Salesforce Marketing Cloud’s Einstein Engagement Scoring is a powerful tool that uses AI to predict how likely your subscribers are to engage with your emails. By leveraging this tool, you can make data-driven decisions to optimize your campaigns and achieve better results.
Let’s dive into the setup and utilization of Einstein Engagement Scoring so you can start making smarter marketing decisions and increasing your campaign effectiveness.
What is Einstein Engagement Scoring?
Einstein Engagement Scoring is an AI-powered feature within Salesforce Marketing Cloud that predicts the likelihood of your subscribers engaging with your email content. It does this by analyzing past behavior and assigning predictive scores. It predicts how the audience will engage with email marketing campaigns and programs over the next 14 days, analyzing the past 90 days of subscriber engagement.
Core Components:
1. Persona:
Categorize customers into the following segments based on their engagement with your emails. (Fig.1.1)
- Loyalists: Highly engaged users who frequently open and click on emails.
- Window Shoppers: Users who often open emails but rarely click through.
- Selective Subscribers: Users who open and click on emails occasionally.
- Winback/Dormant: Users who rarely engage and may need re-engagement strategies.
Fig.1.1. Persona Segments
2. Engagement Likelihood:
Engagement Likelihoods, including Open Likelihood, Click Likelihood, Unsubscribe Likelihood, and Web Conversion Likelihood (Fig.1.2), provide predictive insights into how subscribers interact with your emails. These components help tailor strategies to enhance engagement and reduce unsubscribes.
Fig.1.2. Einstein Engagement Scoring Components
Einstein Engagement Scoring Predictive Data Extension:
In Salesforce Marketing Cloud, the Einstein_MC_Predictive_Scores and Einstein_MC_MobilePush_Scores data extensions are designed to help you leverage Einstein’s predictive analytics for email and mobile push campaigns. Scoring metrics are updated weekly. Don’t delete or modify these data extensions. Here’s a detailed look at each:
#1 Einstein_MC_Predictive_Scores:
- This data extension contains predictive scores related to email engagement, allowing you to better understand and anticipate subscriber behavior. (Fig.2.1)
Fig.2.1. Einstein_MC_Predictive_Scores Predictive Data Extension Properties
How the Predictive Data Extension Helps in Scoring?
Predictive data extension helps marketers by providing detailed predictive scores and likelihoods for each subscriber, allowing them to tailor email campaigns effectively (Fig.2.2). By analyzing these scores, marketers can:
- Segment Audiences: Create highly targeted segments based on likelihood scores and personas.
- Personalized Content: Deliver personalized email content that matches the likelihood of engagement of different subscribers.
- Optimize Campaigns: Use the scores to focus efforts on high-value subscribers, re-engage dormant ones, and reduce unsubscribe rates.
- Improve Conversion Rates: Focus on subscribers with high conversion likelihood, tailoring offers and calls to action accordingly.
Fig.2.2. Einstein_MC_Predictive_Scores Predictive Data Extension Records
#2 Einstein_MC_MobilePush_Scores:
- This data extension focuses on predictive scores for mobile push notifications, helping you optimize your mobile engagement strategies.
Einstein Engagement Scoring for Journey Builder:
1. Login to Salesforce Marketing Cloud and open Journey Builder.
2. Create or open a journey. (Fig.3.1)
Fig.3.1. Journey Builder
3. Drag Scoring Split under flow control, configure with Einstein Engagement Scoring. (Fig.3.2)
Fig.3.2. Einstein Scoring Split
- Persona-Based Split in Journey Builder:
A Persona-Based Split in Journey Builder allows you to segment your audience based on the personas identified by Einstein Engagement Scoring (Fig.3.2.1). These personas, like “Loyalists, Selective Shoppers, Window Shoppers, and Winback,“ are generated by analyzing subscriber behaviors. (Fig.3.2.2)
Fig.3.2.1. Einstein Scoring Split in Journey Builder – Persona Based
Fig.3.2.2. Einstein Scoring Split – Persona Based
- Engage Customers by Likelihood:
Einstein Engagement Scoring uses a prediction model based on how often subscribers open emails, click on links, and their subscription status. It sorts each subscriber into four categories: Open Likelihood, Click Likelihood, Unsubscribe Likelihood, and Web Conversion Likelihood (Fig.3.3.1). Within each category, subscribers are further ranked as Most Likely, More Likely, Less Likely, or Least Likely to help you understand their engagement levels better. (Fig.3.3.2)
Fig.3.3.1. Einstein Scoring Split – Engage Customers by Likelihood
Fig.3.3.2. Einstein Scoring Split in Journey Builder – Engage Customers by Likelihood
4. Test and activate the journey.
Conclusion:
Incorporating Einstein Engagement Scoring into your marketing strategy is a game-changer for targeted and effective campaigns. By using Scoring Split, you can precisely segment your audience based on their engagement levels, ensuring that each customer receives content that resonates with their interests and behaviors. This data-driven approach not only enhances personalization but also maximizes the impact of your marketing efforts, leading to higher engagement, better conversion rates, and increased customer loyalty.
Leverage Einstein Engagement Scoring to set your business apart!
Einstein Engagement Scoring is a game-changer for email marketers. Predicting subscriber behavior enables you to tailor your campaigns for maximum impact, driving higher engagement and conversions.
Marmato Digital can help you implement Salesforce Marketing Cloud’s Einstein Engagement Scoring to make smarter marketing decisions. By leveraging AI-driven insights, you’ll be able to understand your audience better and target the right customers with personalized content, ensuring maximum impact and higher engagement rates. Let our expert team guide you in optimizing your campaigns and taking your marketing strategy to the next level. Contact us today to get started!
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