Canonical URL: https://chargezen.com/docs/checkoutos/ab-testing/traffic

# Traffic Allocation

How to split traffic effectively for A/B tests

Last updated: 2025-01-16

**Traffic allocation** determines how visitors are distributed between your experiment variants. The right allocation strategy balances statistical power with business risk.

## Allocation Basics

When a visitor enters your checkout, CheckoutOS randomly assigns them to a variant based on your traffic allocation settings. Once assigned, visitors stay in that variant for their entire session and future visits.

Traffic Allocation Example

```json
{
  "traffic_allocation": {
    "method": "random",
    "variants": [
      { "name": "Control", "weight": 50 },
      { "name": "Variant A", "weight": 50 }
    ],
    "sticky_assignment": true,
    "assignment_key": "visitor_id"
  }
}
```

## Allocation Strategies

### Equal Split (50/50)

The standard approach for most experiments:

*   **Fastest to significance** — Equal sample sizes optimize power
*   **Simple to analyze** — No weighting needed in calculations
*   **Best for** — Most standard A/B tests

### Conservative Split (90/10)

Minimize risk when testing potentially disruptive changes:

*   **Lower risk** — Only 10% see the new experience
*   **Slower results** — Takes longer to reach significance
*   **Best for** — Major changes, new features, risky experiments

Conservative Allocation

```json
{
  "traffic_allocation": {
    "variants": [
      { "name": "Control", "weight": 90 },
      { "name": "New Checkout Flow", "weight": 10 }
    ]
  }
}
```

### Ramped Rollout

Gradually increase exposure to the variant:

Ramped Allocation

```json
{
  "traffic_allocation": {
    "method": "ramped",
    "stages": [
      { "day": 1, "control": 95, "variant": 5 },
      { "day": 3, "control": 80, "variant": 20 },
      { "day": 7, "control": 50, "variant": 50 }
    ],
    "auto_progress": true,
    "hold_on_degradation": true
  }
}
```

##### When to Use Ramped

Use ramped rollouts when you want the benefits of testing but need to minimize risk. If the variant performs poorly, the impact is limited to a small percentage.

### Multi-Armed Bandit

Automatically shift traffic toward better-performing variants:

Bandit Allocation

```json
{
  "traffic_allocation": {
    "method": "bandit",
    "algorithm": "thompson_sampling",
    "exploration_rate": 0.1,
    "optimize_for": "revenue_per_visitor",
    "minimum_traffic_per_variant": 0.05
  }
}
```

#### Bandit Pros & Cons

*   Pro: Maximizes revenue during test
*   Pro: Adapts automatically to performance
*   Con: Harder to calculate significance
*   Con: May converge prematurely

## Audience Targeting

Run experiments on specific customer segments:

Targeted Experiment

```json
{
  "audience": {
    "include": [
      { "type": "customer_tag", "value": "vip" },
      { "type": "order_count", "operator": "gte", "value": 2 }
    ],
    "exclude": [
      { "type": "customer_tag", "value": "wholesale" }
    ],
    "percentage_of_audience": 100
  }
}
```

#### Targeting Options

*   Customer tags and segments
*   New vs returning visitors
*   Order history (first-time, repeat)
*   Cart value thresholds
*   Geographic location
*   Device type (mobile, desktop)
*   Traffic source (organic, paid, referral)

## Exclusion Rules

Prevent certain visitors from entering experiments:

Exclusion Configuration

```json
{
  "exclusions": {
    "other_experiments": ["experiment-123", "experiment-456"],
    "customer_segments": ["employees", "testers"],
    "conditions": [
      { "type": "cart_value", "operator": "lt", "value": 20 }
    ]
  }
}
```

##### Mutual Exclusion

When running multiple experiments, ensure they don't conflict. A customer in an upsell pricing test shouldn't also be in an upsell placement test — the results would be confounded.

## Sample Size Calculator

CheckoutOS includes a built-in sample size calculator. Input your:

*   **Baseline conversion rate** — Your current performance
*   **Minimum detectable effect** — Smallest improvement worth detecting
*   **Statistical significance** — Usually 95%
*   **Statistical power** — Usually 80%

The calculator tells you how many visitors you need and approximately how long the test will take based on your traffic levels.

## Best Practices

##### Traffic Allocation Tips

*   **Start with 50/50** — Unless you have a reason not to
*   **Use sticky assignment** — Visitors should see consistent experience
*   **Don't change allocation mid-test** — This invalidates results
*   **Exclude other experiments** — Avoid interaction effects
*   **Consider business cycles** — Run tests for full weeks

## Related Topics

[

### Creating Experiments

How to set up your A/B tests

Learn more



](https://chargezen.com/docs/checkoutos/ab-testing/creating)[

### Analyzing Results

Interpret your experiment data

Learn more



](https://chargezen.com/docs/checkoutos/ab-testing/results)

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