STEP 3 Campaign preparation
Introduction
In this chapter, we will discuss how to properly prepare for an AI campaign, including considerations for display settings, filter, preview, and more. We will also cover strategies for integrating AI into existing campaigns.
From this chapter you will learn:
about using AI recommendations in different places,
about AI campaign preparation.
Campaign preparation
Campaign preparation consists of a few parts described below.
- Choosing campaign type
- Adding recommendation filters
- Previewing recommendations
- Recommendation statistics
- Distributing recommendations
Choosing campaign type
Create an AI campaign that indicates what products will be displayed to customers – first of all you have to choose an AI campaign type.
- Campaign types and preparation - learn more about campaign types you can use and campaign preparation.
Adding recommendation filters
Filters can apply additional logic on top of recommendation results served by the AI engine.
They are used in two situations:
- For filtering, to exclude or include items from a recommendation.
- For score boosting, to promote or demote matching items.
- Recommendation filters - learn more about examples of recommendation filters.
Previewing recommendations
You can check the preview of the recommendation to:
- See the set of items recommended when there is a specific item or items in the shopping cart.
- See the set of items recommended for a selected customer. The context of the preview depends on the recommendation type selected.
- Preview of the recommendations - learn more about preview of the recommendations.
Recommendations statistics
Synerise offers you a panel with full statistics of the recommendation.
- Recommendations statistics - learn more about recommendation statistics.
Distributing recommendations
You can add recommendations to many different campaigns and communication channels such as emails, webpush, dynamic content, mobile campaigns, and so on.
- Distributing recommendations - learn more about distributing recommendations.