Interaction Studio Knowledge Base
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Interaction Studio Classic Only

This use case is for customers using Interaction Studio Classic (formerly Evergage Classic) ONLY. For customers using the Interaction Studio 'Campaigns and Templates' application, refer to the Use Case Library instead.

Leverage truly 1:1 personalized recommendations on your homepage, but don’t limit product discovery to one row of five products. Instead, leveraging machine learning provides an opportunity to create more granular recommendations based on the individual and their affinities.

CategoryIncrease Product and Content DiscoveryVerticalE-Commerce, Retail
TopicUse different machine learning algorithms to recommend items on your homepageID #233
Sections in this Article

Example

You can incorporate multiple recommendations campaigns throughout your site; each utilizing a different algorithm to maximize the chances that visitors will convert.

For example, you can show trending items, boosting the visitor’s category affinity in one campaign, and in another campaign use collaborative filtering to boost brand affinity.

Segment

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

Measure

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

Setup

Process

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

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

Reference Materials

The following articles from the Interaction Studio Knowledge Base will provide process steps to help you execute this play: