B testing, multi-armed bandits, conversion optimization, and the science of experimentation.
LaunchDarkly Alternative: When You Need Experimentation, Not Just Feature Flags
LaunchDarkly is excellent for feature flag management but costs $75+/seat for basic experimentation. Here's a detailed comparison for teams who want proper A/B testing without the feature-flag overhead.
Product-Led Growth and Experimentation: How PLG Companies Win with A/B Testing
Product-led growth companies use experimentation differently. Learn how PLG teams run free-to-paid experiments, optimize in-product virality, and use multi-armed bandits to accelerate self-serve revenue.
The E-commerce CRO Playbook: 30 Tests That Move Revenue
A comprehensive conversion rate optimization guide for e-commerce, covering product pages, cart abandonment, checkout friction, category pages, search, and post-purchase. Includes specific A/B test designs.
Feature Flags vs. A/B Testing: What's the Difference and When to Use Each
Feature flags and A/B tests solve different problems. Learn when to use each, how they complement each other, and why conflating them leads to bad decisions and bloated tooling costs.
The North Star Metric: How to Find It, Measure It, and Align Your Experiments to It
Your North Star Metric is the single number that best captures the value your product delivers. Learn how to identify it, avoid common pitfalls, and use it to prioritize which experiments will have the biggest impact.
Experiment Velocity: How to Double the Number of Tests You Run
The teams that win at optimization aren't the ones who run the best individual experiments—they're the ones who run the most. Here's how to build an experimentation culture that moves fast and learns faster.
The Peeking Problem: Why Your A/B Test Results Might Be Wrong
Checking your experiment results too early is the most common statistical mistake in A/B testing. Learn why it inflates false positives, how to calculate proper sample sizes, and how Bayesian methods and sequential testing fix the problem.
A/B Testing for Startups: How to Experiment When You Don't Have Enough Traffic
Most A/B testing advice assumes you have millions of pageviews. Startups don't. Learn how early-stage teams can validate ideas, reduce risk, and make better decisions with limited traffic.
Growth Loops vs. Funnels: Why the Best Companies Think Differently About Growth
Traditional funnel thinking optimizes each stage in isolation. Growth loops create compounding, self-reinforcing systems. Learn how to identify your product's growth loops and design experiments that strengthen them.
AB Tasty Alternative: Modern A/B Testing Without the Enterprise Complexity
AB Tasty is a feature-rich enterprise platform with pricing to match. For teams who want powerful experimentation without the overhead, here's how Experiment Flow compares.
Pirate Metrics (AARRR): The Complete Growth Framework
Learn how to use AARRR pirate metrics to systematically improve acquisition, activation, retention, revenue, and referral. Includes specific A/B testing strategies for each stage of the funnel.
7 CRO Case Studies: Real Results from Conversion, Retention, and Churn Experiments
Seven detailed case studies showing how teams used A/B testing to improve conversion rates, boost retention, and slash churn—with real experiment setups and measurable results.
VWO Alternative: Why Growing Teams Switch to Experiment Flow
A detailed comparison of VWO vs Experiment Flow: pricing, SDK size, statistical methods, and multi-armed bandits. Find out why teams paying $199+/month for VWO are switching.
How to Reduce SaaS Churn with A/B Testing
A practical guide to using experimentation to cut churn: from onboarding flow tests to cancellation flow experiments, learn which A/B tests move the needle most on retention.
Improving User Retention Through Experimentation
How to use A/B testing and multi-armed bandits to improve Day 1, Day 7, and Day 30 retention rates. Includes specific experiment ideas and frameworks for measuring success.
Optimizely Alternative: Enterprise-Grade A/B Testing Without the Enterprise Price
Optimizely starts at $50K+/year. Experiment Flow offers the same statistical rigor, multi-armed bandits, and full-stack experimentation for $29/seat/month. Full comparison inside.
Pirate Metrics for SaaS: Applying AARRR to Grow Your Product
A SaaS-specific guide to AARRR metrics with benchmarks, common pitfalls, and the experiments that actually move each metric. Plus a framework for deciding which pirate metric to attack first.
Game Theory Meets the Scientific Method: How Strategic Thinking Accelerates A/B Testing
Combine game theory with the scientific method to model user incentives, control for variables, and reach conclusions faster using Thompson Sampling and strategic experiment design.
When Not to A/B Test: The Experiments You Shouldn't Run
Not every decision needs an A/B test. Learn when testing wastes time and money — from button color debates to testing things you already know are better — and how to spend your experimentation budget on changes that actually matter.
Google Optimize Is Gone: The Best A/B Testing Alternatives in 2026
Google Optimize shut down in September 2023. Here's our comprehensive comparison of the best A/B testing alternatives including Optimizely, VWO, LaunchDarkly, and why Experiment Flow is the top choice for modern teams.
Multi-Armed Bandits vs A/B Testing: When to Use Each
Learn the key differences between multi-armed bandit algorithms and traditional A/B testing, and when each approach maximizes your conversion rate.
Thompson Sampling Explained: The Bayesian Approach to Optimization
A practical guide to Thompson Sampling, the Bayesian bandit algorithm that balances exploration and exploitation to find winning variants faster.
The Complete Guide to Conversion Rate Optimization in 2026
Everything you need to know about CRO: from hypothesis generation to statistical significance, with practical examples and tools.
Statistical Significance in A/B Testing: A Practical Guide
Understand p-values, confidence intervals, and sample sizes without a PhD. Learn how to run experiments that produce trustworthy results.
Personalization at Scale: Using Contextual Bandits for Dynamic Content
How contextual bandits enable real-time personalization that adapts to each visitor's context, outperforming static A/B tests.
