Basic Recommendations

Basic recommendations are a set of straightforward custom rules which act as filters. In addition, you can sort these items by numeric catalog attributes. Basic recommendations consist of the following models.

1. Item attributes

This model applies manual merchandising rules using the catalog attribute filters and sorting by some numerical catalog attribute e.g. price. These recommendations are used when you want to promote certain types of products. Additionally, these recommendations can be customized to align with the user's profile.

Example use-cases

• Reveal the 'New arrivals' section
• Inspire with your branded collection
• Celebrate with our festive picks
• Recommend ideal jobs matched to your users' skill set
• Locate local restaurants near your customers.

2. User actions

This models aid in recollecting items that users have recently engaged with. In the retail sector, when a user creates a wishlist or adds an item to their cart, it indicates their strong intention to purchase. Likewise, in classified industries, viewing an item can be seen as a strong indicator of interest. By customizing campaigns that focus on recalling these interactions, they can serve as a decisive factor.

Example use-cases

• Recover lost sales with cart abandonment reminders
• Boost engagement with personalized campaigns from users' wish lists
• Re-engage users with recently viewed items
• Drive repeat business with gentle nudges.

3. Catalog Alerts

Catalog Alerts delivers personalized, timely recommendations to users by monitoring changes in your catalog, such as price or quantity variations, and analyzing user interactions. When the catalog refreshes, it detects significant differences and can generate tailored alerts for targeted campaigns with personalized content that can boost engagement and drive conversions.

Example use-cases

• Price drop alerts
• Selling fast alerts
• Back-in-stock alerts
• Price increase alert

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