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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

  • Give personalized product suggestions based on past and current shopping behavior
  • Identify trending categories and brands that align with customer preferences
  • Give homepage recommendations that showcase relevant items to inspire product discovery

A visitor who frequently browses sportswear brands can receive suggestions for new arrivals in activewear, boosting engagement and conversion rates.

Financial Services

  • Suggest mutual funds, insurance plans, or account types based on customer interests
  • Display trending financial articles and resources based on visitor engagement
  • Personalize recommendations to match customer asset class preferences

If a visitor explores articles on retirement planning, they can receive recommendations for relevant investment products.

Technology and Demand Generation

  • Suggest case studies, webinars, or whitepapers based on visitor behavior
  • Display personalized resources that align with the visitor’s industry or role
  • Avoid redundant recommendations by excluding previously downloaded content

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.

Resources

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