Develop Feature-Specific Plans

Learning Objectives

After completing this unit, you’ll be able to:

• Describe the tasks a developer must take to implement Predictive Sort and Product Recommendations.
  
• List the features that you can implement with minimal effort.
  
• List the pre-launch steps you must take to implement Product Recommendations and Predictive Sort.
  
• List the post-launch steps a developer must take to implement Product Recommendations.
  

Introduction

Linda Rosenberg, the Cloud Kicks administrator, takes a step back to review what the team must do to implement specific features in Commerce Cloud Einstein. She also looks at how feature choices can impact implementation.

Commerce Insights requires just data feeds and a login to Configurator.

These features require more effort.

• Search Dictionaries
  
• Search Recommendations
  
• Predictive Sort
  
• Product Recommendations
  

Linda takes a deeper look at the more challenging features.

Search Dictionaries

Einstein generates search dictionaries based on these factors.

• The existing synonym groups for a site. If there are no synonym groups, then there are no Einstein Search Dictionaries synonym suggestions.
  
• The amount of traffic going to the site and reaching the no search results page.
  

After doing some research about data sharing with Einstein, Linda accepts the Einstein Data Privacy Agreement. We show you the steps in the next unit. This is an agreement to anonymously share and receive synonym suggestions based on B2C Commerce network search and synonym group data.

Brandon adds email addresses to the Business Manager search preferences (on the staging instance) to notify the merchandising team of search terms to approve or decline. At first, he approves or declines the search terms, and his manager also receives an email. As his role expands, his manager assigns this task to other team members.

Search Recommendations

Though Linda wants to implement all the Search Recommendations features, she can implement them in increments. She can start with the low-effort features in Search Recommendations that don’t require storefront customizations, and then add the other features. Here’s her plan.

1. Enable Einstein Search Recommendations. The machine learning algorithm starts to consume search queries and identify search phrases to recommend. Einstein begins to show search suggestions based on actual shopper searches.
   
2. With help from a developer, decide in which locations you want to put Einstein Search Recommendations. Extend the type-ahead search flyout (expanding search box) to render multiple search suggestions (versus the default single suggestion). This requires a customization because it isn’t available out of the box.
   
3. Once the design is ready, you can add add these Einstein Search recommendation options:
   

• Implement recent search phrases by modifying the type-ahead search flyout menu to render a personalized list of search phrases entered by shoppers.
  
• Add popular search phrases to the recent search phrases customization.
  
• Add popular storefront searches.
  
• Add recent personal searches.
  

4. Be sure to replicate search preferences to production when this is ready to go live on your site
   

When developing the flyout, the CSM and Vijay must agree on what appears on the flyout and the maximum number of results that display for each element, such as:

• Business view
  
• Search for/Did You Mean
  
• Popular searches
  
• Brands
  
• Category
  
• Content
  
• Recently viewed
  

Predictive Sort

To implement Predictive Sort, Brandon copies an existing sorting rule then blends predictive sort into a dynamic or static sorting rule within Business Manager based on his business needs. A/B Testing is the best way to measure predictive sorting.

Product Recommendations

The Product Recommendations feature also requires a team effort.

To implement this feature, the team plans to take the following steps.

• Vijay adds the Product Recommendations feature to the product detail page via a content slot associated with a recommender.
  
• Brandon creates the recommenders in Configurator.
  
• Vijay adds recommendations to the content slots.
  

When Brandon creates recommenders, we recommend that he starts with the default product detail page (PDP) recommender, which is automatically available in a Business Manager content slot configuration after Einstein deployment. This recommender lets him personalize the product details page through machine learning algorithms that include:

• View to view correlations
  
• Product affinities
  
• Natural language processing
  

Using this recommender gets Brandon started on the most viewed page.

He uses preview and the validator tool to make sure there are no issues.

Basic Schedule

Here is how the team will implement Product Recommendations.

Merchants can go live with Search Recommendations in parallel with their site go-live because of Einstein's use of natural language processing.

Multi-Product Implementation

The team wants to implement Product Recommendations and Predictive Sort (tasks in bold) at the same time.

Next Steps

In this unit, Linda and her team learned about the steps required to implement specific Commerce Cloud Einstein features. In the next unit, Linda enables the privacy agreement, installs the Chrome Extension, and runs the deployment in Business Manager.

Resources

• Salesforce: B2C Commerce Einstein API
• Salesforce: Salesforce Trailblazer Community