C C E

From which business manager instance do the feeds need to be scheduled?

• 1. Production only

• 1. Any Primary Instance Group (PIG) Instance

• 1. Any Secondary Instance Group (SIG) Instance

• 1. Can be run in any Primary or Secondary Instance Group (PIG or SIG) Instance

Answer explanation

For scheduling feeds for Commerce Cloud Einstein, the feeds need to be configured and executed on the production instance of your Salesforce Commerce Cloud environment. This is crucial for integrating live data into the recommendation engine, which is essential for the effective delivery of personalized product suggestions. Here's a step-by-step guide on how to proceed:

1. Log into Business Manager: Access your Salesforce Commerce Cloud Business Manager.

2. Navigate to the Einstein Status Dashboard: Once in Business Manager, locate the Einstein Status Dashboard. This is typically found under the Merchant Tools or Einstein section, depending on your version of Business Manager.

3. Configure Catalog and Order Feeds: In the production instance, you need to set up and execute both catalog and order feeds. This involves specifying the data to be included in the feeds, such as product details, prices, categories, and order information.

4. Schedule the Feeds:

   • For Catalog Feeds, you'll typically configure them to run after each catalog update or at a frequency that matches the update rate of your catalog.

   • For Order Feeds, it's common to schedule them to run daily to ensure that the recommendation engine has the most current order data to learn from.

5. Activation:

   • Immediate Activation: If you need the data to be captured immediately, you can run the feeds right away for a one-time capture.

   • Recurring Schedule: For ongoing data capture, configure the feeds to run on a recurring schedule. This ensures that Einstein's recommendations are based on the most up-to-date information.

6. Monitor and Validate: Use the Commerce Cloud Recommendations Validator Chrome extension to validate and debug Einstein recommendations and sorting rules on your storefront. This tool helps ensure that the data feeds are correctly influencing the recommendations.

7. Adjust as Necessary: Based on the performance of the recommendations and the insights from the Validator tool, you may need to adjust the frequency of the feeds or the data included within them.

By following these steps and scheduling the feeds from the production instance, you ensure that Commerce Cloud Einstein has the necessary data to provide personalized product recommendations and improve search results, thereby enhancing the shopping experience for your customers.

What three sources data are used by Commerce Einstein to fuel Commerce Einstein features?

• 1. Shopper clickstream

• 1. Internal & External Orders

• 1. Product Catalog

• 1. Salesforce Real-Time Event Monitoring

• 1. Google Analytics Tracking

Answer explanation

https://trailhead.salesforce.com/content/learn/modules/cc-einstein-plan-and-implement/cc-einstein-implementation-basics

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
  

If a customer has brick & mortar store order data available and their product catalog has matching product ids between brick & mortar and online, how can a developer send that external order data to commerce Einstein?

• 1. Send the files via support ticket

• 1. Have a developer create .csv file to spec:
     a) Log into the stores Commerce Einstein SFTP directory

  2. b) Post file/s one time or on a routine basis

• Have a developer create .csv file to spec and gzip the file:

  a) Log into the stores commerce Einstein SFTP directory

  b) Post file/s one time or on a routine basis

• Have a developer create .tsv file to spec:

  a) Log into the stores commerce Einstein SFTP directory

  b) Post file/s one time or on a routine basis

• Have a developer create .tsv file to spec and gzip the file:

  a) Log into the stores commerce Einstein SFTP directory

  b) Post file/s one time or on a routine basis

Answer explanation

Rationale:

1. File Format and Compression: The documentation specifies that for bulk uploads, the data should be prepared in a gzipped tab-separated values (TSV) file format. This ensures efficient data transfer and compatibility with Einstein's data processing systems.

2. SFTP Directory Access: Accessing the Commerce Einstein SFTP directory is essential for securely transferring the prepared file(s) to Einstein. This step ensures that the data is uploaded to the correct location where it can be processed and utilized by Einstein for enhancing product recommendations and other features.

3. Posting Schedule: The option to post files one time or on a routine basis provides flexibility in how often the external order data is updated in Einstein. For businesses with dynamic inventories and sales data, regularly scheduled uploads can ensure that the recommendations remain relevant and up-to-date.

Steps to Implement:

1. Prepare the Data: Aggregate the brick & mortar store order data, ensuring that product IDs match those in the online catalog. Format this data into a TSV file according to Einstein's specifications.

2. Compress the File: Gzip the TSV file to reduce its size and prepare it for upload. This step is crucial for meeting the file format requirements mentioned in the documentation.

3. Access the SFTP Directory: Use the provided credentials to log into the Commerce Einstein SFTP directory. This access allows for the secure transfer of the gzipped TSV file.

4. Upload the File: Post the gzipped TSV file to the SFTP directory. Decide whether this will be a one-time upload or part of a routine schedule, depending on the business's needs and the frequency of data changes.

5. Monitor and Adjust: After the initial upload, monitor the impact on product recommendations and other Einstein features. Adjust the data preparation and upload schedule as needed to optimize performance.

