# Writing an A/B Test Planning Document

{% hint style="info" %}
When designing an A/B Test, using this One-Pager template will help you plan and prepare more thoroughly and extract valuable lessons learned.

Items marked with (\*) are required — try to prepare your experiment following the examples as closely as possible.
{% endhint %}

### Experiment Preparation

<table><thead><tr><th width="74.6171875" data-type="number">Order</th><th width="245.8359375">Category</th><th>Description / Example</th></tr></thead><tbody><tr><td>1</td><td>Problem identified *</td><td>Product list views are high, but the product click-through rate is low.</td></tr><tr><td>2</td><td>Analysis and definition for problem-solving *</td><td>Add qualitative and quantitative data</td></tr><tr><td>3</td><td>Hypothesis definition for the solution *</td><td>Displaying the discount rate on the product list will increase the click-through rate.</td></tr><tr><td>4</td><td>Experiment duration</td><td>Approximately 2 weeks</td></tr><tr><td>5</td><td>User distribution timing *</td><td>Distribute at the point when users view the product list</td></tr></tbody></table>

### Experiment Execution

<table><thead><tr><th width="74.6171875" data-type="number">Order</th><th width="245.8359375">Category</th><th>Description / Example</th></tr></thead><tbody><tr><td>1</td><td>Changes applied *</td><td>Group A: Control (no change)<br>Group B: Treatment (change applied)</td></tr><tr><td>2</td><td>Target description *</td><td>All users</td></tr><tr><td>3</td><td>Success metric *</td><td><p>Which metric needs to improve or decrease for this experiment to be a success</p><ul><li>Product click-through rate (click_product / view_product_list)</li></ul></td></tr><tr><td>4</td><td>Supporting metrics</td><td><p>Add any additional metrics worth monitoring together.</p><ul><li>Add-to-cart rate (click_add_cart / view_product_detail)</li><li>Purchase conversion rate (complete_purchase / view_product_detail)</li></ul></td></tr><tr><td>5</td><td>Guardrail metrics</td><td><p>Consider metrics that must be maintained during the experiment.</p><ul><li>Order cancellation rate, Retention, etc.</li></ul></td></tr><tr><td>6</td><td>URL where the experiment can be verified</td><td></td></tr></tbody></table>

### Post-Experiment

<table><thead><tr><th width="74.6171875" data-type="number">Order</th><th width="319.859375">Category</th><th>Description / Example</th></tr></thead><tbody><tr><td>1</td><td>Experiment results *</td><td>Winner group: Group B</td></tr><tr><td>2</td><td>What can we learn if Group A wins?</td><td></td></tr><tr><td>3</td><td>What can we learn if Group B wins?</td><td>If the click-through rate increased after showing the discount rate, users are highly price-sensitive.</td></tr><tr><td>4</td><td>Based on these learnings, consider a follow-up test topic.</td><td>If users are price-sensitive, should we try sorting by discount?</td></tr></tbody></table>


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# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.hackle.io/en/ab-test/about/ab-test-one-pager.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.