Unlock new possibilities for 1:1 personalization
Learn about the top benefits of A/B testing that your business should be taking advantage of in 2024.
Picking the right tool for building personalization
How to make a strategic decision and look for alignment, not just a laundry list of functionality
A "How To" Based On My Lessons Learned In the Trenches
How to avoid the pitfalls of peeking at A/B tests without flying blind
Measuring impact when you cannot hold out a control group
How many engineers will you commit to internal tool buildouts?
Don't let these violations invalidate your experiment results
A holistic comparison of statistical methods for online experimentation
Ensure core metric definitions are in sync across experimentation in Eppo, BI, and other data platforms, all managed via GitHub
Accurately measure the cumulative impact of your experimentation program
Holdouts help measure the cumulative, long-term impact of an experimentation program. But getting value out of holdouts requires scale and maturity that not all organizations are ready for.
When does it make sense to bandit, and when to experiment?
Some of my personal takeaways and highlights from a slightly bigger, seemingly more diverse CODE conference.
You can’t just say “we should take a scientific approach.” That won’t work.
Optimizing search ranking algorithms can drive millions of dollars in revenue. Here's how to A/B test them.
Are mutually exclusive experiments necessary, or dangerous? Here's how to run them in Eppo.
Shailvi Wakhlu explains why machine learning and AI products require experimentation to quantify success.
It’s time for experimentation tools to integrate directly with the CMS instead of trying to imitate them