Bayesian vs. Multi-Armed Bandit: Which is Best for Your WordPress Site?
Should your traffic be split 50/50 until the end? Or should it dynamically shift to whatever’s winning? It’s a crucial question every data-driven marketer faces. The answer depends on one thing: are you testing to learn or to earn?
In this post, we’ll explore the two advanced optimization methods in AB Split Test: Bayesian and Multi-Armed Bandit and give you clear, actionable use cases so you can confidently choose the right mode for every test and start maximizing your results.
What is Bayesian?
Bayesian is an intelligent feature that uses Bayesian statistics to automatically conclude your A/B test as soon as a clear winner emerges with high confidence.
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How it works: The Bayesian model continuously evaluates the probability that one variation is better than the others. Once this probability exceeds a strict threshold (e.g., 95% confidence), the test automatically stops and declares a winner.
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Primary Goal: To reliably identify a winning variation while minimizing the risk of false positives and saving you time and traffic.
What is Multi-Armed Bandit (MAB) Optimization?
Multi-Armed Bandit (MAB) is a dynamic allocation algorithm that continuously shifts traffic toward the best-performing variation during the test.
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How it works: The MAB algorithm starts by exploring different variations. As performance data comes in, it increasingly "exploits" the leading variation by sending it more and more traffic, maximizing conversions while the test is still running.
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Primary Goal: To maximize the total number of conversions throughout the entire duration of the experiment.
| Feature | Bayesian Autocomplete | Multi-Armed Bandit (MAB) |
|---|---|---|
| Core Philosophy | “Learn fast, then implement.” | “Earn while you learn.” |
| Traffic Allocation | Fixed split (e.g., 50/50) until a winner is declared. | Dynamic; continuously shifts traffic toward the winner. |
| Primary Goal | Statistical confidence in a result. | Maximizing conversions during the test. |
| Best For | Testing a hypothesis; determining which variation is truly better long-term. | Optimizing a high-traffic page; maximizing return on traffic right now. |
| When You Get a Result | A clear winner is declared at the end. | Continuous improvement in conversion rate during the test. |
| Ideal Traffic Level | Works well with most traffic levels. | Truly shines with high-traffic sites. |
When to Choose Standard Bayesian Mode
Choose Bayesian Autocomplete when your primary goal is learning and validation. Select this mode from the Winning Mode dropdown if:
- Use Case: Testing a New Design or Layout. You've redesigned your homepage and need to be statistically confident that the new version performs better before you permanently switch all your traffic.
- Use Case: Comparing Messaging. You're testing two different value propositions or headlines. You need a definitive answer on which message resonates more with your audience for all future marketing campaigns.
- Use Case: For Lower-Traffic Sites. You don't have millions of visitors. Autocomplete helps you find a reliable answer faster without needing an enormous sample size.
- Use Case: Making a Permanent Decision. You are testing a change you plan to implement once and leave for a long time (e.g., a new logo, a permanent page redesign).
✅ Pros: Reduces testing time, minimizes risk of false positives, provides a statistically sound result.
❌ Cons: Does not maximize conversions during the test period.
When to Choose Dynamic Multi-Armed Bandit Mode
Choose Multi-Armed Bandit when your primary goal is maximizing immediate performance. Select this mode from the Winning Mode dropdown if:
- Use Case: Short-Term Promotions or Sales. You have a 72-hour flash sale. You need to maximize sign-ups now and can't afford to waste a single click on a losing variation. MAB will quickly find and favor the best-performing promotion.
- Use Case: High-Volume Landing Pages. Your paid ad landing page gets massive, expensive traffic. Even a small, immediate uplift from dynamically favoring a potential winner translates to significant conversion gains and a higher return on ad spend (ROAS).
- Use Case: Continuous Optimization. You want to always show the best-known version but remain open to new ideas. MAB can constantly test new variations while ensuring most users see the current best performer.
- Use Case: When a "Good Enough" Answer Now is Better Than a "Perfect" Answer Later. If speed and incremental gain are more important than absolute certainty, use MAB.
✅ Pros: Maximizes total conversions during the test, reduces opportunity cost, ideal for high-stakes pages.
❌ Cons: Can be slower to detect small, long-term differences between variations. Less focused on pure statistical confidence.
Conclusion: Test Smarter, Not Just Harder
Both Bayesian Autocomplete and Multi-Armed Bandit are advanced features that put your WordPress site's traffic to work more efficiently. Understanding their strengths allows you to align your testing methodology with your business objectives perfectly.
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For learning and validation, choose
Standard - Bayesian. -
For immediate ROI and continuous optimization, choose
Dynamic - Multi-Armed Bandit.