Creating and Testing a Merchandising Carousel in SAP Commerce Cloud

In this lesson, you learn how to create a merchandising carousel in SAP Commerce Cloud. The carousel relates to a strategy in intelligent selling services. It enables you to observe how the product recommendation displays to customers on the SAP Commerce Cloud storefront.

The following video provides detailed instructions on how to create the merchandising carousel.

In this video, you’ve learned:

• To use SmartEdit to create a merchandising carousel, which allows you to strategically place it on the SAP Commerce Cloud webpage based on your business requirements.
• While choosing the content management system (CMS) component type, that you must select Merchandising Carousel. It connects to intelligent selling services
• To customize the carousel, which you can use the merchandising carousel editor to determine the number of recommended products to be displayed.
• To adjust the scroll type. You can choose between scrolling through products one at a time or row by row.
• To incorporate a strategy defined in intelligent selling services
• To synchronize the newly created carousel from the staged version to the online version, if necessary.

After creating and configuring the merchandising carousel in SAP Commerce Cloud, you must test and validate it. The following video shows the practical demonstration covering the key areas that you must focus on when testing and validating a merchandising carousel.

Here are the key takeaways of the demonstration:

• Global Data Protection Regulation (GDPR) is supported. You must ensure that explicit consent for data collection and analysis in intelligent selling services is granted. The consent process occurs in the composable storefront of SAP Commerce Cloud.
• Verify that the product suggestions listed in the intelligent selling services' product-mix preview workspace align with the product recommendations displayed in the storefront's merchandising carousel.
• Depending on the type of product mix driving these recommendations, you can engage in specific storefront activities to generate clickstream data. You can use the data to refine the intelligent selling services' product-recommendation algorithm.
  • For example, if the product mix depends on an 'add to cart rate', frequently adding certain products to your cart increases the rate. Based on their higher priority, gradually elevates these products to the top of the recommendation list.
  • When working with AI-based product recommendations such as personalized products, you must experiment. Browse various categories and products as a logged in user. You can observe how the system collects and analyzes the user-action data, eventually adjusting the product recommendations based on your simulated preferences.