Product Recommendations in Bloomreach Engagement
Recommendations generate a great chunk of revenue among the market leaders such as Amazon, Netflix, and eBay.
Product recommendations by Bloomreach Engagement could be used in various placements:
- Homepage: To show the webshop sells awesome products (or to reactivate, personalize, etc.).
- Category detail: To show the most popular items within the category that is currently viewed by a user.
- Product detail: To show alternatives for a currently viewed product by a user.
- Basket: To suggest other relevant products to these already in the basket.
- Email campaigns: To personalize email marketing strategies or just to show which products the given webshop is selling.
- Web layers, push notifications...
In general, Bloomreach Engagement Recommendations leverage all customer and catalog data to find the most suitable items for each individual customer in a given situation. Due to the power of artificial intelligence, Bloomreach Engagement recommendations enhance user experience through advanced personalization resulting in an uplift across multiple KPIs.
Watch these two short introductory videos about these recommendations:
By providing multiple pre-built recommendation engine templates suitable for a variety of different placements and Use Cases, you can get all the benefits of AI in a few clicks. Bloomreach Engagement recommendations are not a black box - it is possible to combine different engines and build a solution that fits precisely your needs. Most importantly, as in the whole Bloomreach Engagement platform, everything is done in real-time.
To create a new RS in Bloomreach Engagement, you first need to build an engine using pre-built templates in Bloomreach Engagement. Go to
+ button in the top-right corner and pick a suitable template for your Use Case. See the picture below for visual guidance. Now you can create a new recommender system.
Each template is discussed in the next chapters with examples of ideal setup. You can choose from the following list:
Types of Recommendations
In Bloomreach Engagement we recognize two distinctions between various RS:
1. How the recommendation model is created:
- Rule-based model - a family of methods based on some simple heuristics (e.g. pure statistical approaches). This computation is very quick and the model is ready to be used immediately.
Example: top-selling products, most viewed products, etc.
- AI model - computed by a machine learning algorithm based on the historical data (the process of learning is often called “training”). Each training of an AI model takes several hours.
Example: TF-IDF, Matrix factorization
2. How we request models:
- Recommendations based on their past events.
- Recommendations based on the context of the customer's last actions regarding the product they are currently viewing. There was not enough time to load these actions as events, so they are taken into account by the recommendation through Recommendations for customer API call. An example of such actions would be viewing a product or adding it to the cart.
Example: Recommending alternative products for a product being currently viewed on the product detail page.
To create recommendations, go to Data & Assets > Catalogs, where you must first import the catalog and then make sure:
- To drag the item_id label to the column which indicates a unique product id and matches the same id as defined in the events, e.g. view_item or purchase_item.
- To index (mark as searchable) all of the columns which will be used in the recommendations catalog filter, e.g. active, category (note that you cannot change indexing after you import the catalog)
- To set labels for columns image, URL, title, and price in order to see recommendations results in the single customer view and test tab of each recommender engine.
2. Event tracking
Most recommendation engines require event tracking to understand and make use of customer behavior.
Make sure that event tracking is set up correctly, especially when it comes to:
- View_item which tracks which items are customers viewing
- Cart_update which tracks which items are added or removed from the cart
- Purchase_item which tracks which items were purchased
Data tracking refference
For more information on event tracking visit our Data Manager article.
3. Data Mapping
For all the recommender systems to work correctly, go to Data & Assets > Data manager >Mapping and fill in the following events:
- Purchase item
- Add to cart
- View item
- View category
Updated 6 days ago