I've been running A/B tests on email subject lines for years, but as our list sizes have grown (we're sending to ~500k active subscribers), I'm hitting some statistical walls. The classic "split the list 50/50 and send with two subjects" feels increasingly inefficient and slow for learning.
My core question: what's the best *operational* method to test more variants, faster, without sacrificing statistical significance? I'm especially interested in approaches for businesses sending 100k+ emails per campaign.
A few things I'm considering:
- **Multivariate/Headline Testing Tools:** Platforms like Sentium or native features within ESPs (like HubSpot's multi-variant) that let you test more than two options simultaneously. Anyone have experience with the reliability of these on large lists?
- **Sequential Testing:** Sending the "winning" subject from a small, initial sample to the majority of the list. The risk here seems to be audience fatigue if you're sending the same campaign content multiple times.
- **Bayesian vs. Frequentist:** Some newer tools use Bayesian statistics, which can declare a winner faster with large sample sizes. Is anyone using this in practice for email?
- **Infrastructure for Scale:** At our volume, we need the test to integrate cleanly with our CDP (Segment) and CRM (Salesforce). Manual workflows break down.
Our primary goal is improving open rates, but we also track downstream metrics like lead scoring changes and pipeline influence. I'd love to hear what's working for others at a similar scale. What's your testing framework, and which tools are actually built for this volume?