The A/B Testing Experiments dashboard shows how each of your experiments is performing, comparing your control against every variation you chose to test. It covers all Rebuy experiment types, including Widget, Smart Cart, Checkout Offer, General, Global Smart Flow, and Smart Search experiments. If you have not set up an experiment yet, follow the install instructions to get started.

The dashboard organizes your experiments across five tabs: Live for experiments running now, Scheduled for experiments set to start on a future date, Draft for experiments you created but have not launched, Completed for experiments that have ended (grouped by how strong the result is), and Archive for completed experiments you have set aside. This article explains each tab and every metric column you will see.

These names are the aliases you chose during the installation process. If you did not set any specific alias for each of the widgets or control, then it will default to the widget name.

(Image above: Experiments dashboard row showing the Variation Names column, where each variant is labeled with the alias set during setup, for example, Control and Best Sellers, defaulting to the widget name when no alias was entered.)

This is the allocated traffic you elected to assign to each variation type. The traffic percentages you have chosen for each variation type will be assigned randomly, similar to flipping a coin. As a result, the traffic distribution among the variations may not be exactly equal. This approach is implemented to prioritize the speedy loading of the widgets. By randomizing the traffic allocation, the widgets can be loaded quickly and efficiently, providing a seamless user experience.

(Image above: Experiments dashboard row showing the Traffic column, where each variant displays its allocated traffic percentage. For example, 50% to the control and 50% to the variation).

The total number of visitors or views for each specific variation is tracked using cookies upon page load. Once a visitor sees a particular variation, they will consistently see the same variation throughout their browsing session, unless they clear their cookies or start a new tab in private browsing mode. This cookie-based tracking ensures that visitors have a consistent experience and allows for accurate measurement of the performance and impact of each variation based on the number of views received.

(Image above: Experiments dashboard row showing the Visitors column, where each variant displays its total visitor count from cookie-based page-load tracking. For example, 8,196 visitors for the control and 8,254 for the variation).

The Conversions column in the Experiments dashboard shows the number of orders placed by shoppers who were shown a given variant, counted only when Rebuy's experiment tag was recorded at checkout. It is a raw order count, which is different from the Conversion column (the conversion rate percentage) shown elsewhere in the same table.

(Image above: Experiments dashboard variant rows with the Conversions column highlighted)

The "Conversion Rate" option is used to determine the winning variation in an A/B test based on which variation, including the control, achieves the highest percentage of order conversions. In other words, it assesses the success of each variation by comparing the conversion rates, which represent the proportion of visitors who complete a desired action.

(Image above: Experiments dashboard variant rows with the Conversion column highlighted, showing the dollar total and the per-visitor value beneath it.)

The Total Revenue column in the Experiments dashboard shows the total store revenue from orders attributed to a given variant, including revenue beyond what Rebuy generated. It reflects the overall business impact of that variant, not only the revenue Rebuy drove. Beneath each figure, the dashboard also shows a per-visitor value, which is the variant's total revenue divided by its visitor count.

For example, a variant with $19,232.32 in Total Revenue across 8,196 visitors shows $2.35 per visitor ($19,232.32 ÷ 8,196 = $2.35).

(Image above: Experiments dashboard variant rows with the Total Revenue column highlighted, showing the dollar total and the per-visitor value beneath it.)

The Rebuy Revenue column in the Experiments dashboard shows the revenue Rebuy generated for a given variant. This is the value attributed to Rebuy for the orders tracked in that variant, not the full value of every store order, so it is a subset of the variant's Total Revenue. Beneath each figure, the dashboard also shows a per-visitor value, which is the variant's Rebuy Revenue divided by its visitor count.

For example, a variant with $1,211.40 in Rebuy Revenue across 8,196 visitors shows $0.15 per visitor ($1,211.40 ÷ 8,196 = $0.15).

(Image above: Experiments dashboard variant rows with the Rebuy Revenue column highlighted, showing the dollar total and the per-visitor value beneath it.)

The Rebuy AOV column in the Experiments dashboard shows the average order value Rebuy attributes to each variant, calculated as Rebuy Revenue ÷ Conversions. Rebuy Revenue is the value attributed to Rebuy for the orders tracked in that variant, not the full value of every store order, and Conversions is the count of those tracked orders.

For example, a variant with $1,524.28 in Rebuy Revenue across 98 conversions shows a Rebuy AOV of $15.55 ($1,524.28 ÷ 98 = $15.55).

(Image above: Experiments dashboard variant rows with the Rebuy AOV column highlighted, showing the info tooltip that reads "Rebuy AOV = Rebuy Revenue ÷ Conversions.")

The Scheduled tab of the Experiments dashboard lists experiments you have set to start automatically at a future date. A scheduled experiment has not started collecting data yet, so its card shows the variants, traffic split, and start and end dates you configured, but performance columns such as Conversions and Total Revenue show a dash until the experiment goes live.

