|Category||Increase Conversions||Vertical||E-Commerce, Retail|
|Topic||Use machine learning to recommend blog articles to increase page depth and customer loyalty||ID #||236|
Many retailers today are using social media as an acquisition channel and most of this traffic usually lands on a blog post. Instead, have this traffic land on your homepage where you can use machine learning to suggest not only products of interest, but lifestyle articles as well.
No segments are needed to execute this play as Evergage Recommend uses machine learning to make suggestions
With 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:
This page has no comments.