Cart-Based
The Cart-Based algorithm type lets recommend items based on the contents of the visitor’s current cart. The recommendation keys are supplied through mbox parameter cartIds in comma-separated values. Only the first 10 values are considered.
Cart-based recommendation logic is similar to the “Recommended For You” user-based algorithm and to the “People Who Viewed These, Bought Those” and “People Who Bought These, Bought Those” item-based algorithms.
Target uses collaborative filtering techniques to determine similarities for each item in the visitor’s cart, then combines these behavioral similarities across each item to get a merged list.
Target also gives marketers the choice of looking at visitor behavior within a single session or across multiple sessions:
-
Single Session: Based on what other visitors did within a single session.
Looking at behavior within a single session might make sense when there’s a sense that products strongly “go with” each other based on a usage, occasion, or event. For example, a visitor is buying a printer and might also need ink and paper. Or, a visitor is buying peanut butter and might also need bread and jelly.
-
Across Sessions: Based on what other visitors did across multiple sessions.
Looking at behavior across multiple sessions might make sense when there’s a sense that products strongly “go with” each other based on visitor preference or taste. For example, a visitor likes Star Wars and might also like Indiana Jones, even if the visitor doesn’t necessarily want to watch both movies in the same sitting. Or, a visitor likes the board game “Codenames” and might also like the board game “Avalon,” even if the visitor cannot play both games simultaneously.
Target makes recommendations for each visitor based on the items in their current cart, regardless whether you look at visitor behavior within a single session or across multiple sessions.
The following algorithms are available with the Cart-Based algorithm type:
People Who Viewed This, Also Viewed
Recommends items that are most often viewed in the same session that the specified item is viewed.
This logic returns other products people viewed after viewing this one. The specified product is not included in the results set.
This logic lets you create additional conversion opportunities by recommending items that other visitors who viewed an item also viewed. For example, visitors who view road bikes on your site might also look at bike helmets, cycling kits, locks, and so forth. You can create a recommendation using this logic that suggests other products help you increase revenue.
If you select this algorithm, you can select the following Recommendations Keys:
- Current Item
- Last Purchased Item
- Last Viewed Item
- Most Viewed Item
People Who Viewed This, Also Bought
Recommends items that are most often purchased in the same session that the specified item is viewed.
This logic returns other products people purchased after viewing this one. The specified product is not included in the results set.
This logic lets you increase cross-selling opportunities by displaying a recommendation on a product page, for example, that displays items that other visitors who viewed the item purchased. For example if the visitor is viewing a fishing pole, the recommendation could show additional items other visitors purchased, such as tackle boxes, waders, and fishing lures. As visitors browse your site, you provide them with additional purchasing recommendations.
If you select this algorithm, you can select the following Recommendations Keys:
- Current Item
- Last Purchased Item
- Last Viewed Item
- Most Viewed Item
People Who Bought This, Also Bought
Recommends items that are most often purchased by customers at the same time as the specified item.
This logic returns other products people purchased after buying this one. The specified product is not included in the results set.
This logic lets you increase cross-selling opportunities by displaying a recommendation on a shopping cart summary page, for example, that displays items that other buyers also purchased. For example if the visitor is purchasing a suit, the recommendation could display additional items other visitors purchased along with the suit, such as ties, dress shoes, and cufflinks. As visitors review their purchases, you provide them with additional recommendations.
If you select this algorithm, you can select the following Recommendations Keys:
- Current Item
- Last Purchased Item
- Last Viewed Item
- Most Viewed Item
Popularity-Based
The Popularity-Based algorithm type lets you make recommendations based on the overall popularity of an item across your site or based on the popularity of items within a user’s favorite or most-viewed category, brand, genre, and so forth.
The following algorithms are available with the Popularity-Based algorithm type:
Most Viewed Across the Site
The recommendation is determined by the item that has been viewed most often. This is determined by recency/frequency criteria that works as follows:
- 10 points for first product view
- 5 points for every subsequent view
- At end of session divide all values by 2
For example, viewing surfboard A then surfboard B in one session results in A: 10, B: 5. When the session ends, you have A: 5, B: 2.5. If you view the same items in the next session, the values change to A: 15 B: 7.5.
Use this algorithm on general pages, such as home or landing pages and offsite ads.
Most Viewed by Category
The recommendation is determined by the category that has received the most activity, using the same method used for “most viewed item” except that categories are scored instead of products.
This is determined by recency/frequency criteria that works as follows:
- 10 points for first category view
- 5 points for every subsequent view
Categories visited for the first time are given 10 points. 5 points are given for subsequent visits to the same category. With each visit, non-current categories that have been viewed before are decremented by 1.
For example, viewing category A then category B in one session results in A: 9, B: 10. If you view the same items in the next session, the values change to A: 20 B: 9.