Open the Scheduled tab from Experiments to review or change a test before its start date. From a scheduled experiment's actions menu (the three-dot icon) you can do the following:

When the Scheduled tab is empty, it reads "No scheduled experiments" with the note "Schedule an experiment to start automatically at a future date."

(Image above: The Experiments dashboard with the Scheduled tab selected, showing a scheduled experiment card with its start and end dates and dashes in the performance columns.)

The Draft tab of the Experiments dashboard lists experiments you have created but not yet launched or scheduled. A draft is a work in progress: it has no start date and has collected no data, so its performance columns stay empty until you start it.

Open the Draft tab from Experiments to finish setting up a test. From a draft's actions menu (the three-dot icon) you can do the following:

When the Draft tab is empty, it reads "No draft experiments" with the note "Drafts are experiments you've created but haven't launched yet."

(Image above: The Experiments dashboard with the Draft tab selected, showing a draft experiment card with editable variants and no performance data.)

The Completed tab of the Experiments dashboard lists experiments that have ended. Rebuy groups each completed experiment by how strong its result is, so you can see at a glance which tests produced a winner and which need another run. Each experiment appears as a card you can expand to see the full variant comparison.

Rebuy sorts every completed experiment into one of four groups based on the winning variant's probability of being the best performer, shown in the Prob. Best column. The following table explains each group and what qualifies an experiment for it:

A completed experiment card summarizes the result before you expand it. Each card includes the following:

Select Show Details on a card to expand the full variant comparison table, which lists each variant's Conv. Rate, Conversions, Visitors, Total Revenue, Rebuy Revenue (hidden for Smart Cart experiments), Rebuy AOV, and Prob. Best. The winning variant's row is highlighted, and each metric shows its change compared with the control.

From a completed experiment you can select Archive to move it to the Archive tab, or use the actions menu to Delete it.

(Image above: A Clear Winners card on the Completed tab, expanded to show the Control and winning variant rows with conversion rate, revenue, and Prob. Best columns.)

Your completed experiment says "Not Enough Data" because none of its variants reached a 70% probability of being the best performer, so Rebuy could not name a statistically significant winner. Rebuy shows the message "Not enough data has been collected to determine a statistically significant winner. Consider extending the test duration or increasing traffic." To get a conclusive result, run the experiment again for a longer period or send more traffic to it so each variant collects enough visitors and orders.

The Archive tab of the Experiments dashboard stores completed experiments you have moved out of the Completed tab to keep your active results focused. Archiving does not delete an experiment or its data; it moves the experiment into a separate, searchable list.

To archive an experiment, open the Completed tab and select Archive on any completed experiment card. To move an experiment back, open its actions menu on the Archive tab and select Unarchive, which returns it to the Completed tab.

The Archive tab displays experiments in a table with the following columns:

Above the table, you can narrow the list with the following controls:

Expand any row to see the same variant comparison table shown on completed experiment cards. From a row's actions menu you can Unarchive the experiment or Delete Permanently, which removes the experiment and its data and cannot be undone.

When the Archive tab is empty, it reads "No archived experiments" with the note "Archive experiments from the Completed tab to keep your view focused. Click 'Archive' on any completed experiment card to move it here."

(Image above: The Archive tab showing an archived Smart Cart experiment with a Not Enough Data signal, the search and filter controls above the table, and an expanded variant comparison.)

Your Conversions number is lower than your Shopify order count because Rebuy counts only the orders where its experiment tag was present at checkout, which is a subset of all your store orders. Shopify counts every order your store receives, while the Conversions column counts only orders attributed to shoppers who were shown that variant. The two numbers are measuring different things, so they are not expected to match. Compare Conversions across the variants in your experiment rather than against your Shopify dashboard.

The difference between Total Revenue and Rebuy Revenue is what each column counts. Total Revenue is the full store revenue from orders attributed to a variant, so it reflects the variant's overall business impact. Rebuy Revenue is only the portion of that revenue attributed to Rebuy, so it is always a subset of Total Revenue. Use Total Revenue to gauge the variant's total effect on your store and Rebuy Revenue to isolate the revenue Rebuy specifically drove. Rebuy Revenue is also the value divided by Conversions to produce the Rebuy AOV column.

Your Rebuy AOV differs from your Shopify store AOV because it is calculated only from Rebuy-tracked orders, not from every order your store receives. Rebuy AOV divides the Rebuy Revenue for a variant by that variant's Conversions count, so it reflects the average value of the scoped subset of orders Rebuy tracked. Your Shopify store AOV includes all orders across your store. Use Rebuy AOV to compare variants against each other within an experiment, not as a measure of your overall store performance.