Regular monitoring of recommendation analytics helps you identify underperforming widgets and optimize them to increase revenue and average order value.
By tracking performance metrics like clicks, purchases, and conversion rates for each widget, you can spot opportunities to adjust filtering rules, change widget types, or improve placement to help ensure recommendations consistently drive sales.
Analytics data reveals which widgets perform well and which need refinement—enabling you to make data-driven decisions about your recommendation strategy. This helps ensure you're maximizing the value of recommendation widgets across your store, replacing poorly converting options with alternatives that better match customer interests and generate additional revenue.
The Importance of Regular Monitoring
Recommendations create additional value for ecommerce stores. This solution can increase AOV or convince a customer to buy another product from your store if the currently viewed one does not fit their needs.
Checking Recommendations analytics on a regular basis will help in spotting widgets that perform poorly so that you can update their settings or replace them with ones that perform better.
📘 All analytics data in this guide have been randomly generated for informational purposes.
👍 Selecting the most optimal date range
If you're going to check analytics on a weekly basis, it's best to select date range reflecting the previous week's analytics. This is so you can compare analytics on a week-by-week basis for optimal monitoring and comparison purposes.
Recommendations Performance
Checking recommendations performance can help identify poorly performing widgets and replace them with widgets that perform better, thus increasing overall store revenue.
In this example, Staff Picks on the product page is not performing well - users are not buying any products from this recommendation. This can indicate that the products are not relevant, or that the placement of the widget is not optimal.
In this case, Recommendations filtering rules can be used to adjust the products that are shown within the recommendation to better match the page that the user is on - e.g. showing accessories as the main product.
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