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CategoryIncrease Product or Content DiscoveryVerticalE-Commerce, Retail
TopicUse Evergage Recommend to go beyond product and article promotionID #234

Introduction

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.

Example

The homepage often serves as a point of reference for visitors to understand the breadth of your catalog. Relying on the wisdom of the crowd may result in category suggestions that are not relevant to every visitor to your site. Similarly, restricting categories based on previous behavior can be both limiting and, often times, irrelevant.

Instead, leverage machine learning algorithms to recommend categories based on a visitor’s historical and current session behavior as well that that of other similar visitors will enable you to both inspire the shopper and serve as an easy reference point.

Segment

No segments are needed to execute this play as it relies on Evergage Recommend and machine learning algorithms to suggest categories and brands.

Measure

With Evergage Recommend on your homepage, you can measure success by an increase in the clickthrough rate.

Process

Here is a checklist of what you need to do in Evergage to create this play on your own site:

  1. Create a Recommendations campaign
  2. Add ingredients, exclusions, and boosters
  3. 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
  4. Train and publish your recipe
  5. Create an Evergage campaign and add the recipe in Message Settings

Reference Materials

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