Personalization means adapting content to the visitor according to his or her personal preferences, needs and capabilities. A personalized website is more relevant and engaging than a static non-personalized site. Personalization turns a static pull experience into an interactive push relationship – the visitor indicates their interest and the website pushes matching content. Of all possible content variants that could be offered, personalization helps you serve the content that fits best. Content tailored to the visitor has a better chance of making a sale or persuading the visitor to stay longer. (warning) Magnolia 5.3+ EE Pro

Once you are familiar with Magnolia's personalization mechanism, do a hands-on exercise in My first personalized campaign.

Variants are alternative content

A variant is an alternative content element that replaces the original element in personalized content delivery. Magnolia serves the variant instead of the original element when personalization rules match. A variant is a copy of the original element, edited to best suit the intended audience.

Variants are created in the same app where the original content element was created. For example, page variants are created in the Pages app.

Here are two variants of the Standard Article page. The first variant is targeted to previous buyers, the second to new visitors. The original page carries a special variant icon  which tells you this content has alternatives.

(warning) Variants are displayed under the original page but they are not children. They are copies. Variants don't inherit inheritable components from the original page, for example. Think of variants as "swaps" that take the place of the original page when personalization rules match.

In Magnolia 5.3 variants are limited to pages but in upcoming releases you will be able to create variants of other content elements such as assets. 

Traits describe the visitors

In order to personalize content you need to know something about the visitor. In Magnolia, we call that something a trait. A trait is an attribute of the visitor or visit that you can detect and assign a value to. For example, age is a trait. It tells you if the content is appropriate for the visitor.

Think of traits as characteristics of the visitor's persona:

  • Yên is an 18-year-old Vietnamese speaker.
  • John is a music-loving shopaholic.
  • Carlo is a Boston-based marketing manager whose Honda Accord has 50,000 miles on the odometer.

Here are some traits commonly used for personalization:

  • Age
  • Gender
  • Interests
  • Date of visit
  • Location of visit
  • Language set in browser

Best practice

Every trait has inherent allowed values. For example, locations are countries, ages are numbers, and genders are either male or female. Create traits that have a clear set of allowed values. Traits that have vague values are difficult to detect and assign. Also, make sure the trait applies to the majority of your audience. "New vs. returning visitor" is a good trait because the values are easy to detect and it applies to every visitor.

Default traits

Magnolia provides three traits out of the box:

  • Date: Allows you to serve content based on the current date. For example, run a Valentine's Day campaign the week before February 14.
  • Country: Allows you to target visitors from a particular country. For example, show product prices in pounds to U.K. visitors.
  • Visitor: Allows you to detect new vs. returning visitors. For example, say hello to known visitors.
  • Cookie: Allows you to detect browser cookies. For example, show a weather forecast for an area which the users chose the last visit and which we can send to their browsers.
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You can also create your own traits.

Explicit and implicit personalization

You may have heard about explicit and implicit personalization. In Magnolia you can do both. It depends on the trait.

  • Explicit personalization is based on traits that visitors declare and choose themselves. Explicit traits are typically stored in profile attributes such as age, gender and language. Visitors populate the profile knowingly, usually by filling in a form. You can then connect traits to the profile attributes. In this type of personalization the website pushes content in which the user has an explicit interest.
  • Implicit personalization is based on tracking user behavior as they navigate the site. Implicit traits may also be stored in a user profile but the difference is that these traits are not known to the user. Over time you learn more about the visitor as they click links and view pages. In this type of personalization the website pushes content that matches the visitor's past behavior.

You can mix explicit and implicit traits. For example, you can ask users to declare their gender explicitly but then use implicit behavioral analysis to find out what products they like. Similarly, you can collect a given trait using either method: you could analyze visitor behavior to figure out if they are interested in movies or you could just ask them. The difference between explicit and implicit traits is really just academic.

Rules filter content

Magnolia's personalization is rule based. Rules push relevant content to the front and filter irrelevant content out. Rules can be based on any trait you can reliably detect and analyze, such as profile attributes, preferences, past behavior, search terms, or interests.

To create a rule, define permitted values for a trait. For example, "Age >= 18" is a rule. When a visitor is 20 years old, the rule is met and personalized content is served.

