---
title: "Recommended Product"
slug: "recommended-product"
updated: 2026-01-26T16:22:09Z
published: 2026-01-26T16:22:09Z
canonical: "docs.mapp.com/recommended-product"
---

> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mapp.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Recommended Product

## Introduction

The Recommended Product feature enables product recommendations in personalized messages. It allows you to present contacts with relevant products, such as newly released items or products related to previous purchases or browsing behavior.

Recommendations can be generated from Mapp Intelligence data or provided as precomputed recommendations in Mapp Engage. This flexibility supports different personalization strategies and data availability scenarios.

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## Common Use Cases

- Personalizing email or message content with product suggestions
- Promoting newly released or featured products
- Recommending products based on past purchases
- Recommending products based on browsing behavior
- Using external or precomputed recommendation logic within campaigns

---

## Recommendation Sources and Logic

**The Recommended Product** functionality supports multiple approaches to providing and managing product recommendations. The main distinction is how the recommendation data is generated and made available to Engage.

### Recommendation Data Sources

Product recommendations can originate from the following sources:

- **Mapp Intelligence**: Recommendations are generated based on behavioral data, such as browsing and purchase history. For details, see [Using Mapp Intelligence On-Demand Recommendations](/v1/docs/using-mapp-intelligence-on-demand-recommendations).
- **Mapp Engage**: Recommendations are provided as precomputed data, such as imported attributes. For details, see [Storing Attribute Recommendations Using Imports](/v1/docs/creating-recommended-products-using-stored-attributes).

This separation allows teams to decide whether recommendations are calculated dynamically from analytics data or maintained externally and imported into the system.

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### Storage and Availability

Regardless of the source, recommendations are made available as stored data that can be referenced in personalized message content. This ensures that recommended products can be reused consistently across campaigns and channels.

## Related

- [Using the Recommendation API](/using-the-recommendation-api.md)
- [Importing Recommendations for Personalization](/creating-recommended-products-using-stored-attributes.md)
- [Using Mapp Intelligence On-Demand Recommendations](/using-mapp-intelligence-on-demand-recommendations.md)