Use this algorithm on general pages, such as home or landing pages and offsite ads.
If you select the Most Viewed by Category algorithm, you can select the following Recommendations Keys:
- Current Category
- Favorite Category
Most Viewed by Item Attribute
Recommends items or media similar to the most-viewed items or media on your site.
This algorithm lets you select which item attribute you want to base the recommendation on, for example, “Name,” or “Brand.”
You then select which profile attributes stored in the visitor’s profile to match, for example “Favorite Brand,” “Last Item Added to Cart,” or “Most Viewed Show.”
Top Sellers Across the Site
Displays the items that are included in the most completed orders from across the site. Multiple units of the same item in a single order are counted as one order.
This algorithm lets you create recommendations for top-selling items on your site to increase conversion and revenue. This logic is especially suited for first-time visitors to your site.
Top Sellers by Category
Displays the items that are included in the most completed orders by category. Multiple units of the same item in a single order are counted as one order.
This algorithm lets you create recommendations for top-selling items on your site based on category to increase conversion and revenue. This logic is especially suited for first-time visitors to your site.
If you select the Most Viewed by Category algorithm, you can select the following Recommendations Keys:
- Current Category
- Favorite Category
Top Sellers by Item Attribute
Recommends items or media similar to the most-purchased items or media on your site.
This algorithm lets you select which item attribute you want to base the recommendation on, for example, “Name,” or “Brand.”
You then select which profile attributes stored in the visitor’s profile to match, for example “Favorite Brand,” “Last Item Added to Cart,” or “Most Viewed Show.”
Top by Analytics Metric
Displays the “Top x” where x is an arbitrary Analytics metric. When using behavioral data from mboxes, you can use Top Sold or Top Viewed (x = “Sold” or x = “Viewed”). If you’re using behavioral data from Adobe Analytics, you could use x = “Cart Adds” or some other Analytics metric.
Item-Based
The Item-Based recommendation type lets you make recommendations based on finding similar items to an item that the user is currently viewing or has recently viewed.
The following algorithms are available with the Item-Based algorithm type:
People Who Viewed This, Viewed That
Recommends items that are most often viewed in the same session that the specified item is viewed.
This logic returns other products people viewed after viewing this one; the specified product is not included in the results set.
This logic lets you create additional conversion opportunities by recommending items that other visitors who viewed an item also viewed. For example, visitors who view road bikes on your site might also look at bike helmets, cycling kits, locks, and so forth. You can create a recommendation using this logic that suggests other products help you increase revenue.
If you select this algorithm, you can select the following Recommendations Keys:
- Current Item
- Last Purchased Item
- Last Viewed Item
- Most Viewed Item
People Who Viewed This, Bought That
Recommends items that are most often purchased in the same session that the specified item is viewed. This criteria returns other products people purchased after viewing this one, the specified product is not included in the results set.
This logic returns other products people purchased after viewing this one; the specified product is not included in the results set.
This logic lets you increase cross-selling opportunities by displaying a recommendation on a product page, for example, that displays items that other visitors who viewed the item purchased. For example if the visitor is viewing a fishing pole, the recommendation could show additional items other visitors purchased, such as tackle boxes, waders, and fishing lures. As visitors browse your site, you provide them with additional purchasing recommendations.
If you select this algorithm, you can select the following Recommendations Keys:
- Current Item
- Last Purchased Item
- Last Viewed Item
- Most Viewed Item
People Who Bought This, Bought That
Recommends items that are most often purchased by customers at the same time as the specified item.
This logic returns other products people purchased after buying this one. The specified product is not included in the results set.
This logic lets you increase cross-selling opportunities by displaying a recommendation on a shopping cart summary page, for example, that displays items that other buyers also purchased. For example if the visitor is purchasing a suit, the recommendation could display additional items other visitors purchased along with the suit, such as ties, dress shoes, and cufflinks. As visitors review their purchases, you provide them with additional recommendations.
If you select this algorithm, you can select the following Recommendations Keys:
- Current Item
- Last Purchased Item
- Last Viewed Item
- Most Viewed Item
Items with Similar Attributes
Recommends items or media similar to items or media based on current page activity or past visitor behavior.
If you select Items with Similar Attributes or Media with Similar Attributes, you have the option to set content similarity rules.
Using content similarity to generate recommendations is especially effective for new items, which are not likely to show up in recommendations using People Who Viewed This, Viewed That, and other logic based on past behavior. You can also use content similarity to generate useful recommendations for new visitors, who have no past purchases or other historical data.
If you select this algorithm, you can select the following Recommendations Keys:
- Current Item
- Last Purchased Item
- Last Viewed Item
- Most Viewed Item
For more information, see Content Similarity.