The Scientific Method of A/B Testing: From Hypothesis to Discovery
A/B testing is the scientific method applied to business. Learn how to form hypotheses, design controlled experiments, and draw valid conclusions from your data.
Inside Our Neural Contextual Bandit Engine
A technical deep-dive into how Experiment Flow's personalization engine uses neural networks, embeddings, and online learning to rank content for each visitor.
Gradient Clipping, Weight Decay, and LR Scheduling: Production ML for Personalization
How three classical ML techniques — gradient clipping, weight decay, and learning rate scheduling — make online personalization models more stable and accurate.
Online Personalization and Optimization with Machine Learning: A Technical Deep Dive
How modern ML models power real-time website personalization — from contextual bandits to neural networks, and the engineering that makes it work at scale.
Best A/B Testing Tools & Alternatives in 2026: Complete Comparison
Compare the top A/B testing platforms including Optimizely, VWO, LaunchDarkly, AB Tasty, and Experiment Flow. Find the right tool for your budget and needs.
How to Resolve A/B Tests Faster: Reducing Noise and Controlling Variables
Practical strategies to reach statistical significance faster: dimension reduction, variable control, sequential testing, and multi-armed bandits for quicker experiment resolution.
The Complete Guide to Running Successful Online Experiments
From hypothesis to results: learn how to design, run, and analyze online experiments that produce actionable insights. Covers sample size, segmentation, and common pitfalls.
What an Open-Source Trading Lab Teaches Us About Online Experimentation
How multi-armed bandits, XGBoost, and continual learning power both algorithmic trading and modern web experimentation — drawn from the open-source github.com/lee101/stock-prediction repo.
Quantitative Growth: How to Measure and Optimize Every Business Metric
A data-driven playbook for identifying your most important growth metrics, instrumenting measurement correctly, and running experiments that move them — from North Star metric to activation, retention, and revenue.
Email Marketing Optimization: A/B Testing Every Stage of Your Email Funnel
From subject lines to send times, segmentation to re-engagement flows — a complete guide to running controlled experiments on your email channel and compounding the gains.
Pricing Experiments: How to Find the Price That Maximizes Revenue
Pricing is the highest-leverage growth lever most teams never test. Learn how to run safe, legal pricing experiments — from price point testing to packaging and trial length — and use data to maximize revenue.
Landing Page Optimization: A Systematic A/B Testing Playbook
A step-by-step playbook for running A/B tests on landing pages — from hero headlines and CTAs to social proof and page speed — with a prioritization framework and real impact benchmarks.
SaaS Onboarding Optimization: Reducing Time-to-Value Through Experimentation
Activation is the most important metric most SaaS teams under-optimize. Learn how to run experiments that cut time-to-value, improve aha moment discovery, and compound trial-to-paid conversion.
Churn Reduction Experiments: How to Keep More Customers with Data
Reducing churn by even 1% can double your company's lifetime value. Learn how to identify at-risk users, run win-back experiments, and test retention interventions with a rigorous, data-driven approach.
E-commerce Conversion Optimization: Testing Every Stage from Browse to Buy
A complete A/B testing playbook for e-commerce — from product page copy and images to cart design and checkout flow — with impact benchmarks and a prioritization framework.
Viral Growth and Referral Loops: Engineering Word-of-Mouth with Experiments
Viral growth isn't luck — it's engineered. Learn how to design and test referral programs, sharing mechanics, and viral loops using A/B experiments, and how to measure virality with the K-factor.
How We Grew Camda: A Data-Driven Experimentation Story
How the Camda team used systematic A/B testing and user feedback across every channel — app stores, search, paid, and product — to compound growth through quantitative experimentation.
Mobile App Growth: A Complete Experimentation Framework for Apps
Mobile apps have unique growth levers — ASO, push notifications, in-app flows, and session depth. Learn how to build a systematic experimentation program that compounds mobile growth across every channel.
App Store Optimization: A/B Testing Your Way to More Downloads
Use A/B testing to optimize your app store listing — icons, screenshots, descriptions, and keywords — and compound your download growth with a systematic, data-driven approach.
SEO Growth Experiments: A Quantitative Approach to Search Optimization
Stop guessing at SEO. Learn how to run structured experiments on titles, meta descriptions, content structure, and page experience to compound your organic search growth.
Multi-Channel Traffic Optimization: Experimenting Across Every Growth Channel
A systematic framework for running experiments across paid, organic, social, email, and referral channels — and how to use quantitative data to allocate your growth budget to what actually works.
Turning User Feedback Into Growth: A Systematic Experimentation Framework
Learn how to collect user feedback at scale across every platform, turn qualitative signals into quantitative hypotheses, and build a continuous improvement loop that compounds growth.
Growth Hacking with A/B Testing: 10 Experiments That Actually Move the Needle
Ten proven A/B test ideas for startups — from landing page copy to pricing pages to onboarding flows — with implementation tips, what to measure, and realistic impact estimates.
Building a Culture of Experimentation in Your Company
How to transform your team from opinion-driven to data-driven — from getting leadership buy-in and democratizing access to data, to celebrating failures and building an experimentation velocity flywheel.
App Performance Optimization: Using Experiments to Improve UX and Retention
Performance is a product feature. Learn how to run controlled experiments on load times, UX flows, and in-app messaging to reduce churn and improve retention metrics.
The Growth Experiment Funnel: Optimizing Every Stage from Awareness to Retention
A complete framework for running experiments at every stage of the growth funnel — from first touch to loyal customer — using the AARRR pirate metrics as your optimization map.