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When you hear the term “recommendations” in reference to an e-commerce site, you typically think of product and content suggestions based on affinities, preferences, and other algorithmic configurations. But Evergage Recommend is much more powerful and flexible than that and can apply machine learning algorithms to recommend categories, brands, authors, styles, and other classes of products and content.
No segments are needed to execute this play as it relies on Evergage Recommend and machine learning algorithms to suggest categories and brands.
With Evergage Recommend on your homepage, you can measure success by an increase in the clickthrough rate.
Here is a checklist of what you need to do in Evergage to create this play on your own site:
- Create a Recommendations campaign
- Add ingredients, exclusions, and boosters
- Ensure that you set your recommendation type to the category you want to use. By default it is set to product or article (for content), but category, brand, class, author, and others are available
- Train and publish your recipe
- Create an Evergage campaign and add the recipe in Message Settings
The following articles from the Evergage Knowledge Base will provide process steps to help you execute this play: