Customer engagement is the lifeblood of any successful marketing strategy. The way brands connect with their audience can make or break a campaign’s success, driving deeper relationships or alienating customers. In today’s rapidly evolving marketing landscape, engaging customers is more complex than ever. Brands must not only deliver personalized, relevant content but also ensure that the timing and channel of delivery align with each customer’s preferences and behaviors.
Salesforce Marketing Cloud (SFMC), with its suite of AI-powered tools, offers marketers unprecedented capabilities to optimize customer engagement across channels. At the core of these capabilities is Einstein AI, Salesforce’s intelligent engine that helps marketers better understand their audience, predict behavior, and personalize interactions at scale.
In this article, we’ll explore how AI can optimize customer engagement within SFMC, focusing on Einstein’s role in personalizing experiences, predicting customer behavior, and enhancing campaign performance. We’ll reference concepts covered in previous articles, such as Day 63: Use Cases for Einstein Engagement Frequency and Day 61: Using Einstein Recommendations in SFMC, to build a comprehensive understanding of how AI works to drive engagement.
As always, this article is part of the #SalesforcewithSumit series, where we unpack SFMC’s features to help you build more effective, data-driven marketing campaigns.
The Importance of Optimizing Customer Engagement
Before diving into how AI can help optimize engagement, let’s start by discussing why engagement is so critical in today’s marketing environment. Customer engagement refers to the interactions between a brand and its customers across various touchpoints—whether it’s through email, mobile apps, social media, or web channels. The more relevant and timely these interactions are, the stronger the customer’s connection to the brand.
Engagement drives loyalty, retention, and ultimately revenue. A customer who feels understood and valued by a brand is more likely to make repeat purchases, stay subscribed to services, and become an advocate for that brand. Conversely, poorly timed or irrelevant messages can lead to disengagement, increased unsubscribes, and even brand abandonment.
In previous articles like Day 62: Setting Up Predictive Send Times with Einstein, we explored the importance of delivering messages at the right time to maximize engagement. Now, let’s see how AI can take that to the next level by not only optimizing timing but also content and delivery channels.
AI-Powered Tools for Optimizing Engagement in SFMC
Einstein AI provides several key tools that can help marketers optimize engagement throughout the customer journey. These include:
1. Einstein Engagement Frequency
Einstein Engagement Frequency analyzes past interactions with your content to predict how often customers want to hear from you. This tool helps prevent over-communication or under-communication by suggesting the optimal number of messages to send within a specific time frame.
In Day 63: Use Cases for Einstein Engagement Frequency, we discussed how this tool ensures that your customers receive communications at just the right frequency—keeping them engaged without causing fatigue. With the right balance, your engagement rates will increase, and customers are less likely to unsubscribe or become disengaged due to message overload.
2. Einstein Recommendations
Einstein Recommendations is another powerful tool for optimizing engagement by delivering personalized content to each customer. It uses machine learning algorithms to analyze past behavior—such as product views, purchase history, and interactions with your content—and then predicts what content or products the customer is most likely to engage with.
As we discussed in Day 61: Using Einstein Recommendations in SFMC, this tool enables marketers to deliver highly targeted, relevant content to each individual customer. For example, an email promoting a new product can be tailored to recommend items that align with each recipient’s preferences and past purchase behavior, thereby increasing the likelihood of engagement.
3. Einstein Predictive Scores
Einstein Predictive Scores help marketers understand the likelihood of certain customer behaviors, such as clicks, opens, purchases, and even customer attrition. This tool assigns scores based on historical data, giving marketers valuable insights into which customers are more likely to engage and which may need re-engagement efforts.
Predictive scores allow you to prioritize high-value customers or those at risk of disengagement. In Day 59: Using Dynamic Content in SFMC Emails, we discussed how predictive scores can be used to deliver dynamic content that adjusts based on the recipient’s likelihood to engage, further boosting the effectiveness of your campaigns.
4. Einstein Send Time Optimization
Timing is everything in marketing. Einstein Send Time Optimization takes the guesswork out of determining when to send emails or mobile notifications by analyzing when each customer is most likely to engage. By delivering messages at the optimal time for each individual, you significantly improve the chances of engagement.
In Day 62: Setting Up Predictive Send Times with Einstein, we explored the impact of send time optimization on campaign performance. Customers are more likely to open emails and engage with content when it arrives at a time that suits their habits. This is particularly important for global brands with audiences in multiple time zones.
AI in Action: Personalizing the Entire Customer Journey
The real power of AI in SFMC lies in its ability to optimize not just individual campaigns but the entire customer journey. Let’s take a closer look at how AI can be leveraged at different stages of the journey to maximize engagement:
1. Attracting New Customers
AI-powered tools like Einstein Recommendations and Predictive Scores can be used at the top of the funnel to attract new customers. By analyzing data from similar customers, Einstein can predict which types of content, products, or offers are most likely to appeal to new prospects.
