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Everything you wanted to know about the Einstein Engagement Scoring Feature

Everything you wanted to know about the Einstein Engagement Scoring Feature

This is the second article in the series of articles exploring the Salesforce Marketing Cloud Einstein feature. We have started the series with the first article featuring the Einstein Content Selection feature (divided into part 1 and part 2).

In this blog, we shall be focusing on Einstein Engagement Scoring from the house of Einstein- The Artificial Intelligence [AI] product from Salesforce Marketing Cloud.  

Einstein Engagement Scoring predicts the probability of the contact or subscriber [for email] to engage with your marketing content like Emails & Mobile push messages. In the background, Einstein uses the existing customer data stored in Marketing Cloud and its machine learning capabilities to generate predictive models. These models then assign scores to all the contacts within Salesforce Marketing Cloud. These scores predict the likelihood of the contact to open or click the emails and engage with push notifications. 

Einstein Engagement Scoring comes as a part of the following Editions in Salesforce Marketing Cloud: 

Additionally, you can purchase this as an add-on to the Professional Edition of Marketing Cloud. This feature needs to be activated from the SetUp option in Marketing Cloud. 

Benefits of Einstein Engagement Scoring 

Einstein Engagement Scoring provides multiple benefits. Let’s explore a few: 

How it works  

The Einstein Engagement Scoring feature predicts how a particular contact will engage with your brand and their likelihood of making a purchase. Now, this analysis is done based on the contact’s email engagement data for the past 90 days. This data includes the number of emails sent to that subscriber, clicks, opens, etc..  

Einstein also considers other factors to determine the subscriber engagement with the emails. These factors can range from email clients used by the contact to the device used to access emails to the frequency of emails opens. These factors clubbed with the engagement data of the subscriber help Einstein to predict the likelihood of the subscriber opening the email.  

Einstein then groups these subscribers into various personas based on the number crunching as detailed above: 

These personas are used to make prediction models over the next 14 days for the subscribers who are most likely to open and click the emails and hence more likely to make a purchase and stay subscribed. 

Salesforce Marketing Cloud recalculates the subscriber scores on a Business Unit level to make sure that the predictions are made for BUs which are actively sending the emails. We don’t want predictions made on BUs which are used as sandboxes or used as Admin functions. 

Einstein Engagement Scoring feature saves the engagement data of subscribers in the below data extension: 

These data extensions can be accessed via Email Studio or Contact Builder within Salesforce Marketing Cloud. 

Einstein Engagement scoring Use cases 

Based on the user personas, there could be various use cases that will fit the scenarios and are listed below: 

How To use Einstein Engagement Scoring   

Salesforce Marketing Cloud has introduced Einstein Spilt activity within Journey Builder to help Marketers target the specific set of customers with various options as listed below: 

Below image shows the various settings of the Einstein Split Activity.  

The below image displays Persona Split in the Journey Builder canvas.  

Wrap Up

We’ve listed the various  benefits of the Einstein Engagement Scoring feature and also elaborated on the various use cases for its applicability. We hope the use cases will help you  use Einstein Engagement scoring in the right scenarios.For a detailed understanding of Einstein Engagement Scoring, please visit this link.

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