Reports show no clicks or attributed orders

Start with the two signals that power Cart reporting

The GoScript Smart Cart Recommendations WordPress plugin reports on two connected stages: valid shopper interactions with displayed recommendations, and later qualifying purchases of those recommended products. If either stage has not occurred yet, the related report metric can remain at zero.

That makes troubleshooting straightforward. First confirm the Cart recommendation section is visible and tracking is enabled; then confirm there has been a valid click and, for attributed orders, a matching product purchase inside the configured attribution window.

1. Confirm the recommendation runtime and tracking are active

Open the License page and confirm the plugin runtime is available for the site. Then open Smart Cart Recommendations → Settings and confirm Track recommendation interactions is enabled.

Visible Cart sectionOnly a recommendation that is actually displayed in the main Cart can create the interaction signal used by reporting.

Tracking enabledInteraction tracking must be on before valid clicks can be recorded for future reporting.

If the Cart section itself is not visible, start with the Cart recommendations are not showing guide, then return here once the storefront experience is ready.

2. Confirm a valid recommendation click has happened

The plugin records clicks on the image, title, or Add to Cart link of a displayed, eligible recommendation. A report with no clicks can simply mean shoppers have not interacted with a recommendation yet, or that testing has not used one of the supported product actions.

The configurable click cooldown also avoids counting repeated rapid clicks on the same recommended product from the same local visitor hash too frequently. That keeps the metric more useful, but it can make repeated test clicks look quieter than expected.

3. Confirm the purchase qualifies for attribution

An attributed order requires more than a click. The later order must include the same recommended product, reach Processing or Completed, and be matched to an unexpired attribution record from the valid click.

4. Review the attribution window and cooldown

The default attribution window is 48 hours, configurable in hours. A purchase outside that window cannot use the earlier click for attribution. The click cooldown is also configurable and can prevent rapid repeat clicks from being recorded as separate interaction events.

Choose both controls to reflect the natural pace of shopping and checkout in your store, then allow new activity to accumulate under the settings you have selected.

5. Check the reporting period and product focus

The Reports page supports 7-, 30-, and 90-day views. Confirm you are looking at a period that includes the activity you expect, then use the recommended-product search by name or numeric product ID when you are investigating a particular suggestion.

Remember that reports describe activity recorded after tracking was enabled. Enabling tracking today does not create click or attribution records for interactions that happened earlier.

6. Use a simple, deliberate validation plan

1. Enable interaction tracking and confirm the main Cart displays a valid recommendation.
2. Use a supported click target on that recommendation during a fresh test.
3. Complete an order containing the same recommended product within the configured attribution window.
4. Check the Reports page in a period that includes the new activity after the order reaches Processing or Completed.

This creates a clean reference scenario you can compare against your normal shopper activity and use when refining the Cart strategy.

Let fresh activity build the insight

Zero activity is often a starting point, not a failure. Once the Cart section is visible, tracking is enabled, and shoppers begin interacting with relevant suggestions, the local reporting workspace can start showing the engagement and attribution signals behind your product-discovery strategy.

The GoScript Smart Cart Recommendations WordPress plugin keeps those signals close to your store, making it easy to test, learn, and refine with your own data.