Product Recommendations

The Product Recommendations toolbar plugin adds a capability to the toolbar of the content editor that allows you to insert product placements, dependent on having set up a Product Collector Connection. Click on the icon to insert a content placement based on an algorithm of your choosing at the place of the cursor. 

To add a recommendations placement, edit an interaction and click "Insert object" from the content editor toolbar:

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Selecting 'Product Recommendations' will add a placeholder placement to the content editor. Hovering over the placeholder will light it up in blue. Click and edit or double click the placeholder to open a configuration popup with four tabs for configuring the products placement:

Source

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Select a source of products, which is defined by configuring a Product Collector Connection.

Design

Template
Edit or select another template for your recommendations placement
Number of items
Enter the number of products you'd like to include in the placement
CSS URL
If you wish to control the styling of the placement by loading CSS from an accessible URL, enter it here.
Custom CSS
Optionally enter custom CSS to style the list. This CSS will overrule CSS loaded from the URL, and the CSS in the template.

Algorithms

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On the algorithms tab, you will be able to affect the algorithm that selects a unique product set for each individual user. As you make changes here, you should be able to see them reflected in the placement in your editor window.

There are a number of algorithms you can introduce, to boost the prevalence of products with certain characteristics as described below. For each algorithm added, you can control whether it is used at all, or to what degree it will be incorporated into the overall selection.

The algorithm options can be grouped into three types: Aggregate stats-based, product-based, and profile-based. 

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Aggregate stats-based algorithms

Recent high CTRs: Products that have been clicked most within recommendations placements served by BlueConic.

Top products: Products that has been engaged with the most across the site over the last several hours / days (configurable in product collector).

Viral products: Content that has been popular as an entry point to the site over thelast several hours / days (configurable in product collector).

 

Product-based algorithms

Look-alike products: Products that have a similar textual makeup in the description to the current product.

Same category: Products in the same category as the current product.

Recency: Products with a recent release/availability date.

 

Profile-based algorithms

Collaborative filtering: Products engaged with by other users similar to the current user.

Same interest: Products in the same categories as those the user has shown the most interest in.

Seen products: Products the user has already browsed.

Profile-based algorithms also take a "ramp up speed" setting, which allows BlueConic to react more quickly to any information that is being populated in the user profile.

 

Filters

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The filters area allows you to include or exclude products based on its metadata or other options. 

Hide products which are out of stock: Only include products that are in stock, based on an in stock indicator configured in the product collector

Seen products: Exclude products already viewed by the current user

Frequency cap: Exclude products after the user has not clicked on them after having clicked other products within the same placement a specified number of times.

Metadata filtering:

Metadata scraped from the product collector can be used to filter the product selection:

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In this way, you can include or exclude products based on their relationship to metadata within the current product being browsed, or within the user profile:

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Below is an example where an explicitly defined value, "clearance", being excluded from a selection of products:

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And here is an example where only products in this users categories of interest are included: 

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The Product Recommendations plugin is a certified plugin. Contact us if you are interested in using content recommendations.