This approach aligns with the best practices outlined in the Salesforce Commerce Cloud documentation for utilizing external data to enhance the capabilities of Commerce Cloud Einstein.

What three data points does commerce Einstein gather in real-time about shoppers? (3 solutions)

• 1. Mouse hover and heat mapping

• 1. Weather and humidity

• 1. Search and product view

• 1. Add to cart and purchase

• 1. Location and device type

Answer explanation

Commerce Einstein gathers real-time data about shoppers to fuel its features, focusing on enhancing the shopping experience through personalized recommendations and search functionalities. The three key data points it collects in real-time about shoppers are:

1. Clickstream Data: This includes the sequence of actions a shopper takes on the site, such as pages visited, items clicked, and searches conducted. Clickstream data is crucial for understanding the shopper's journey and interests, allowing Einstein to tailor product recommendations and search results accordingly.

2. Product Interaction Data: This refers to specific interactions with products, such as viewing product details, adding items to the cart, and making purchases. By tracking which products a shopper views, clicks on, or adds to their cart, Einstein can determine shopper preferences and suggest similar or complementary products.

3. Search Queries: The search terms entered by shoppers provide valuable insights into their immediate needs and interests. Einstein uses this data to offer personalized search recommendations, improving the relevance of search results and helping shoppers find what they're looking for more efficiently.

These data points are collected through various activities and interactions on the site, as outlined in the documentation excerpts. For example, the viewProduct, addToCart, and viewSearch activities directly contribute to gathering these real-time insights. By analyzing this data, Commerce Einstein can deliver a more personalized and engaging shopping experience, encouraging higher conversion rates and customer satisfaction.

What is the benefit of using the zone components in Commerce Einstein Recommendations API?

• 1. By using the zone component, Developers can then apply Commerce Einstein Recommendations to a customer's Salesforce Service Cloud console, so the customer's service agents can suggest products to shoppers

• 1. By using the zone component, Merchants can easily switch between recommenders in Einstein Configurator, instead of requiring the developer make code changes

• 1. By using the zone component, Developers can then use Commerce Einstein Recommendations within Page Designer

• 1. By using the zone component, Developers can switch between recommenders by making a code change each time the switch is requested by the customer

Answer explanation

The benefit of using zone components in Commerce Einstein Recommendations API lies in the added flexibility and ease of switching between different recommenders without the need for extensive code changes. Here are the key advantages of utilizing zone components:

1. Enhanced Flexibility: Zones allow you to define recommendation slots, enabling you to have multiple recommendation slots on a single page. For instance, you can have different zones for "You may also like" and "Complete the set" recommendations on a product details page. This flexibility enables you to tailor recommendations based on specific contexts or user behaviors.

2. Easy Recommender Switching: By using zones, you can easily switch between different recommenders within the same zone without the need for significant code modifications. This simplifies the process of changing recommenders for a specific zone, offering a more dynamic and adaptable approach to managing recommendations.

3. Streamlined Configuration: Zones provide a structured way to organize and manage recommendation slots, making it easier to configure and customize recommenders for different zones. This streamlined configuration process enhances the overall management of recommendation strategies and improves the efficiency of recommendation updates.

4. Improved User Experience: By leveraging zone components, you can deliver more targeted and relevant recommendations to users based on their interactions and preferences. This personalized approach enhances the user experience, increasing engagement and potentially leading to higher conversion rates.

In summary, utilizing zone components in the Commerce Einstein Recommendations API offers increased flexibility, simplified recommender switching, streamlined configuration, and improved user experience, ultimately contributing to a more personalized and effective shopping journey for customers.

As a developer implementing Commerce Einstein Product Recommendations on a category grid or landing page, what would be an example of the category slot tag?

• 1. <isslot id="recomm-category-anchor" description="Category page slot" context="category" context-object="${pdict.ProductSearchResult.category}"/>

• 1. <isslot id="recomm-category-anchor" description="Category page slot" context="global"/>

• 1. <isslot id="recomm-category-anchor" description="Category page slot" context="global" context-object="${pdict.Product}"/>

• 1. <isloop items="${suggest.product.phrases}" alias="phrase" status="loopState">

• 1. <iscache if="${!searchModel.isPersonalizedSort()}"/>

Answer explanation

Here’s how he defines a slot for a product detail page.

<isslot id="product-show-recommend" context="global" context-object="${pdict.product}"/>

Copy

He can also pass the context-object attribute to a basket or collection ( dw.util.Collection) of products. This lets Brandon create and use recommendations based on up to five selected products or all the products in the basket.

Here’s how he defines a slot for a category landing page.

<isslot id="cat-banner" context="category" context-object="${pdict.productSearchResult.category}" />

Copy

For the product details page, Vijay defines the context as global because Brandon wants to configure the content slot as global in Business Manager for this page. For the category landing page, Vijay uses the category context. With this setting, Einstein uses product categories and subcategories defined in Business Manager to create the recommendations. For a slot on either page, he must include a reference to the referring product (or category) ID as part of the context-object attribute.

