Personalized interaction has always been a challenge, and this is seen in its many monikers: personalization, recommendation systems, content targeting. The key to successful customization is through segmentation of customer bases. Based on known data about customers/users, custom interactions are created for them, hoping to be more effective than generic interaction. These segments can either be explicit (based on the user/customer directly telling you what they like and don’t like) or implied (data-driven insights into their behavior and interactions).
Much of the information we provide is in the Salesforce Marketing Cloud (SFMC), but little is used effectively to customize interactions.
First, we will see what it would be like if we didn’t personalize the interaction, then follow up with valuable tips for how to start better to target our users with the data available to us.
Let’s say we want to build a model of my customer base through segmentation and then personalize interactions for each group. A simple strategy would be to divide customers into buckets based on what they like or don’t like.
For example, we could have two buckets for customers who like sports and two for those who don’t. When a user comes to my site or performs an action that the Salesforce Marketing Cloud can track, we’ll put them in one of these four buckets based on which bucket they are most likely to belong. If we believe they like sports, we’ll put them into bucket #1; if not, bucket #2. Now that the user has been placed into a bucket, you can create different interactions for each one.
The problem with this strategy is that it doesn’t leverage any user information. Each bucket is homogeneous, so the only possible personalized interaction is to show different content to each segment. This would result in both wasted resources and a poor user experience.
A more efficient strategy would be the following: for each customer, keep track of what they’ve done or shown interest in through interactions on your site/app/platform. For example, if a user has watched two videos and downloaded a whitepaper, we’ll add them to bucket #1 (a high-interest group).
Now that we have built a model based on personalization, we can use it in Marketing Cloud. Instead of having to create tactics like “sports” or “outdoors”, you can create segments like “high interest” and “low interest”. Whenever a user comes to the site or performs an action, we’ll know if they belong in bucket #1 or bucket #2. This allows personalized interactions that use the data and insights you’ve collected earlier.
If this was an eCommerce site, you could show the high-interest user content whenever they’re on the site while offering a low-interest range when they’re not. This would allow you to maximize your conversion rates by focusing only on clients interested in your product.
Personalization can be done effectively if you have data about what clients are specifically looking for. We can use this information to create even more custom interactions like proposed videos, recommended products, and more.
Here are the top 5 benefits of personalized interactions with SFMC.
With personalized interactions, you can stay front of mind for your customers by sending them personalized messages triggered by cues from their behavior.
Your customers can get messaging across marketing channels, from targeted emails to dynamic website content, all with the same core message.
Customized interactions bring together all the advanced analytics you need to know about your customers and their behaviors so that you can understand them. That means you’ll be able to make smarter decisions around strategy and campaign direction.
With personalized interactions, each message gets a high-quality score based on their customer’s interactions. In this way, you can ensure that your assets work effectively throughout your client’s journey.
Tailored interactions help marketers manage how they send targeted messages to users who have signed up for a whitelist or an unsubscribe list, ensuring 100% inbox delivery.