Get to Know Commerce Cloud Einstein

Learning Objectives

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

List three types of data that Commerce Cloud Einstein tracks.
List three ways that Einstein improves a shopper’s experience.
Describe the two Einstein search features.
Explain the differences between Einstein Search and Product Recommendations.

What Is Einstein

Linda Rosenberg is a site administrator for Cloud Kicks, a company that specializes in high-end custom sneakers. The Cloud Kicks ecommerce site runs on Salesforce B2C Commerce, part of Salesforce Commerce Cloud. Recently, Cloud Kicks management learned about the benefits of Commerce Cloud Einstein. They ask Linda to deploy it to the Cloud Kicks site.

Linda Rosenberg

Before Linda starts, she wants to learn what Commerce Cloud Einstein is and how it can help her company’s business.

Commerce Cloud Einstein is artificial intelligence (AI) embedded right in the application. Just like Einstein in the Salesforce core platform, merchants using B2C Commerce can easily access the power of predictive intelligence without having to hire a data scientist, use a costly third-party recommendations provider, or create a home-grown solution.

She learns that with Einstein, you can:

Use shopper behavior to deliver tailored product sorts.
Decrease the time a customer spends looking for what they want.
Increase the likelihood of a purchase.
Bring personalization into the commerce experience for all shoppers, improving their experience.

Because Einstein is embedded in B2C Commerce, data collection has no measurable effect on the shopper experience. There’s no additional contract or payment required to use it.

Although it’s embedded, you still need to enable and implement it. Let’s see what that means.

Enablement is when you...	Configure B2C Commerce to use Commerce Cloud Einstein. Enable the storefront data. Enable the Configurator.
Implementation is when you...	Create and update the templates and code. Create Configurator recommenders. Configure the features in Business Manager. Complete testing.

Linda's part of the job is enablement.

Data Is Key

Einstein needs data and all storefronts have it. So how much data does it need? Two years of data is ideal, but data is useful to Einstein soon after a storefront is live. Einstein uses the following data.

Clickstream
Products
Orders

Data includes orders, products, and clickstream.

Clickstream is anonymous data on shopper behavior. The shopper’s browser sends information to B2C Commerce servers each time the shopper:

Views a product or page
Views or clicks a recommendation
Performs or refines a search
Adds a product to cart
Removes a product from the cart
Finishes checkout

This is the data it sends.

Timestamps
Cookies
Activity types (product view, page view, add to cart, remove from cart, finish checkout, view category, view search, click a category, customer sign-up)
Product data, such as the product ID of the current page
Category data, such as the category ID of the current page
Search terms and search refinements
IP address
User-Agent (the browser, version, and operating system that the shopper is using)

Einstein aggregates this data for benchmarking and analysis, making intelligent data-driven personalization possible. Merchants like Cloud Kicks can:

Personalize recommendations.
Personalize search results and category grid sorting.
Identify new search synonyms.
Analyze shopper baskets to identify products that are commonly purchased together.
Provide shoppers with type-ahead search guidance, recent personal searches, and popular storefront searches.

Commerce Cloud Einstein Features

The merchandisers in Linda’s company want to use Einstein features to personalize the shopper experience and automate personalization-related tasks. These are the features they want to use.

Feature	Description
Commerce Insights	Captures shopper, order, and product data and displays products that are most commonly purchased together. Merchandisers can use this info to create product bundles, deals, and “complete the look” suggestions.
Einstein Search Dictionaries	Consumes site searches and terms used in searches that are not in the keyword list and makes recommendations to add them.
Einstein Search Recommendations	Performs personalized term completion and corrections, and provides recommendations from recent search phrases, and popular or trending search phrases based on predictive intelligence.
Einstein Predictive Sort	Sorts search results or the product display grid based on predictive intelligence.
Einstein Product Recommendations	Provides personalized product recommendations based on an individual shopper’s experience.

Who Does What?

The Cloud Kicks storefront is already live, so Linda can enable Commerce Cloud Einstein immediately, or as soon as she gets the team together and they create a plan. These are the people that Linda works with for the deployment.

Developer: Creates and changes code and templates and tests.
Merchandiser: Configures Business Manager and Configurator settings and tests the results.

Size the Effort

The team considers the level of effort required to implement the features that Cloud Kicks wants.

Feature	Developer	Merchandiser	Average Time to Complete
Commerce Insights	None	Review routinely with Marketing team to develop new product-centric campaigns.	Immediate
Einstein Search Dictionaries	None	To accept or deny synonym suggestions go to Business Manager > site > Merchant Tools > Search > Search Dictionaries Search > Search Dictionaries to enable Search Preferences Search > Search Preferences to add your email for notifications	Immediate
Einstein Search Recommendations	Required	If search flyout exists, configure in Business Manager. Configure and test.	Varies: Immediate to 2 sprints
Predictive Sort	None	Copy existing sorting rule and apply predictive sort. Configure and test.	Immediate
Product Recommendations	Define rendering templates for recommendation type slots.	Create recommenders in Configurator. Configure in the given content slot within Business Manager, test and replicate content slots.	2 sprints

Enablement Components

Linda learns that the Cloud Kicks developers already prepped the site for enabling Einstein. Prior to go-live, developers created templates and code with Einstein in mind. For example, the content slot rendering and related storefront template code they developed pre-launch follows Einstein best practices. That’s great news!

Next, Linda needs to understand which components to use to enable and implement Commerce Cloud Einstein.

Feature	Configurator	Development Element	Business Manager	A/B Testing
Commerce Insights	Yes	No	No	No
Einstein Search Dictionaries	No	No	Search > Search Dictionaries to enable Search > Search Preferences to add your email for notifications	No
Einstein Search Recommendations	No	Search flyout	No	No
Einstein Predictive Sort	No	No	Sorting Rules	Yes
Einstein Product Recommendations	Yes	New recommendation slot rendering templates	New slot configuration to use Einstein recommender	Yes

Implementation Tips and Resources

The merchandisers play a big role in implementing Commerce Cloud Einstein and acting on the insights it uncovers. So the CSM also points out some resources to help them learn more about Commerce Cloud Einstein. These come in handy when the merchandisers plan the best ways to inject AI into the shopper experience.

The B2C Commerce Einstein Platform Adoption Workshop.
1:1 with a Salesforce field specialist in Accelerator engagements (see your customer success manager).
Partner resources: the Partner Community > Commerce Cloud Chatter group.

Next Steps

In this unit, Linda Rosenberg learned about the data that Commerce Cloud Einstein collects and how Einstein can enhance the shopper experience in a storefront. She explored Commerce Cloud Einstein features, who’s involved in enablement and implementation, and the effort required per feature. Next, she explores how to plan for enablement and implementation.

Resources

Copyright

Rights of ALBERT EINSTEIN are used with permission of The Hebrew University of Jerusalem. Represented exclusively by Greenlight.