Access Einstein predicted email engagement data is on the email dashboard, categorizing contacts into personas based on engagement thresholds. Utilize detail pages to dig into prediction specifics and assess your audience health.
Einstein Engagement Scoring is a feature that anticipates how a contact will engage with a brand and the likelihood of them making a purchase. This analysis relies on the contact’s email engagement data from the past 90 days, encompassing metrics like the number of emails sent, clicks, opens, etc.
Einstein Predictive Email Engagement Scoring
Einstein Predictive Email, specifically the Engagement Scoring aspect, foresees consumer engagement with email and MobilePush messaging. Leveraging customer data and machine learning, it creates predictive model. By assigning scores to contacts it indicates their likelihood to engage with emails and interact with push notifications.
Salesforce Marketing Cloud introduces the Einstein Split activity in Journey Builder. Thus enabling marketers to target segments based on predicted email engagement scores. Different decision splits, such as Persona Split, Web Conversion Likelihood Split, Click Likelihood Split, Subscription Likelihood Split, and Open Likelihood Split, allow precise targeting based on various engagement factors.
The email engagement heat map classifies subscribers into personas based on predicted email engagement:
- Loyalists: High open and click engagement
- Window Shoppers: High open and low click engagement
- Selective Subscribers: Low open and high click engagement
- Winback/Dormant: Low open and click engagement
The clickTime Comparison feature illustrates changes in subscriber personas over time, providing insights into messaging resonance. For instance, an increase of 10,000 subscribers may show a 5,000 increase in the Loyalist bucket, with 70% transitioning from the Selective Subscriber persona. To utilize Einstein Engagement Scoring for Persona changes over time, a minimum of 14 days of active subscriber evaluation in each business unit is required.
Einstein Engagement Scoring
Einstein Engagement Scoring utilizes existing customer data and machine learning capabilities in Marketing Cloud to predict the probability of a contact engaging with marketing content. This is including emails and push messages. The generated scores indicate the likelihood of the contact opening or clicking emails and engaging with push notifications.