After connecting intelligent selling services for SAP Commerce Cloud, we can now create personalized product recommendations for display on your SAP Commerce Cloud shop.
Let's start by defining a product mix. A product mix determines recommended products for customers. Before diving into how to create and manage product mixes, let's review the basics of a product mix.
Based on a defined type of product recommendation, you can customize every product mix. For a head start, we provide the following types of recommendations by default:
- Trending Products: What's hot in the market?
- Related Products: What other products are customers interested in that match or are similar to the products?
- Personalized Products: What have customers previously shown an affinity for?
- Complementary Products: What other product options enhance the customers' purchase?
- Replenishment Products: What must customers regularly replenish?
- Recently Viewed Products: What are customers' recent interests and considerations?
Let's explore the following informative table. It breaks down each of the previously listed product recommendation types.
| Type | Based on | Description | Influencers | Recommended Pages for Display |
|---|---|---|---|---|
| Trending Products | Real time product/order related performance data | Highlights products based on business-driven criteria, such as "Add to Cart Rate", "Product Page Views", "Revenue", or "Units Sold" | One required | Home, Campaign, Category |
| Personalized Products | Artificial Intelligence | Next-click products relevant to an individual customer, in-the-moment, which can be filtered by page category or current search query | Optional | Home, Campaign, Category, Search, Product |
| Related Products | Artificial Intelligence | Derives related products from user behavior and display on product detail pages | Optional | Product |
| Complementary Products | Artificial Intelligence | Products popularly bought with the focus product or with products in the customer’s cart | Optional | Product, Cart |
| Replenishment Products | Artificial Intelligence | Uses past customer orders to determine buying habits and calculate the best timing for displaying products for reorder | None | Home, Category, Order Details, Order List |
| Recently Viewed Products | Logging/history data | Products the customer previously viewed, which they can quickly return to | None | Home, Campaign, Category, Search, Product |
The table contains the following details:
- It highlights diverse types of product recommendations, which leverage collated data from different sources.
- Some recommendations use real-time product-performance or order data (trending products).
- Others are based on user-history logs (recently viewed products).
- Most importantly, there are recommendations that use AI-supported analysis combining relevant contexts (personalized products, related products, complementary products, and replenishment products).
- It illustrates the possibilities of using data-driven influencers to affect the product recommendation for selected product mixes.
- Trending products require you to set at least one influencer.
- However, for personalized products, related products, and complementary products, influencers are optional.
- Also, it’s impossible to add influencers for both replenishment products and recently viewed products.
- It suggests the best locations to display these recommendations. For instance, because of their broad appeal, trending products and personalized products are best suited for the home page or landing page. Related products and complementary products are more effective on the product detail page. Replenishment products are recommended for the order history page, facilitating continuous customer engagement.
Now, let's learn how to create and manage a product mix.