Vijay already defined content slots on his storefront, so he just needs to create a template to render the recommended products. He already has a template that renders other types of product content slots, so he reuses it with a slight change.

Here’s the product template he copies to create the home_categories-m.isml template.

<iscontent type="text/html" charset="UTF-8" compact="true"/> <iscomment> should not be cached, the tiles are cached individually.</iscomment> <isif condition="${slotcontent}"> <div class="product-listing product-listing-1x4"> <h2>${slotcontent.calloutMsg}</h2> <ul class="search-result-items tiles-container"> <isloop items="${slotcontent.content}" var="product" begin="0" end="3"> <isobject object="${product}" view="recommendation"> <li class="grid-tile"> <isinclude url="${URLUtils.url('Product-HitTile', 'pid', product.ID, 'showswatches', 'true', 'showpricing', 'true', 'showpromotion', 'true', 'showrating', 'true')}"/> </li> </isobject> </isloop> </ul> </div> </isif>

Copy

Vijay uploads the new file, home_categories-m, to the staging instance. Product recommendations are dynamic, so he makes sure the entire slot isn't cached. Otherwise, every visitor will see the same recommendations. He makes sure that caching occurs only when rendering individual product tiles and not the entire template.

When working with a customer on Commerce Einstein Recommendations, is it required that the partner gain access to the Commerce Einstein Configurator Tool?

• 1. No, because it is the customer's site administrator and merchandiser's choice to create customized recommenders with rules or review Commerce insights

• 1. Yes, because the partner creates customized recommenders with rules, review Commerce Insights, or has immediate access to the zone names and API client Id.

• 1. No, because it is the customer's site administrator and merchandiser's choice to create customized recommenders with rules, review Commerce Insights, or share the zone names and API client id with their partner.

• 1. Yes, because the partner creates customized recommenders with rules, review Commerce Insights, or has immediate access to the zone names and API client id.

• 1. Yes, because the partner creates customized recommenders with rules within the Commerce Einstein Configurator

What is the correct calculation for 'Attributed Revenue' of Commerce Einstein Recommendations in Reports and Dashboards?

• 1. Attributed revenue is calculated by using the following example: A transaction totals $100; the transaction has four products purchased:A,B,C and D; A and B were recommended and clicked on in a 7 day time period; Commerce Einstein Recommendation attributed revenue is the prorated purchase amount of $100 divided by 4 products purchased; times the 2 products clicked on equaling $50. Amounts are in local currency of each transaction and conversion to USD gets applied

• 1. Attributed revenue is calculated using the associated products converted in the given time period, at the merchandise value for each product purchased, based on first-click attribution to a given recommender within a 7 day period

• 1. Attributed revenue is calculated using the associated products converted in the given time period, at the merchandise value for each product purchased, based on first-click attribution to a given recommender within a 14 day period

• 1. Commerce Einstein Attributed Revenue is not available in Commerce Cloud Reports and Dashboards

Answer explanation

Explanation:

• This method involves attributing revenue based on the associated products converted within a specific time frame (7 days).

• The calculation considers the merchandise value for each product purchased and attributes the revenue to the recommender that received the first click within the 7-day period.

• First-click attribution is crucial as it assigns revenue to the recommender that initially influenced the purchase decision, providing insights into the effectiveness of recommendations.

• By focusing on the first-click attribution within a defined time period, this approach offers a clear and specific way to attribute revenue to Commerce Einstein Recommendations, aiding in performance evaluation and optimization strategies.

This method ensures that revenue is attributed accurately to the recommendations that had the initial impact on driving the purchase, providing valuable insights into the effectiveness of Commerce Einstein Recommendations in generating revenue within a specified timeframe.

When a Merchant creates an Einstein Recommendation that employs the recommender type: product-to-product (For example: Commerce Einstein Product Recommendations on the product details page), what must be sent from the page to Commerce Einstein to inform the real-time recommendations?

• 1. No anchor is required

• 1. Content id

• 1. Product id

• 1. Category id

Answer explanation

The recommender type must always match the anchor, as follows:

• Product to Product: product-id

• Products in a Category: category-id

• Products in All Categories: none

• Recently Viewed: product-id (doesn't require and anchor)

• Complete the Set: product-id

A customer that you have been working with for many years is migrating their site from SiteGenesis to SFRA, but they are concerned that they will loose all all the previous Commerce Einstein Activities and customized recommenders in the Configurator. What steps may be done to transfer Commerce Einstein from an old site to a new site?

• 1. Migration of Commerce Einstein from an old site to a new site happens automatically.

• 1. The partner should not be involved in CC Einstein migration from a customer's old site to a new site.

• 1. Submit support ticket to override new realm site with previously deployed Einstein site.

• 1. It is not possible to transfer Commerce Einstein from one version of a site to a new version of the site.