Case Study: Smarter Personalization With Salesforce Einstein AI

Background

The organization had been using Salesforce Marketing Cloud effectively for email automation and outbound communication. Campaigns were timely, the system ran smoothly, and the subscriber base kept growing. Yet, performance metrics such as open rates, click-throughs, and conversions had stalled. Messages were being delivered but no longer driving engagement or influencing customers.

The main issue was limited personalization. All subscribers received identical emails, with no variation based on past behavior or customer lifecycle. This generic approach led to inbox fatigue and declining interest. Although the marketing team had access to rich customer data, they lacked the intelligence to convert it into meaningful insights. Segmentation and targeting were mostly manual and relied on guesswork, demanding high effort for minimal improvement.

To address this, the company set new goals: deliver personalized content, send messages at the best times for each user, align offers with individual interests, and boost conversions through data-driven recommendations—all while reducing manual work. To realize this vision and evolve Marketing Cloud into a predictive personalization platform, they implemented Salesforce Einstein as the enabler.

Challenges Before the Solution

During the discovery phase, several operational and performance barriers became clear. These challenges were preventing the marketing team from maximizing the impact of their campaigns:

1. Generic Email Approach: Every subscriber received the same message, regardless of purchase history, browsing behavior, or engagement patterns. This limited personalization led to disengagement and unsubscribes over time.
   
   

2. Low Email Engagement Metrics: Open rates and click-through rates remained consistently below industry benchmarks. Emails lacked relevance, reducing their motivation for recipients to engage.

3. Labor-Intensive Content Production: Teams spent significant time manually selecting assets for each campaign. The process was slow, required constant planning, and was difficult to maintain as the subscriber base grew.
   
   

4. Lack of Predictive Intelligence: There was no way to determine which content-or which call-to-action-would be most effective for each subscriber. Decision-making was based on assumptions rather than behavioral insights.
   
   

5. No Send Time Optimization: Emails were sent simultaneously to all contacts, regardless of when they typically engaged. This resulted in missed opportunities and reduced visibility in crowded inboxes.

Collectively, these challenges made personalization inefficient, inconsistent, and nearly impossible to scale without automation and intelligence.

How We Implemented the Einstein-Powered System

To unlock true personalization at scale, the solution needed to go beyond traditional segmentation and automated sending. The goal was to build a system that could think, adapt, and optimize continuously based on customer signals. To achieve this, we introduced an Einstein-driven framework inside Salesforce Marketing Cloud that combined AI decisioning with automated journey execution.

The implementation was carried out in structured phases to ensure the technology worked seamlessly, the team could adopt it confidently, and the improvements could be measured progressively.

Phase 1: Preparing the Content and Data Foundation

Before Einstein could personalize communications, it needed organized inputs to learn from. We started by restructuring the content ecosystem and aligning it with the available customer data.

We:

• Built a centralized content catalogue to store images, offers, banners, and messaging blocks
• Applied detailed labels such as categories, tags, audience types, and expiration dates
• Connected customer attributes such as purchase history, behavioral signals, engagement trends, and preference data
• Activated Einstein Content Selection and Send Time Optimization to allow the system to begin training on real data
  
  

This foundational phase made the platform AI-ready. Instead of guessing which content belonged to which audience, Einstein now had a structured dataset to analyze and learn from.

Phase 2: Activating Einstein Content Selection

Once the content was categorized and mapped, we enabled Einstein Content Selection to automatically decide which assets should appear for which audience.

Instead of marketers manually selecting content for every audience type, Einstein evaluated each subscriber in real time using factors such as:

• Recent browsing activity
• Purchase and conversion history
• Interaction patterns with previous campaigns
• Stated preferences and inferred interests
• AI-generated affinity scores
  
  

As a result, every email became unique. Two subscribers receiving the same campaign could see completely different banners, CTAs, or product recommendations -all tailored to what they were most likely to engage with.

Phase 3: Deploying Send Time Optimization (STO)

Next, we focused on when messages were delivered. Previously, all subscribers received an email at the same time, regardless of when they typically opened their inbox.

Einstein Send Time Optimization analyzed historical engagement signals including:

• Open timestamps
• Device usage patterns
• Time zone behavior
• Frequency and timing of previous interactions
  
  

Then, instead of sending in bulk, the system scheduled each email at the exact hour each subscriber was statistically most likely to open.

This approach ensured the message landed at the ideal moment -improving both visibility and the probability of engagement.

Phase 4: Building Smart, Adaptive Customer Journeys

With content and timing optimized, the next step was transforming the customer journey itself. We redesigned email journeys so that they no longer followed a fixed script but instead adapted based on subscriber behavior.

The new journeys were configured to:

• Adjust messaging based on engagement or inactivity
• Swap content dynamically using Einstein’s recommendations
• Trigger fallback, reminder, or promotional messaging only when meaningful
• Learn and refine future touchpoints based on how users responded
  
  

Instead of static campaigns, the result was a living journey experience that evolved with each subscriber interaction.

Phase 5: Continuous Optimization Using Einstein Insights

Once the system was live, optimization became ongoing and intelligence-driven. Instead of relying on manual A/B tests or intuition, the team now received AI-powered recommendations for improvement.

Einstein insights guided enhancements in areas such as:

• Best-performing content formats
• Most effective subject lines
• Ideal send frequency and cadence
• High-engagement content themes and keywords
  
  

This turned campaign evolution into a continuous improvement cycle -powered by AI rather than human effort.

Outcomes Delivered

• 40% Increase in Email Open Rates: Optimized send times improved deliverability performance and attention relevance.
• 20% Lift in Click-Through Rates: Dynamic content recommendations ensured subscribers saw messaging aligned with their interests.
• 15% Increase in Revenue From Email: Personalized messaging increased conversions and average order value.
• Scalable AI-Driven Personalization Model: The system now continuously learns and improves, reducing manual workload and maintaining consistency.
• Improved Subscriber Experience and Retention: Customers receive messages that feel personal, timely, and relevant -improving satisfaction and reducing unsubscribes.
  

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