Experimentation Program

Preference Testing vs A/B Testing for B2B SaaS

Most B2B teams settle headline arguments in Slack because the page doesn't have the traffic to settle them any other way. A page converting at 3% needs about 103,000 visitors to detect a 10% lift. This post covers where preference testing beats A/B testing, where it quietly misleads you, and the three checks to run before you buy a panel.

The Experimentation Center of Excellence, Deconstructed

Ten experimentation leaders, from Meta to Uber to a Heineken subsidiary, describe their Center of Excellence the same way. It's a bridge, not a destination, and this post covers what changes once yours has done its job.

Decision Velocity: Why One Confidence Bar Fails

Most experimentation programs apply the same 95% confidence bar to every test, a CTA colour and a pricing model alike. That backs up queues and drags low-risk tests behind high-risk ones for no good reason. This piece breaks down Decision Velocity: matching your confidence threshold to the actual risk of each decision, set before anyone sees the data.

How To Use AI for Better Ideation in Mature Experimentation Programs

Mature experimentation programs don't stall from a lack of ideas, they stall because years of test history sit in decks nobody reopens. This post walks through the AI-assisted workflow, building memory layers in Airtable and NotebookLM, then turning that memory into fast, relevant test ideas with Gemini, while keeping a human step to stress-test what actually ships.

Three Questions Every Test Should Answer Before It Launches

Most programs can size a test. Far fewer can tell you what it serves or what it actually proves. This piece breaks down the three questions every test should answer before it launches, and the one tree that answers all three at once.

Scaling Wins: Why Experimentation is the Future of Business Growth

With endless competition, mountains of data, and countless options to think about, businesses face a critical challenge: How do you make smart, decisive moves that keep you ahead of the pack, without getting bogged down by complexity or losing sight of your goals? And then how do you do this for teams of people, how do you scale this approach?

Why Mature Programs Grow Faster: Speero + Kameleoon Original Research

Businesses with mature testing programs are 69% more likely to grow significantly. Read to find out why and how you can do it yourself.

CoE Interviews Part 2: Three Key Steps to Launching CoE

Learn how to build CoE model through defining and standardising the experimentation model, planning for people, and establishing governance.

How to Scale Experimentation in Different Structures, Organization Designs, and Operating Models

Learn how each operational and organizational model behaves and influences your scaling efforts. Understand their pros and cons and you'll scale faster, even without increasing headcount. Better still, you won't stress yourself with limitations in your current approach. Knowledge is power.

13 SaaS Pricing Models Explained (2026)

Learn more about how to test and optimize 13 SaaS pricing models, including needed research and their strengths and flaws.

How to Create a Revenue-based Experimentation Program For eComm

Learn more about how to create a revenue-based experimentation program for ecomm, quick and easy. A simple way to ensure you speak in C-level language—revenue.

Running Concurrent Experiments

Goal Tree Maps for Experimentation Programs 

Most teams can agree on a top-level number and still pull in different directions every quarter. A goal tree map fixes that: break your BHAG into business KPIs, then tactical and engagement metrics, so every experiment traces back to something specific instead of a vague "improve conversion rate" ask. This guide walks through building one from scratch, plus how to turn it into prioritized strategies and tactics.

Building an Experimentation Program Requires A Strong Foundation

The State of Experimentation Programs 2023