Personalize Interactions with Einstein for Marketing Cloud
After completing this unit, you’ll be able to:
- List the Einstein features that help create personalized experiences for consumers.
- Understand how to include Einstein in your marketing campaigns.
The productivity and performance improvements gained from implementing AI tools are a key business reason for brands to integrate them into their technology strategy.
Another equally — if not more important — reason to implement AI tools is to deliver exceptional customer experiences. And that can mean many different things, from discovering more about an audience or segmenting to match them with the right content, to personalizing products and offers for individual tastes, to optimizing engagement throughout the journey like where or how often a customer prefers to be engaged. You’re starting to get the picture.
So now let’s learn about some of the Einstein features that help marketers personalize the entire customer journey.
There’s a common saying: "You don’t know what you don’t know", meaning that we can’t always identify gaps in our knowledge about how or why something works. This holds true in marketing. Often, we build experiences, share content, and send offers without considering the audience.
Einstein Segmentation takes a fresh look at a marketer’s audiences and segments using machine learning. It’s able to optimize their targeting strategy with new insights and personas.
Consider the following example:
Northern Trail Outfitters is an outdoor gear and apparel retailer serving the outdoor enthusiast market. They know they have a large segment of avid hikers and target them with relevant hiking content and offers. But when they applied Einstein Segmentation, they found distinct personas like Glampers, who appreciate higher-quality gear and creature comforts while on the trail, and Trail Techies, who are interested in the most high-tech, innovative gear. Now they can adjust their outreach strategy to target Glampers and Trail Techies with different campaigns and content as it makes sense, such as highlighting the on-the-go coffee maker for Glampers versus their new GPS watch for the Trail Techies in their next ad campaign.
When designing a campaign, it’s crucial to understand the recipients. Segmentation is often based on subscribers’ past behaviors and static attributes.
Einstein Engagement Scoring is an advanced subscriber segmentation tool that assigns a score based on a subscriber’s likelihood to:
- Open an email.
- Click an email.
- Unsubscribe from your list.
- Convert on the web.
In addition to each subscriber score, Einstein groups customers in more recognizable personas:
- Loyalists: Engage often
- Selective subscribers: Open infrequently, but tend to click when they do
- Window shoppers: Open regularly, but click sparingly
- Dormant/Winbacks: Unlikely to engage at all
Einstein also offers insight into why subscribers take action, and predictions update daily for each business unit and subscriber so the data is always fresh.
Some example use cases include:
- Increasing acquisition and conversions - Send loyalists special offers, cross-sell campaigns, refer-a-friend or event-signup campaigns OR use as lookalike audiences to find new customers that look like your best ones.
- Engaging and inspiring existing customers - Leverage the scores and segments to define a targeting strategy, such as encouraging your selective shoppers to open with action oriented subject lines or sending them on campaigns aligned to their known preferences.
- Decreasing churn - Use the dormant subscribers as a target audience for a winback campaign and then refine your subscriber lists by archiving subscribers who you are unsuccessful at winning back.
- Enhancing email reporting - Add personas your custom or advanced reports in Marketing Cloud to discover new insights.
Einstein Split builds on Einstein Engagement Scoring to deliver offers with a customer’s future behavior in mind.
The pre-configured split activity available in the Marketing Cloud Journey Builder tool sets up separate engagement paths based on a subscriber’s predicted actions or persona. When you know email isn’t a preferred channel, you can test different channels, such as social ads or direct mail.
You’ve probably been browsing a website only to find yourself clicking on a related item that catches your interest. That experience was likely powered by a recommendation engine. It automates product and content curation to increase sales or engagement goals.
Einstein Recommendations collects behavior from channels such as web, email, mobile, and even offline customer records, then uses machine learning models to create a profile of preferences for each consumer. Using this profile, Einstein generates recommendations automatically within engagement channels.
Here are two examples of Einstein Recommendations in action:
- Cross-sell related items to increase total sales: Use Einstein to intelligently recommend items that complement a user’s past purchases and are personalized based on their other browsing and affinity characteristics. If they bought pants, maybe they’d next like to buy a shirt or shoes to go with it.
- Nurture prospects and customers with relevant content: Sometimes the best approach is supporting content that promotes added value to the customer. A B2B company may use Einstein to recommend relevant blog posts or webinars based on a prospect’s topic or category affinity. A B2C company can recommend how-to articles as a complement to their product or service offerings.
With customers receiving tons of emails daily, they can quickly become overwhelmed. It’s imperative that marketers send the right amount to stay top of mind, but avoid becoming a nuisance by sending too many.
Einstein Engagement Frequency identifies how many emails to send and which subscribers are emailed too often (or too little). This improves engagement while preserving customer satisfaction.
Adjusting email send frequency:
- Reduces list unsubscribes: Suppress oversaturated subscribers from future sends or journeys.
- Optimizes spend: Save money to reinvest in other channels or marketing strategies.
- Drives incremental sales opportunities: Send undersaturated subscribers on a retargeting journey to increase exposure and conversion.
If you tend to play favorites with more recent messages at the top of your inbox when you are checking, don’t worry, you’re not alone, most people do it. In fact, it’s a well known phenomenon dubbed position bias. For many brands, being at the top of the inbox when the subscriber is actively engaging is the best way to ensure their message is seen.
Einstein Send Time Optimization aims to help marketers at brands cut through the noise, especially as the competition for the inbox and customer mindshare increases with more and more messages being sent.
Review an intuitive dashboard of send and engagement patterns as you plan your email and campaign send strategy or simply use a pre-built, machine learning driven activity in Journey Builder to deliver your message at the time that each individual is predicted to most likely to engage with it.
As you see throughout this module, it’s now imperative for marketers to weave artificial intelligence into their customer engagement strategies in order to solve their myriad challenges and use cases. The good news is that marketers who adopt AI tools and technology see great results reflected in both improved performance in their execution of programs and increased customer engagement, satisfaction, and loyalty. And making customers happy is something all marketers can agree on.