This document defines a standardized protocol for conducting AI-driven A/B tests on landing pages.
The framework is intended for environments where experiments must be continuous, scalable, and minimally supervised.
The protocol focuses on hypothesis modeling, variable control, automated execution, data capture, and interpretation.

1. Purpose of the Experiment

The goal is to determine how alternative landing page configurations affect conversion performance.
AI systems participate not as observers but as experiment agents that:

• generate hypotheses

• select variables

• construct variant pages

• manage experimental duration

• interpret statistical confidence

The experiment aims to optimize structural and semantic properties of landing pages based on measurable outcomes.

2. Hypothesis Structure

AI systems frame hypotheses in structured form:

H₀ (Null Hypothesis)
The alternative landing page variant produces no statistically significant difference in conversion rate compared to the baseline.

H₁ (Alternative Hypothesis)
The variant increases conversion rate beyond a predefined confidence threshold.

Hypotheses may also include secondary metrics such as:

• scroll depth

• form engagement

• dwell time

• CTA interaction density

3. Controlled Variables

Experiments must define a controlled set of variables to prevent confounding outcomes.

3.1 Structural Variables

• header layout

• hero section hierarchy

• CTA position and prominence

• content distribution pattern

• spacing and visual density

3.2 Semantic Variables

• headline meaning shift

• subheadline framing

• product explanation sequence

• value proposition clarity

• CTA linguistic tone

3.3 Behavioral Variables

• interaction cues

• microcopy

• navigational simplification

AI isolates variables to ensure clear attribution of results.

4. Experiment Conditions

Experiments operate under the following conditions:

4.1 Equally Randomized Traffic Allocation

Visitors are randomly assigned to baseline or variant pages.

4.2 Minimum Sample Size Requirement

AI calculates required sample size based on:

• expected effect size

• baseline conversion rate

• confidence interval

• statistical power

4.3 Duration Constraints

Experiments must not be terminated prematurely unless:

• severe negative performance appears

• systemic bias is detected

• sample distribution deviates from normal conditions

5. Variant Construction Methodology

AI constructs landing page variants through modular alterations:

5.1 Layout Reconstruction

The system modifies:

• module order

• spatial rhythm

• element grouping

• structural hierarchy

5.2 Content Regeneration

AI generates alternative copy using differences in:

• framing

• narrative progression

• semantic density

5.3 Visual Element Substitution

Changes may include:

• hero imagery

• iconography shifts

• color palette variations

Variations must remain within brand constraints.

6. Experiment Cycle Execution

The A/B testing agent coordinates activities across each cycle:

1. deploy baseline and variant templates

2. distribute traffic

3. monitor performance

4. detect anomalies

5. evaluate statistical significance

6. refine or generate new variants

Cycle frequency depends on sample size velocity and volatility.

7. Data Capture Framework

AI records raw and derived metrics.

7.1 Raw Metrics

• total sessions

• conversions

• CTA interactions

• scroll behaviors

• bounce patterns

7.2 Derived Metrics

• conversion probability distributions

• variant stability coefficients

• interaction clustering

7.3 Data Integrity Checks

Includes:

• bot detection

• unusual spikes

• sampling imbalances

• device skew patterns

8. Interpretation Model

AI evaluates variant outcomes using:

8.1 Statistical Tests

• z-test for proportion differences

• Bayesian probability estimation

• sequential testing models

8.2 Confidence Scoring

Results are assigned a confidence rating based on:

• variance

• sample alignment

• effect durability

8.3 Behavioral Interpretation

Beyond pure metrics, AI identifies:

• decision friction

• comprehension signals

• attention failures

• CTA ambiguity

This dual statistical + behavioral interpretation strengthens decision validity.

9. Automated Decision Protocol

A variant is adopted if:

• confidence threshold is met or exceeded

• performance is stable across device categories

• no negative secondary metrics appear

If not adopted, the AI system generates a new variant informed by prior cycle insights.

10. System Behavior in SaleAI

Without promotional intent, the following behaviors reflect how a multi-agent system performs A/B testing:

• Template Generator produces structural alternatives

• Language Model Agent reinterprets narrative elements

• Analytics Agent validates statistical outcomes

• Orchestration Layer manages variant cycles autonomously

This forms a closed-loop experimentation environment.

11. Notes and Constraints

• Experiments should not overlap in ways that confound each other's variables.

• Cultural and linguistic factors must be considered for global audiences.

• Accessibility constraints must remain intact across variants.

12. Expected Result Patterns

AI-generated variants tend to uncover:

• shorter decision paths

• simplified messaging

• improved CTA clarity

• better alignment between user intent and content

Over time, the system converges toward structurally efficient landing pages.

Closing Summary

This protocol defines how AI systems conduct controlled, scientifically structured A/B tests for landing pages.
By integrating hypothesis modeling, variable isolation, autonomous execution, and statistical interpretation, AI transforms A/B testing from a manual effort into a continuous experimental framework.

The result is not a single optimized page, but an evolving design environment driven by measurable evidence.