The analytics dashboard is where you see whether your experiments are working. Every time a visitor loads a page, adds something to their cart, reaches checkout, or completes checkout, ABConvert records that event and attributes it to the correct test group. You can review these results at any time as snapshots refresh throughout the day.Documentation Index
Fetch the complete documentation index at: https://docs.abconvert.io/llms.txt
Use this file to discover all available pages before exploring further.
Accessing your analytics
To view analytics for an experiment, go to the experiment table on your ABConvert homepage and open the analytics page for that experiment. From there you can see a breakdown of results for each test group, filter the data by date range or visitor segment, and track statistical confidence over time.

What does the conversion funnel track?
ABConvert tracks the purchase journey for every experiment. By default, most experiments show a standard 4-step funnel:Sessions
A distinct session in your experiment. Sessions is always the top of the funnel and serves as the 100% baseline for all conversion rate calculations.
Add to carts
The visitor adds the product to their cart. This is the first indicator of purchase intent.
Reached checkouts
The visitor reaches the checkout page. This shows they moved past browsing and are actively considering buying.
Custom funnel steps
In Analytics v2, you can customize the funnel stages to track more granular events like Shipping Information Submitted or Payment Submitted. This lets you identify exactly where visitors drop off in your specific flow. With custom funnel steps, Sessions remains fixed as the baseline, and you can select up to 5 additional stages from the available tracked events to display in the Conversion Funnel card. To configure your funnel:- Navigate to the Conversion Funnel card on your analytics dashboard.
- Click the Customize icon in the top right of the card.
- Select up to 5 custom steps from the available event types in addition to Sessions.
- Click Apply Changes.

Which revenue and discount metrics can I review?
Analytics also includes revenue and discount metrics so you can evaluate business impact alongside conversion results.- Revenue and profit metrics show order value, revenue per visitor, and profit per visitor. See Revenue and profit metrics.
- Discount metrics show how much discount cost contributes to each variant’s result. See Discount metrics.
What can I see in the breakdown table?
The Breakdown table lets you review the metrics you care about across test groups and dimensions. Use Metrics to choose which values to compare, such as visitors, conversion rate, orders, or average order value. Then select a dimension to see how variant performance changes across an audience or traffic segment.How do I add dimensions to the breakdown table?
Select Country, Device Type, Visitor Type, or Traffic Source to group the table results by one preset dimension.
Choose the secondary dimension (optional)
Optionally select a nested level, such as Visitor Type, to compare segments within each primary group.

Filtering your results
You can narrow your data to focus on specific audiences or time windows. The following filters are available on the experiment analytics page:Date range
Set a start and end date to focus on a specific period. Useful for isolating the impact of a promotion or seasonal change.
Country
Filter by one or more countries to see how a variant performs in specific markets.
Device type
Choose between Desktop, Mobile, or All to compare behavior across device types.
Visitor type
Filter by New visitors (visitors whose first recorded visit happened after the experiment started) or Returning visitors (visitors who had already visited before the experiment started) to understand how each segment responds to your test.
Visitor type is not determined by Shopify’s session cookie. ABConvert determines visitor type using each visitor’s first-seen timestamp and the experiment start time:If the first-seen timestamp is after the experiment start time, the visitor is counted as new.If the first-seen timestamp is before the experiment start time, the visitor is counted as returning.To segment this data correctly, enable the visitor label script in your theme settings before the experiment starts and keep it enabled. Visitors tracked only after the script is enabled may otherwise be treated as new by default.
How quickly does analytics update?
Analytics data is typically refreshed within about 1 hour. If you want to confirm whether new data is available, click the Refresh button in the analytics page.