Examples of rules:

  • Age >= 18
  • Gender = female
  • Interests include movies
  • Date of visit = 2/14
  • Location of visit = China
  • Language set in browser = English

Choosing an audience

You can choose the audience for a content variant in two ways:

  • Local rule: The simplest way to choose an audience is to pick a trait and define permitted values for it. This creates a local rule. In the example below, the local rule "Date = 2015-05-10" serves this content on Mother's Day only. Start your personalization experiments with a local rule because it is transparent and easy to understand.
  • Segment: Once you know your audience well divide it into segments. Choose a segment as audience when you want to target the variant to visitors who have responded to personalization well in the past. In the example below, the segment "German visitors" serves this content variant only to visitors from Germany.

You can use segments and local rules at the same time. One of the segments and all the local rules must be true for audience to match. Then the content variant is served.

Think of segments having an OR condition and local rules an AND condition.

audience matches IF 
   date = 2015-10-05 AND
   weather = sunny  AND
   (segment = "German visitors" OR segment = "Returning visitors")


To simplify the process of assigning rules, you can divide the entire visitor population into segments. A segment is all the visitors who meet a given rule. This means that people in the segment have common needs and priorities. It must be large enough to be measurable, stable over time, reachable and responsive. These qualities make the segment a meaningful target audience for repeated campaigns.

Examples of segments and the rules that define them:

  • Chinese moviegoers
    • Age >= 18
    • Interests include movies
    • Location of visit = China
  • Returning marketing managers
    • Job title = "Marketing Manager" OR "CMO"
    • Type of visitor = returning
  • Shutterbugs
    • Interests include photography
    • Has Flickr account = true


Best practice

Create a segment only when you know your audience well and have targeted at least one successful campaign to them using local rules. Visitors who share a combination of traits are good candidates for segmentation if they respond well to personalization. For example, add visitors who previously bought a product to a "Previous buyers" segment and offer them a discount as a reward for return business. Segmentation helps you repeat successful personalization experiments. Start with local rules, move to segmentation later.

All traits of a segment are combined with a logical AND constraint:

segment matches IF 
   trait 1 matches AND 
   trait 2 matches AND
   trait 3 matches AND ...


Persona is a hypothetical visitor who represents the target audience. The persona has the same goals as other visitors in a segment group. Use personas together with segmentation to test content variants. If a variant makes sense for the persona then it is suitable for everybody in the same segment.

Describe the persona in a short paragraph that explains their behavior, needs and goals. Add a few fictional personal details to make the persona a realistic character. A realistic persona belongs to more than one segment at the same time. For example, a persona can be interested in both music and technology at the same time.

Use personas to preview content variants. Personas are helpful because they put a personal human face on otherwise abstract data about visitors. By thinking about the needs of a fictional persona you can better infer what a real person might need. 


Magnolia does not cache personalized pages. If a page has a variant then the page will not be cached. Caching such pages would be problematic because the variant that is served first would be served to all subsequent visitors.

The Personalization module changes cache configuration during installation. It replaces the default cache policy with a new policy and a new cache store. This results in bypassing cache for pages that have variants or are personalized.

  • info.magnolia.personalization.cache.BypassUncacheableEntriesPolicy
  • info.magnolia.personalization.cache.BypassVariantsCacheStore

If this solution does not work for you, customize the cache key to include your traits. Be aware that a customization likely only works if the majority of your pages are personalized and you have a small amount of traits and those traits have a small number of allowed values. The Date trait is already problematic as it allows a large number of values. Even using only 2 traits with 2 allowed values would increase the cache size by a factor of 4. We don't recommend going this route. Magnolia plans to implement a personalization-friendly caching mechanism in the future which allows you to cache pages with variants while maintaining a reasonably sized cache. Trait detectors would be executed before the cache filter.


The Preview app allows you to test personalized content. You can impersonate a visitor to verify that the correct page variant is served. The impersonated visitor can be a persona or a mix of local traits. The Preview app looks just like the preview in the Pages app but it has a sidebar for selecting the persona and traits.

Personalization & integrations

If you want to use a personalization engine of an external system, look how it's done for Customer segments in IBM WebSphere Commerce Integration module or see the example how to retrieve traits from SugarCRM Connector.

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