How to Create Personalized Recommendations for Commerce in Salesforce?

How to Create Personalized Recommendations for Commerce in Salesforce?


Modern-age shoppers are always on the lookout for improved search experiences. If the retailers need to build their website based on personalized recommendations then ideally Commerce Cloud Einstein is all that is needed. It is the artificial intelligence technology(AI), already built inside the platform.

However, businesses would like to achieve higher conversions and for this, they must resort to not only increase the customer engagement for the eCommerce site but must also create personalized recommendations across through all messaging channels.

First, let us try to find out how the shoppers are benefited by Einstein Search Recommendations.

Einstein Search Recommendations

Shoppers get benefited by Einstein Search recommendations in the following ways:

  • Reduce abandonment rate in the Search Results page.
  • Enhance shopper experience with Storefront search.  
  • Minimize the zero-search results.

Moreover, Einstein Search Recommendations help to personalize the type-ahead search, Einstein uses machine learning algorithms. The algorithms are used for identifying the search phrases, which are relevant to the shoppers and this then creates the recommendations.

Furthermore, the search results are updated in real-time by Einstein. This is based on site-wide search activity with respect to the shopper’s location and device type. As an example to cite, Einstein searches through the device and location and returns a phrase, if possible. Otherwise, it searches ahead with a wider pool of data across devices and locations until they come up with pertinent phrases.

For all those brands and retailers who are using Marketing Cloud as well as Commerce Cloud, personalized recommendations for the customers are all based on some steps and considerations. The recommendations in Marketing Cloud are populated with the Commerce Cloud.

Both Commerce Cloud and Marketing Cloud provide personalized recommendations – onsite personalization is offered by Commerce Cloud and email channel recommendations by Marketing Cloud. Marketing Cloud drive users to click through an email and Commerce Cloud recommendations help drive sales and engagement for the commerce site.

What are the Key Steps for creating Personalized Recommendations?

Here are the key steps:

Set up Commerce Cloud to Marketing Cloud Connector

This is about helping to get your product stock keeping units (SKUs) and catalogs into the Marketing Cloud and subsequently used to drive the product recommendations.

Collection from Commerce Cloud

This is collecting the Commerce Cloud so that it can be utilized by Marketing Cloud to embed the personalized recommendations inside the email. One can track the product views, purchases and carts through the Collect.js in the Commerce Cloud.

This way the behavioral data is collected and it can be then used to build individual profiles and capture affinity. This not only helps behavioral triggers such as abandoned carts or even products browsed but also personalized recommendations.  

Collect Data and Build Data Profiles

Once the data collection gets over, 30 days of data tracking is needed to build robust data profiles. This ensures personalized recommendations while sending emails.

Add Product Catalog from Marketing Cloud

The product catalog needs to be added to the Marketing Cloud and this has to match with the product identifiers on the website via Collect.js for purchases, views, and carts.

There are two ways to add the catalog – through streaming updates and daily flat-file catalog feed. The catalog has to be built via an additional Javascript snippet that is added to the website.

Create Email Templates

The email templates are built based on the recommendations of the Personalization Builder. This Personalization Builder, the artificial intelligence of  Marketing Cloud, offers a rule manager to create business logic with relevance to the use cases. The recommendations are limited to a specific brand, product type, and attributes. However, it is recommended to let the predictive models find the right personalized recommendations.  

Finally, we will discuss the tracking offered the personalized recommendation results.

How to Track Personalized Recommendations?

It is possible to run reports within Marketing Cloud Einstein for tracking the recommendations. The reports depict parameters such as conversion rates, click through rates, and revenue generation through Marketing Cloud recommendations.

In addition to the above, Marketing Cloud recommendations are supportive of Urchin Tracking Module parameters (that are ideal for tracking the efficacy of online marketing campaigns across publishing media and traffic sources). This enables you to track your results in Google Analytics. Finally, it is important to track and compare the results in the analytics tools currently used by you.

Conclusion

The power of Commerce Cloud and Marketing Cloud recommendations helps create experiences and this enables your brand to stand apart from peers. You can increase your business conversions by leveraging the strengths of each tool.

Setting up the considerations ensures that you provide the best experiences across all channels. This is what drives revenue. You can achieve further by following the step-by-step guide on the Solution Kit- meant for personalized recommendations.


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