For example, let’s say you’re running an email campaign to introduce your brand to a new audience. Einstein can recommend personalized content based on demographic data or behaviors from similar profiles, ensuring that each recipient receives the most relevant introduction to your brand. This leads to higher open rates, more clicks, and ultimately, a higher conversion rate.
2. Nurturing Leads and Prospects
Once a prospect has entered your ecosystem—whether by subscribing to your newsletter, visiting your website, or making an initial purchase—AI can help nurture that relationship. Einstein Engagement Frequency ensures that you’re sending the right number of communications to keep prospects interested without overwhelming them, while Send Time Optimization ensures those messages are delivered when the prospect is most likely to engage.
In Day 30: Introduction to Journey Builder, we discussed the importance of mapping out the customer journey and delivering personalized messages at key touchpoints. AI takes this a step further by making real-time adjustments based on customer behavior, ensuring that your messages are always relevant and timely.
3. Driving Conversions
When it comes to turning prospects into customers, personalization is key. Einstein Recommendations allows you to serve personalized product recommendations based on each customer’s browsing and purchase history. For example, if a customer frequently browses sports equipment but hasn’t made a purchase yet, Einstein can recommend products that align with their interests, increasing the likelihood of conversion.
We covered the importance of personalized recommendations in Day 58: Introduction to Personalization in SFMC, where we highlighted how tailoring content to individual preferences can lead to higher conversion rates. By leveraging AI, you can ensure that every interaction feels personalized and relevant to the customer.
4. Retaining Customers
Customer retention is a critical part of long-term success, and AI can help you keep your existing customers engaged. Einstein Predictive Scores can identify customers who are at risk of disengaging, allowing you to take proactive steps to re-engage them. Whether it’s sending a special offer, a personalized product recommendation, or a re-engagement email, AI helps you target the right customers at the right time.
In Day 66: Customer Profiling and How to Leverage It, we discussed how detailed customer profiles can help drive retention by ensuring that you deliver relevant content based on each customer’s preferences and behavior. AI takes this a step further by predicting future behavior and helping you intervene before customers disengage.
5. Re-Engaging Lapsed Customers
Even when a customer has become inactive or stopped engaging with your content, AI can help bring them back into the fold. By analyzing past behaviors, Einstein Predictive Scores can identify which lapsed customers are most likely to re-engage and which type of content will be most effective in doing so.
For example, you can create re-engagement campaigns that target customers with high potential for reactivation based on their predictive scores. AI can recommend the best offers, content, or messaging to win them back, ensuring that your re-engagement efforts are as effective as possible.
Measuring the Impact of AI-Driven Engagement
One of the great benefits of AI in SFMC is that it not only helps you optimize engagement but also provides real-time insights into the performance of your campaigns. Einstein tracks key metrics such as open rates, click-through rates, and conversions, allowing you to measure the impact of AI-driven engagement.
1. Increased Engagement Rates
By delivering personalized content at the optimal time, AI significantly boosts engagement rates. Customers are more likely to open emails, click on links, and engage with your brand when the content is relevant and delivered at the right time.
2. Improved Customer Retention
AI helps you maintain consistent engagement with customers, keeping them connected to your brand. By preventing over-communication and sending personalized offers, you increase the chances of retaining customers over the long term.
3. Higher Conversion Rates
AI-powered recommendations ensure that customers receive offers and products that align with their preferences, resulting in higher conversion rates. Personalized recommendations, dynamic content, and predictive scores all contribute to more effective campaigns.
4. Reduced Unsubscribes
By optimizing message frequency and delivering content that customers actually want to receive, AI helps reduce the number of unsubscribes and spam complaints, keeping your audience engaged and interested in your brand.
Conclusion: The Future of Customer Engagement with AI
As the marketing landscape continues to evolve, AI-powered tools like Einstein in SFMC are becoming essential for optimizing customer engagement. From predicting behavior and personalizing content to optimizing send times and preventing disengagement, AI provides the insights and automation needed to keep customers engaged across their entire journey.
To fully leverage these tools, marketers must adopt a data-driven mindset, continually analyzing customer behaviors, testing AI-generated recommendations, and refining strategies based on performance. The future of marketing lies in AI, and with tools like Einstein AI, marketers can deliver more personalized, relevant, and effective customer experiences than ever before.
For more on how AI can drive marketing success, be sure to check out previous articles like Day 67: AI-Powered Predictive Content: How It Works and Day 61: Using Einstein Recommendations in SFMC.
Join me next time as we continue to explore advanced AI-driven strategies in the #SalesforcewithSumit series!