Use MCP Recommendations
Learning Objectives
After completing this unit, you’ll be able to:
- Define essential terms related to recommendation strategies.
- Compare MCP Recommendations against a static recommendation system.
- Explore how to build, refine, and deploy recommendation campaigns.
Overview of Recommendations
Marketing Cloud Personalization Recommendations integrates seamlessly with your data set to provide instant, real-time recommendations for your website visitors. Unlike static recommendation systems, Marketing Cloud Personalization enables dynamic, continuously updated suggestions based on current visitor behavior and historical preferences.
This is accomplished via the Marketing Cloud Personalization catalog—a dynamic database that stores products, content, categories, and tags. It automatically updates in real time, ensuring that recommendations reflect the latest available data. Products that go out of stock are automatically removed from recommendations.
This level of personalization allows your businesses to:
- Recommend products, content, brands, and categories tailored to each visitor.
- Use machine learning to deliver timely, relevant suggestions.
- Deploy personalized recommendations across multiple industries.
Unlike black-box recommendation engines, Marketing Cloud Personalization Recommendations is fully transparent. You can see what’s driving recommendations and customize them with prebuilt algorithms, known as recipes. This flexibility ensures that recommendations align with business goals and user preferences.
A recipe is a set of rules and algorithms that determine how recommendations are generated. Recipes are composed of:
- Ingredients: The core machine learning algorithms powering recommendations
- Exclusions: Rules that filter out unwanted items (for example, removing items already in the cart)
- Boosters: Mechanisms that prioritize recommendations based on visitor affinity
For example, brand affinity boosters prioritize recommendations for brands that a visitor has shown interest in, creating a highly personalized experience.
Item Blocks and Templates
Marketing Cloud Personalization makes it easy to deploy recommendations using preformatted item blocks and templates.
- Item blocks: Containers that present recommendations in a structured format.
- Item templates: Customizable layouts that define how recommended items appear.
By using these tools, you can effortlessly integrate recommendations into your website without extensive coding.
Industry-Specific Recommendations
Marketing Cloud Personalization is designed for multiple industries, allowing your businesses to fine-tune its recommendation strategies based on your unique needs. Marketing Cloud Personalization Recommendations enables real-time, one-to-one personalization across industries via machine learning, so you can:
- Deliver highly relevant recommendations tailored to each visitor.
- Use transparent algorithms to refine recommendation strategies.
- Ensure recommendations remain timely and up to date without requiring manual updates.
Here are examples of how Marketing Cloud Personalization can benefit specific industries.
Industry | What MCP Can Do | Example |
|---|---|---|
Retail and Ecommerce |
| A visitor who frequently browses sportswear brands can receive suggestions for new arrivals in activewear, boosting engagement and conversion rates. |
Financial Services |
| If a visitor explores articles on retirement planning, they can receive recommendations for relevant investment products. |
Technology and Demand Generation |
| A visitor’s past interactions and industry interests trigger personalized ebook recommendations on a homepage. |
Now that you understand the basics of Marketing Cloud Personalization Recommendations, you’re ready to look at some data. The next stop in the MCP tour is all about reporting.