Explore Marketing Cloud Personalization Concepts
After completing this unit, you’ll be able to:
- Define segments and how they are used.
- Explain how campaigns and templates work in Marketing Cloud Personalization.
- List the elements of Einstein Recipes.
Marketing Cloud Personalization Concepts
Now that you’ve learned about the capabilities and terms used in Marketing Cloud Personalization, let’s cover some of the concepts behind its functionality. In this unit we cover segments, campaigns and templates, and Einstein Recipes.
Since customers drive our business, let’s start with segments. Segments are groups of accounts or individuals that are updated in real-time based on criteria you define. User segments are segments of individuals who visit your site or log into your application. Account segments are segments of companies or organizations that individual users belong to. They are typically used to group visitors from a particular organization, for example, employees of Salesforce.
Ways to Use Segments
Segments can be used in several ways. Here are just a few.
- Personalize campaigns. Show content created for a specific user segment. For example, show new visitors an introductory video about your company the first time they visit your site, but not the next time.
- Review member profiles. Each segment member, whether a user or an account, has a profile screen that shows behavioral data. View trends and compare data to gain insight into member preferences.
- Import or export segment data. Sort and export segments with captured rule data. For example, sync with your CRM so you can include everything Marketing Cloud Personalization has captured about a lead, contact, or account.
Add New Segments
Once enabled in your account, you can add new rule-based segments, manual segments, or use existing subscriber lists. Rule-based segments are powerful because they allow you to define criteria for segmentation. What conditions do users or accounts need to meet to be included in the segment you’ve defined? Create simple or complex rules based on your implementation.
Personalize With Campaigns
Once you have identified your users, captured data, and segmented them into audiences, you can now target them with campaigns. Campaigns are at the heart of personalization, but the word campaign can have several different meanings. In Marketing Cloud Personalization, campaigns are channel-specific initiatives that use templates to identify areas or content zones that can be personalized. From a process perspective, in order to create a campaign, you first need a template—either from the templates within the app or a developer-created custom template. Think of a template as the foundation of a house. While there are a variety of channel types available in Marketing Cloud Personalization, this module focuses on the web.
A web template is a reusable framework that is used to create web personalization campaigns. Templates define what a campaign can render, from recommendations to images. It also defines what campaign creators can configure, such as the algorithms powering the recommendations, message copy, buttons, and so on. To build templates, developers use content zones, which are areas on your website that are eligible for personalization. An example of a content zone is a website’s homepage hero banner, which could be personalized with various offers, promotions, or lifestyle images.
A web campaign is a container or placeholder for a personalized experience based on a visitor’s behavior, affinities, preferences, location, or other qualifying criteria. Campaigns are organized into experiences or a specific visitor interaction that you curate. You can use experiences to create different personalization results within the same campaign. Basically, use campaign and experience rules to control who sees what campaign on your website.
There are many ways to personalize your site for your visitors. Let’s review a few.
- Homepage Hero Banner. Homepages are often built with a static, one-size-fits-all approach. Instead, create a personalized product-based hero banner that updates based on a visitor’s location. Based on season or weather conditions in the visitor’s location, the hero banner changes to reflect the type of footwear popular in that region. You can also adjust an image based on the industry or possible profession of your website visitors. So for example, show a photo of a person reading a book if the visitor is from a university or a doctor with a stethoscope if the visitor is with a health care practice.
- Blog Recommendations. Blog content recommendations promote brand awareness and build customer loyalty. Create a personalized content zone that promotes articles curated for each visitor. Each visitor sees the content that resonates with them based on their behaviors and preferences.
- Customer Onboarding. Onboarding provides a great opportunity to guide and track a user’s experience and interaction with your content. For example, when a visitor first logs into your application, you can present them with five defined steps to complete. If they only complete three on the first visit, when they return you can remind them to complete steps four and five.
Create a Campaign
Content zones, templates, and campaigns are the central components of web personalization. Once you have the content zones and templates defined for your site, it’s easy to create a personalization campaign that resonates with your users.
To do so, create a new campaign, select a template, complete the inputs available, add criteria rules, and then publish the campaign. The published campaign is visible to visitors that qualify based on the testing and targeting logic you set when you create the campaign.
Want to take your personalization to the next level? Once you’ve created your audiences and campaigns, you can then test, analyze, and improve your customer experiences with AI-driven recommendations using Einstein Recipes. Once integrated with your dataset, Einstein Recipes allows you to provide real-time, personalized recommendations uniquely tailored to each and every visitor based on their individual affinities and intent. You also have the power to decide which pre-built algorithms are included in your recommendations recipe. And you have the flexibility to tailor your recommendations to ensure that they show relevant, personalized items to each of your visitors.
What’s in the Recipe?
There are several components that go into building a recommendation strategy. We discussed some of these terms earlier in this badge, but as a refresher, let’s review what makes up Einstein Recipes. Your catalog is a dynamically populated collection of your products and content as well as related categories and tags, such as brand, keyword, and blog author. You can’t have a recipe without first having a catalog. New recipes include these elements.
- Ingredients. Ingredients are the core algorithms driving an Einstein Recipe. Select from a list of ingredients to return the most effective recommendations for your visitors.
- Exclusions. Exclusions add additional filtering criteria to your recipe. You can specify what items you do or don’t want to show in your recommendation, such as avoiding recommending products that are already in the user’s cart.
- Boosters. Boosters use a visitor's affinity score to match that affinity and boost it in the recommendations query results. In other words, if you boosted the brand affinity in your recipe, visitors who show a preference for a particular brand will be shown items with that brand first.
- Variations. Variations are used to modify the returned results to provide more options to the user. For example, use a variation based on item type to limit the number of hats shown to a visitor. So instead of just seeing hats, they would see only the two most relevant hats and a variety of other items such as gloves.
Build recipes by adding together one or more ingredients with exclusions, boosters, and variations. Create a variety of combinations to serve up the right content or products based on an individual visitor’s behavior and affinities on your site. Remember, if it’s in your catalog, you can recommend it. Once trained and ready, your custom recipe is then queried against the proprietary Einstein Recipe engine and the individual query results are presented to each visitor as personalized recommendations.
Now that you have a basic understanding of Marketing Cloud Personalization, in the next unit we review use cases and how to prepare to implement Marketing Cloud Personalization.