HOW TO USE SEATEXT AI
Adaptive Multivariate Testing Algorithm: Optimizing Page Elements for Better Performance
Our multivariate testing (MVT) algorithm is designed to efficiently test multiple small changes across your webpage while incrementally improving overall performance. Unlike traditional A/B testing, MVT focuses on optimizing individual elements to create cumulative improvements. Here's how our algorithm works:
1. Initial Testing Phase
The algorithm starts by testing the first variant of each element until it reaches 200 views.
It then ensures each new variant for that element gets a fair initial test by showing each one until it also reaches 200 views.
2. Advanced Testing Phase
After the initial testing, the algorithm enters a more sophisticated phase:

2.1 Smart Scoring System

Each variant is assigned a score using the Upper Confidence Bound (UCB) method.
This score balances the variant's performance (conversion rate) with how much it's been tested (number of views).
Variants performing well or those less tested receive higher scores.

2.2 Dynamic Traffic Allocation

As more data is gathered, the algorithm gradually shifts from exploration (trying different variants) to exploitation (showing better-performing variants more often).
The maximum exploitation rate is capped at 85%, ensuring continuous exploration of new possibilities.

2.3 Continuous Learning

The algorithm continuously updates its understanding of each variant's performance.
Even when primarily showing the best-performing variants, it occasionally tests others to detect improvements over time.
3. New Variant Introduction
When all existing variants of an element have been viewed at least 200 times, the algorithm signals that it's time to introduce new variants for that element.
4. Safeguards
The algorithm includes safeguards against unexpected data, ensuring continuous function even if there are issues with data collection or storage.
Benefits of MVT for Page Optimization
  • Incremental Improvements: By testing small changes across multiple elements, MVT can lead to significant cumulative improvements in page performance.
  • Efficient for Small Changes: Unlike A/B testing, MVT doesn't require thousands of views to reach high confidence. This makes it ideal for optimizing individual page elements.
  • Holistic Page Optimization: By testing multiple elements simultaneously, MVT provides insights into how different page components interact and contribute to overall performance.
  • Faster Iteration: With lower view requirements, you can test and implement improvements more quickly than with traditional A/B testing.
  • Subtle Refinement: Perfect for fine-tuning page elements without drastic changes that might disrupt user experience.
  • Resource-Efficient: Allows for testing many small variations without the need for creating entirely different page versions.

This algorithm ensures that your multivariate testing is always working towards incrementally maximizing page performance. It continuously explores new possibilities to improve your content's effectiveness, focusing on small, impactful changes across multiple page elements.